A Comprehensive Review of Sensor Technologies in IoT: Technical Aspects, Challenges, and Future Directions

Abstract

The rapid advancements in wireless technology and digital electronics have led to the widespread adoption of compact, intelligent devices in various aspects of daily life. These advanced systems possess the capability to sense environmental changes, process data, and communicate seamlessly within interconnected networks. Typically, such devices integrate low-power radio transmitters and multiple smart sensors, hence enabling efficient functionality across wide ranges of applications. Alongside these technological developments, the concept of the IoT has emerged as a transformative paradigm, facilitating the interconnection of uniquely identifiable devices through internet-based networks. This paper aims to provide a comprehensive exploration of sensor technologies, detailing their integral role within IoT frameworks and examining their impact on optimizing efficiency and service delivery in modern wireless communications systems. Also, it presents a thorough review of sensor technologies, current research trends, and the associated challenges in this evolving field, providing a detailed explanation of recent advancements and IoT-integrated sensor systems, with a particular emphasis on the fundamental architecture of sensors and their pivotal role in modern technological applications. It explores the core benefits of sensor technologies and delivers an in-depth classification of their fundamental types. Beyond reviewing existing developments, this study identifies key open research challenges and outlines prospective directions for future exploration, offering valuable insights for both academic researchers and industry professionals. Ultimately, this paper serves as an essential reference for understanding sensor technologies and their potential contributions to IoT-driven solutions. This study offers meaningful contributions to academic and industrial sectors, facilitating advancements in sensor innovation.

1. Introduction

In the modern world, advancements in science and technology have significantly enhanced daily life, enabling seamless interaction with our surroundings. A wide array of electronic devices and household appliances, including computers, photocopiers, mobile phones, microwave ovens, refrigerators, air conditioners, and television remotes, play a vital role in everyday activities [1]. In addition, technologies such as smoke detectors, infrared (IR) thermometers, and automated lighting and fan control systems have become integral to contemporary living. The efficiency and functionality of these devices are largely attributed to sensors, which serve as fundamental components in numerous applications.

A sensor is a specialized device or module designed to detect variations in physical or environmental conditions, such as temperature, pressure, humidity, motion, and force. The sensor also responds to electrical parameters like current and voltage. These sensors convert detected changes into measurable signals, which can then be analyzed and processed for various applications. Whether in industrial automation, healthcare, consumer electronics, or smart home technologies, sensors play a crucial role in optimizing performance, enhancing safety, and improving overall user experience [2].

In recent years, there has been significant progress in sensing technologies, the development of wearable and implantable devices, and advancements in wireless communication systems. The rapid evolution of network technologies, coupled with sophisticated sensors such as laser-based systems, radar sensors, and high-resolution camera devices, has facilitated the creation of smart environments where both homogeneous and heterogeneous devices seamlessly interact. This integration has paved the way for highly interconnected systems that enhance automation and data-driven decision-making capabilities [3].

The Internet of Things (IoT) has played a transformative role in harnessing these advancements by enabling comprehensive monitoring systems that leverage data processing and analytics. Through the utilization of IoT-based networks, real-time information can be efficiently collected, analyzed, and acted upon, improving operational efficiency across various domains. These networks have been successfully deployed in a diverse range of applications, including intelligent energy management [4], smart city infrastructure [5], home automation and monitoring systems [6,7], industrial automation [8], and vehicle monitoring technologies [9].

The fusion of sensor technologies with IoT-driven applications has expedited diagnostic procedures, streamlined treatment methodologies, and facilitated better management of chronic diseases. From a technological standpoint, the IoT incorporates sophisticated wireless body area networks (WBANs), which enable the seamless transmission of sensor-acquired data to centralized cloud platforms. This allows for advanced data analysis, pattern recognition, and predictive modeling, ultimately supporting more informed decision-making in critical applications [10,11,12].

As the convergence of the IoT and sensing technologies continues to evolve, their applications will further expand, driving innovation across multiple sectors and reinforcing the role of intelligent systems in shaping the future of technology and automation.

A sensor is a highly specialized device designed to detect, interpret, and respond to various external stimuli by converting non-electrical signals into measurable electrical outputs [13]. As an essential component of modern technology, sensors are widely integrated into various environments, including residential, commercial, and industrial settings, as well as transportation systems. These devices can be embedded in humans, animals, and objects, allowing for extensive real-time data collection and analysis.

Sensors serve as the cornerstone of numerous technological advancements by facilitating the detection, transmission, and processing of critical information. Their diverse applications encompass motion tracking, image recognition, voice activation, environmental monitoring, physiological signal assessment, and gesture recognition. The continuous evolution of sensor technology has led to the development of smaller, faster, and more cost-effective sensors, enhancing their efficiency and expanding their scope of implementation across multiple industries [14].

Furthermore, sensors represent the fundamental building blocks of wearable devices, serving as the primary interface between the physical world and digital systems [15]. Their ability to capture and process physiological and environmental data has revolutionized fields such as healthcare, automation, and artificial intelligence (AI). Given their increasing significance, a comprehensive examination of sensor classifications, underlying mechanisms, and real-world applications is crucial to understanding their transformative role in modern innovation.

In practice, the integration of sensor technologies with IoT capabilities holds immense potential for large-scale data collection, enhanced automation, and more sophisticated decision-making processes. As these technologies continue to evolve, their influence on industrial innovation, smart infrastructure, and intelligent systems becomes increasingly evident. This paper highlights the significance of sensing technologies by evaluating contemporary developments in their applications, emphasizing their fundamental role in shaping the future.

1.1. Related Works and Paper Contributions

Different studies have investigated the importance of IoT-based sensing technologies. As such, various types of review papers have been studied over the past years, which have provided a review of the types of sensors and their applications in the IoT. Each study was conducted for a specific purpose: to provide a comprehensive view of the current importance of this topic and its impact on practical life, keeping pace with future developments and needs, and to identify the requirements and challenges that arise when applying these technologies in various aspects of life.

Numerous studies have investigated the significance of IoT-based sensing technologies. Several review papers have been published over the years to examine different aspects of this evolving field; see, e.g., [1,16,17,18,19,20]. Many of these reviews focus on specific types of sensors and their applications within IoT systems, each with the aim of providing an understanding of the role of these types.

For example, the study presented in [16] offers an analysis of various sensor types and their applications within the IoT, emphasizing their role in facilitating real-time detection and data retrieval from any location. The research highlights the growing significance of IoT technology in the realm of Plug-and-Play smart devices. The paper further discussed several applications such as housing metering, large-scale wireless sensor networks, high-security systems, critical infrastructure monitoring, cloud computing with data storage and analysis, smart grids, renewable materials, and nano-power units.

Similarly, the paper in [17] provided insights into the challenges and future prospects of sensing materials, addressing the rising demand for efficient and adaptable sensing systems. While the study underscores the essential role of sensors across various industries, it also identifies key challenges, including the complexity of designing sensing materials with optimal response speed, selectivity, sensitivity, durability, and environmentally sustainable properties.

A review of the security architecture of the IoT, along with a taxonomy of the associated challenges based on RFID and wireless sensor networks (WSNs), was presented in [21]. The work in [18] offers a detailed review of electrochemical sensors, highlighting recent advancements and their applications in detecting minute biomolecules, as well as monitoring electrochemically induced organic and inorganic pollutants. Similarly, biosensors and the role of nanostructured materials in enhancing their performance have been examined in [19]. This study explores various applications of biosensors, including drug delivery, healthcare diagnostics, environmental monitoring, and the assessment of food and water quality. Additionally, the paper categorizes these sensors based on their receptor types, transduction mechanisms, and the integration of nanomaterials.

The work in [1] provided a review of sensor technologies and their transformative impact on our lives across various fields. The author explored the capability of sensors to detect environmental changes and capture signals, facilitating diverse applications across various domains, including lifestyle, healthcare, fitness, and manufacturing. In particular, the study emphasized the role of sensors in healthcare, highlighting their potential in drug delivery systems. These sensors can enhance medication adherence by issuing reminders for timely intake and administering precise doses, thereby offering significant benefits to older adults, athletes, and individuals with high-risk medical conditions. Additionally, the review outlined key industrial trends such as ultrasound, radar, and optoelectronic solutions driving sensor innovation, emphasizing their critical role in modern business operations and everyday activities.

David et al. [20] conducted a review on smart health monitoring, focusing on its role in collecting, interpreting, and analyzing data to assess structural integrity and predict lifespan. The study examined failure mechanisms in composite structures and explored advanced non-destructive testing techniques for structural health monitoring. In addition, the paper highlighted the emerging trend of embedding sensors within composite materials, enabling them to function as self-sensing structures capable of detecting and localizing damage in real time. Mahmood et al. [22] reviewed current research and the challenges associated with patient monitoring systems. The study also discussed essential services for monitoring human activities, providing valuable insights for both industry professionals and academic researchers, while identifying promising research directions.

Similarly, the work in [23] conducted a thorough review of the IoT and its implications for the mining industry. The research examined the industry’s capacity to integrate and advance IoT systems, analyzing key challenges and proposing a comprehensive model that considers exploration, operational efficiency, and safety measures. The study also highlighted the role of wireless sensor networks and global data management in optimizing mining operations. Furthermore, the work in [24] focused on optoelectronic humidity sensors, providing an in-depth analysis of various metal oxide nanomaterials used in their development. To this end, the work explored the techniques employed in these sensors, contributing to the advancement of humidity-sensing technologies.

Furthermore, several studies have conducted comprehensive reviews of wearable devices, including [25,26,27,28,29]. While significant advancements have been made in the field of wearable sensors, much of the existing research remains centered on specific sensor types, their inherent limitations, and potential future developments. Considerable progress has been achieved in designing sensor technologies capable of detecting physiological and biochemical markers with increasing accuracy and efficiency.

However, many of these studies adopt a fragmented approach, focusing on individual sensor categories or addressing technical challenges in isolation, rather than offering a thorough perspective on the integration of wearable sensors into broader technological ecosystems. Addressing this gap is essential for advancing the field and realizing the full potential of wearable sensing technologies in various applications. Therefore, this paper distinguishes itself by offering a comprehensive review that not only categorizes various sensor technologies but also examines their convergence with the IoT. We go beyond conventional discussions by exploring the latest advancements in sensing materials, their real-world applications, and the technological challenges shaping their evolution. In doing so, we provide a structured analysis of key innovations, obstacles, and transformative opportunities that have the potential to redefine industries and societal infrastructures.

A key differentiating factor of this work is its focus on the intersection of sensors and the IoT, an area poised to revolutionize data acquisition, automation, and intelligent decision-making at scale. Unlike previous studies that primarily assess sensors in isolation, this review underscores the synergistic potential of integrating sensor systems within interconnected digital frameworks. By bridging the gap between traditional sensor research and the increasing demand for smart, adaptive environments, this study offers valuable insights into the next generation of sensing technologies and their far-reaching implications for the future.

This article presents a comprehensive review of sensor technologies, their classifications, and their pivotal role in the IoT, enabling real-time data acquisition and seamless information retrieval from any location. Furthermore, it explores recent advancements in sensing materials, examining their diverse applications, emerging research trends, and the critical challenges shaping this field. A systematic analysis of key technologies, existing limitations, and prospective opportunities is provided, underscoring the transformative impact of sensors on a wide range of industries and societal advancements. In addition, this review article delves into recent progress, applications, and research trends in sensors while addressing the key challenges and emerging opportunities that position these materials as transformative elements across multiple industries and societal domains. Furthermore, this paper offers valuable insights into how we can leverage these advancements to navigate the rapidly evolving landscape of sensing and automation.

1.2. Paper Organization

The structure of this survey paper is organized as follows. Section 2 presents the fundamental principles related to IoT systems. Section 3 provides a thorough explanation of the sensors. Furthermore, this section also discusses the role of AI in the IoT and sensor-based applications. Section 4 provides a detailed description of sensor classifications. Section 5 provides a description of sensor types and applications. Section 6 highlights the integration of sensor networks and the IoT into industrial applications. Section 7 presents research challenges related to sensor technology and discusses future research directions in sensors based on IoT systems. Finally, we conclude this review paper in Section 8.

2. Overview of the IoT

This section discusses the fundamental principles of the IoT, highlighting its defining characteristics, underlying technologies, and prominent architectural models.

In the 21st century, the IoT has emerged as one of the most influential technological advancements, revolutionizing a wide range of critical fields. Its significance continues to grow, positioning it as a cornerstone of modern digital infrastructure. The IoT has fundamentally transformed data exchange processes, driven by the exponential increase in data transmission rates and storage capacities in recent years [22].

The IoT is an interconnected network that operates via the Internet, integrating physical objects with embedded technologies such as sensors, electronics, and actuators [30]. These components, combined with sophisticated software systems, facilitate the seamless collection, transmission, and exchange of real-time data. Sensors, in particular, serve as the primary interface between the physical and digital worlds, enabling continuous monitoring, automation, and intelligent decision-making. The integration of advanced sensing technologies within IoT networks has enhanced efficiency across multiple domains, reinforcing their role as a pivotal element in modern technological ecosystems.

The IoT represents a groundbreaking technological framework that seamlessly interconnects a wide range of physical devices, enabling real-time data acquisition, processing, and autonomous management [31]. Through the integration of sensors, actuators, and network interfaces, IoT systems can independently collect and transmit data, eliminating the need for human intervention. These vast, interconnected entities generate an immense volume of data, which can be analyzed to extract meaningful insights, optimize operations, and drive intelligent automation. The IoT is fundamentally defined as a network of things, encompassing physical objects embedded with advanced software and integrated technologies. These objects range from everyday consumer electronics such as smartphones, home appliances, and cameras to large-scale systems, including industrial machinery, vehicles, and smart infrastructure [32]. By enabling these devices to communicate and exchange data in real-time, the IoT fosters the development of intelligent and autonomous systems capable of optimizing operations across multiple sectors.

A key advantage of the IoT lies in its ability to support real-time monitoring and remote control, significantly enhancing efficiency, decision-making, and adaptability across diverse industries [21]. As a result, the IoT has established itself as a transformative force, revolutionizing sectors such as healthcare, smart cities, manufacturing, and environmental monitoring. By facilitating seamless data exchange and intelligent automation, the IoT continues to redefine the digital landscape, positioning itself as one of the most influential technological advancements of the modern era [33,34].

The primary function of the IoT is to facilitate seamless communication and efficient data exchange through the Internet, ensuring continuous interaction between interconnected physical entities. The term “Internet” in this context refers to the vast global network that connects billions of computers and electronic devices, allowing users to access information and interact with others from any location at any time through standardized protocols. Meanwhile, the term “things” encompasses any object capable of establishing a connection within this network, making the IoT a versatile framework for achieving ubiquitous connectivity [35].

The ability to establish continuous and reliable communication between a vast network of interconnected devices and the Internet is fundamental to the efficiency of the IoT ecosystem. These devices are constantly engaged in data generation and collection, capturing extensive volumes of information from their operational environments. This real-time data acquisition serves as the foundation for in-depth analysis, intelligent decision-making, and enhanced automation, enabling optimized system performance across various industries.

To efficiently process and interpret such large-scale data, advanced computational models and analytical frameworks are indispensable. These systems not only facilitate the extraction of actionable insights but also enhance predictive capabilities, making IoT-driven operations more responsive and adaptive. Additionally, scalability is still considered a critical consideration in IoT deployment. This consideration ensures that in expanding networks, the IoT devices are able to seamlessly accommodate an increasing number of connected devices. The ability to manage and integrate this dynamically evolving infrastructure is essential for sustaining the IoT’s long-term impact.

Given the interconnected nature of the IoT and the sensitive nature of the data it transmits, it is important to implement robust security measures. Thus, advanced encryption techniques, authentication protocols, and intrusion detection systems are in high demand for protecting the networks against cyber threats, unauthorized access, and data breaches. Strengthening these security mechanisms safeguards data integrity, confidentiality, and overall system resilience, ensuring that the IoT remains both secure and reliable in an increasingly digitized world.

The seamless functionality of the IoT is made possible by a range of critical technologies. Embedded systems, which integrate microcontrollers with sensors, actuators, and networking capabilities, enable the transformation of everyday objects into intelligent, connected devices. Various wireless communication protocols, such as Wi-Fi, Bluetooth, Zigbee, and cellular networks, facilitate reliable and efficient data exchange between IoT-enabled devices [36]. The rapid advancement of wireless communication technologies has played a pivotal role in the evolution of the IoT [37,38,39]. As wireless connectivity continues to progress, its integration into different infrastructure enhances accessibility, reduces costs, and minimizes errors, thereby transforming the efficiency of service delivery.

2.1. Architectural Frameworks of the Internet of Things (IoT)

Several architectural frameworks have been proposed to optimize the structure and functionality of IoT ecosystems, each offering distinct advantages and limitations. Figure 1 provides an illustration of the general layered architecture and fundamental components of IoT systems. These architectures define how data is collected, processed, and transmitted within IoT networks. The most commonly adopted IoT architectures can be categorized as the follows.

• Four-Layer Architecture: This foundational model consists of four primary layers. The perception layer encompasses sensors and actuators, responsible for data acquisition and interaction with the physical environment. The network layer facilitates data transmission through communication protocols and gateways, ensuring seamless connectivity. Data processing layer, which is essential to process the data effectively. Finally, the application layer provides user interfaces and data visualization tools, enabling end-users to interpret and utilize the collected information effectively.
• Five-Layer Architecture: Building upon the four-layer structure, this model introduces two additional layers to enhance functionality. The transport layer ensures efficient data aggregation and routing between network components, while the business layer focuses on data analytics and decision-making, enabling more sophisticated data-driven insights and automation.
• Cloud-Centric Architecture: This approach centralizes data processing and application logic within cloud computing infrastructure. By leveraging the computational power and storage capacity of cloud platforms, this model facilitates large-scale data processing and advanced analytics. However, reliance on cloud-based processing can introduce latency and bandwidth constraints. In addition, leveraging cloud computing within optimizes data management and analytical processes, ensuring seamless access to real-time information. Specifically, the IoT leverages a distributed cloud-based infrastructure to efficiently process, store, and analyze data. Information can be acquired through the communication or gateway layer, or alternatively, sensor data from connected devices, including wearables, can be transmitted directly to the cloud for further processing. Cloud computing provides a scalable, flexible, and accessible platform that facilitates centralized data management and supports advanced analytical capabilities. Typically, IoT devices establish cloud connectivity through WiFi, cellular networks, or other long-range wireless communication protocols. Within the cloud environment, raw sensor data undergoes processing, where sophisticated algorithms are applied to identify patterns, detect anomalies, and extract critical insights relevant to specific applications.
  Cloud-based solutions facilitate secure and synchronized data exchange across multiple platforms, promoting scalability while addressing critical security concerns. Furthermore, integrating cloud computing with advanced algorithms enhances data reliability and ensures that vital information remains readily available when needed, ultimately improving the accuracy and effectiveness of modern communication services. Despite the advantages offered by cloud computing, the widespread expansion of the IoT has introduced significant challenges in centralized data processing. Storing and managing data from a vast network of distributed IoT devices within centralized cloud servers can lead to high latency, reduced throughput, bandwidth limitations, privacy concerns, centralized security vulnerabilities, and increased operational costs, including expenses related to data transmission, energy consumption, storage, and computational resources [40]. Finally, cloud computing provides scalable storage, computational power, and application hosting, supporting the vast volume of data generated within IoT ecosystems.
• Edge-Centric Architecture: In contrast to the cloud-centric model, the edge-centric paradigm prioritizes localized data processing, shifting computational tasks closer to the network’s edge within IoT devices, gateways, or edge servers. This distributed processing approach reduces latency, decreases bandwidth usage, and enhances real-time decision-making, making it particularly beneficial for time-sensitive applications. This computational layer acts as an intermediary that facilitates preliminary data processing and basic analytical tasks before transmitting information to higher-level systems. By leveraging edge computing, networks can be programmed in a decentralized manner, allowing for efficient data processing and management at the periphery of the network rather than relying solely on a centralized cloud infrastructure. This decentralized approach is particularly advantageous for applications that require low-latency responses, such as real-time monitoring systems.
  Fog computing is a key extension of edge computing. Fog computing enhances cloud computing by bringing computational resources closer to the locations where data is generated and collected. With the distribution of processing capabilities (decentralization) throughout the network, fog computing enables data analysis to occur closer to the source. Therefore, latency is minimized, bandwidth consumption is reduced, and overall system performance is optimized. This architectural approach ensures that computational tasks are handled efficiently, improving the responsiveness and reliability of sensor-based applications.

Each of these architectures plays a crucial role in shaping the efficiency, scalability, and responsiveness of IoT ecosystems. In addition, the selection depends on the specific requirements of the intended application.

Furthermore, the exponential growth in data transmission and storage requirements has increased the necessity for efficient big data handling. This reinforces the significance of the IoT in modern information exchange. In addition, the huge amount of data enables objects with unique digital identities to seamlessly integrate into a comprehensive information network; therefore, delivering intelligent and adaptive services [41]. To manage and extract value from the extensive datasets produced by IoT networks, big data analytics techniques can also be employed. These methodologies of big data analytics enable the processing, organization, and interpretation of large-scale data streams, uncovering meaningful patterns and trends.

In addition, AI and machine learning (ML) algorithms play a pivotal role in enhancing IoT functionality. These algorithms enable predictive analytics, automate decision-making, and improve system autonomy, notably in areas that involve predictive maintenance and real-time anomaly detection.

In the IoT, sensor technologies play an indispensable role in capturing real-world data from diverse environmental parameters. This includes temperature, pressure, illumination, motion, and chemical composition. These sensors enable continuous monitoring and facilitate real-time decision-making by collecting precise measurements from their surroundings.

2.2. Applications of the Internet of Things (IoT)

The IoT has revolutionized various industries by enabling seamless connectivity, real-time data exchange, and intelligent automation. As technology advances, IoT applications continue to expand across multiple domains, enhancing efficiency, decision-making, and user experiences. Below are some of the most significant applications of the IoT, categorized based on their impact across different sectors:

• Intelligent Healthcare Systems: The IoT has transformed healthcare through remote patient monitoring, wearable medical devices, and smart hospital management systems [22,42,43]. By integrating IoT-enabled sensors and AI-driven analytics, healthcare providers can track vital signs in real-time, predict potential health risks, and enhance patient care. This technology also facilitates telemedicine, allowing medical professionals to diagnose and treat patients remotely, thus improving healthcare accessibility and reducing hospital overcrowding.
• Autonomous Smart Vehicles: Driving assist systems are a significant component of a self-driving autonomous vehicle to overcome non-autonomous driving problems [44]. The IoT plays a crucial role in the evolution of autonomous vehicles, enabling real-time data exchange between vehicles, infrastructure, and traffic management systems [45]. Advanced driver-assistance systems (ADAS), vehicle-to-everything (V2X) communication, and predictive maintenance enhance road safety and efficiency. By leveraging IoT sensors and AI algorithms, self-driving cars can optimize navigation, reduce congestion, and minimize accidents.
• Smart Manufacturing: IoT-driven smart manufacturing, also known as Industry 4.0, enhances productivity through automation, predictive maintenance, and real-time monitoring of industrial equipment [46,47]. Connected sensors provide valuable insights into production lines, detecting inefficiencies and preventing equipment failures before they occur. This leads to improved operational efficiency, reduced downtime, and optimized resource utilization in manufacturing plants.
• Smart Agriculture: The IoT in agriculture enables precision farming by providing real-time data on soil moisture, weather conditions, and crop health [48]. Smart irrigation systems, drone-based monitoring, and automated farming machinery help optimize resource usage, improve yield, and reduce environmental impact [49]. By integrating the IoT with AI-driven analytics, farmers can make data-driven decisions to enhance productivity and sustainability.
• Smart Cities and Smart Management: The IoT is a cornerstone of smart city development, facilitating intelligent transportation, energy-efficient buildings, and enhanced public safety [50]. Smart traffic management systems optimize urban mobility, while connected environmental sensors monitor air quality and waste management. These innovations contribute to sustainable urban development by improving energy efficiency, reducing congestion, and enhancing the quality of life for residents.
• Wearable Smart Technology: Wearable IoT devices, such as smartwatches and fitness trackers, provide real-time health monitoring, activity tracking, and biometric authentication [51,52]. These devices use embedded sensors to collect physiological data, which can be analyzed to detect health anomalies, improve fitness regimes, and enable seamless interactions with other smart devices.
• Smart Energy Management: The IoT enhances energy efficiency by enabling smart grids, automated demand-response systems, and predictive energy consumption analysis [53,54]. Smart meters and IoT-enabled home automation systems help optimize electricity usage by dynamically adjusting energy consumption based on real-time demand. This reduces costs, minimizes energy waste, and supports renewable energy integration.
• Intelligent Transportation Systems: The IoT has significantly improved transportation systems by integrating real-time traffic monitoring, fleet management, and smart public transit solutions [55]. GPS-enabled tracking, AI-based predictive analytics, and IoT sensors facilitate smoother logistics, reduce congestion, and improve public transportation efficiency. These advancements contribute to smarter and more sustainable urban mobility.
• IoT-Enabled Smart Tourism and Hospitality: The tourism and hospitality industries have embraced the IoT to enhance customer experiences through smart hotel rooms, automated check-ins, and personalized services [56]. IoT-enabled devices provide seamless connectivity, optimize hotel energy management, and offer real-time recommendations based on user preferences. This innovation enhances convenience and operational efficiency while improving customer satisfaction.
• Retail IoT: Retailers leverage the IoT for inventory management, personalized shopping experiences, and supply chain optimization [57]. Smart shelves, RFID-based tracking, and AI-powered customer insights allow retailers to streamline operations and enhance customer engagement. By utilizing IoT-driven automation, businesses can optimize stock levels, reduce operational costs, and improve overall shopping experiences.

Figure 2 depicts the common IoT applications.

3. Review of Sensor Technology

Sensors play a fundamental role in numerous technological applications, serving as critical interfaces between the physical world and electronic systems. A comprehensive understanding of sensor technology requires familiarity with their underlying principles, core performance characteristics, and the most prevalent types of sensors. Such knowledge is essential for selecting the appropriate sensor for specific applications, particularly in the rapidly evolving field of the IoT, where precision and reliability are paramount.

Fundamentally, sensors are designed to detect and measure physical variables or environmental changes, subsequently converting these measurements into signals that can be processed and analyzed by electronic systems. These measurable parameters include temperature, pressure, light intensity, motion, and chemical composition. While the signals generated by sensors are typically electrical, they can also take other forms, such as visual, mechanical, or pneumatic outputs, depending on the application.

The significance of sensors extends across a wide range of industries, enabling critical functions such as data acquisition, process automation, system monitoring, and environmental assessment. They are indispensable in fields as diverse as industrial manufacturing, aerospace engineering, environmental sciences, and healthcare, where they facilitate precision control, enhance operational efficiency, and support advanced technological innovations.

3.1. Fundamental Principles of Sensor Operation

The fundamental operation of a sensor is based on the interaction between the physical quantity being measured and the sensor element itself. This interaction leads to a change in a specific property of the sensor, which is subsequently converted into a measurable signal. In the context of the IoT, sensors generate vast amounts of data, which are transmitted to the cloud for storage and processing. However, this substantial data transmission places significant strain on network bandwidth, necessitating efficient data management strategies to optimize performance and reduce latency [58].

Several key principles underpin sensor design, each leveraging distinct physical phenomena to achieve accurate measurement and signal conversion. The piezoelectric effect, for example, relies on the generation of an electrical charge in specific materials when subjected to mechanical stress. This principle serves as the basis for piezoelectric pressure sensors, accelerometers, and microphones. Similarly, capacitive sensing detects variations in capacitance that result from changes in environmental factors such as distance or dielectric properties. This method is widely used in touchscreens, proximity sensors, and humidity sensors.

Electromagnetic induction is another essential concept. In this concept, the fluctuations in a magnetic field induce voltage in a coil. This mechanism is fundamental to the operation of inductive proximity sensors and magnetic field sensors. On the other hand, optical sensing relies on the interaction of light with matter to measure parameters such as light intensity, wavelength, and propagation changes. Devices such as photodiodes, phototransistors, and optical encoders utilize this principle to achieve precise optical measurements.

Thermal sensing mechanisms are also widely used, with temperature-dependent electrical properties forming the basis of thermocouples, thermistors, and resistance temperature detectors (RTDs). These sensors leverage temperature-induced variations in electrical resistance to provide accurate thermal readings. In addition, chemical sensors function by detecting changes in electrical or optical properties resulting from chemical reactions, enabling the identification and quantification of specific compounds in environmental and industrial applications.

Integrating the aforementioned sensing technologies, modern IoT systems are able to achieve highly accurate data acquisition across a wide range of applications. This will enhance the efficiency, automation, and capabilities of real-time monitoring.

3.2. Embedded Sensors in IoT Applications

The rapid expansion of networked devices, which serve as the backbone of the IoT, necessitates the seamless acquisition of real-world data. This process uses embedded sensors, which are physically integrated into devices to facilitate precise measurements. These sensors convert various physical phenomena into digital signals, allowing for subsequent processing, analysis, and application. This data-driven approach underpins automation, remote monitoring, and data-informed decision-making across a broad spectrum of industries.

Given the diverse applications of the IoT, an equally extensive range of embedded sensors is required to meet specific functional needs. For instance, environmental sensors play a pivotal role in smart home systems, environmental monitoring, and precision agriculture by evaluating key parameters such as air quality, light intensity, atmospheric pressure, temperature, and humidity. Similarly, motion sensors, including magnetometers, gyroscopes, and accelerometers, enable the detection of movement, orientation, and acceleration, making them indispensable in wearables, activity trackers, and security solutions [59]. Furthermore, video and image sensors, such as high-resolution cameras, are instrumental in applications like surveillance, autonomous navigation, and advanced object recognition [60]. On the other hand, chemical sensors are designed to detect specific gases or chemical concentrations. This is essential in the monitoring of environmental pollution, safety in industrial environments, and medical diagnostics. Finally, biosensors, which are critical in healthcare, measure biological parameters such as heart rate, blood pressure, and glucose levels, fitness tracking, and personalized medicine.

The integration of these advanced sensor technologies within IoT ecosystems helps to drive innovation, enhancing the efficiency, accuracy, and adaptability of modern digital systems.

For meaningful analysis and informed decision-making, reliability and precision are considered fundamental–just as the accuracy of sensor-generated data is essential. To ensure dependable insights, consistency in measurement is considered essential across various domains, ranging from industrial automation to healthcare monitoring. Additionally, to maintain functionality over extended periods, durability is considered a key consideration because the sensors must withstand harsh environmental conditions and mechanical stress. Given the large-scale deployment of IoT networks, affordability significantly influences the feasibility of widespread adoption. Overall, to ensure the successful integration of embedded sensors into modern IoT infrastructures, balancing performance, reliability, and economic viability is essential.

3.3. Role of AI in IoT and Sensor Applications

The convergence of AI and the IoT at the sensor level represents a significant advancement in modern technological systems. When embedding AI capabilities within IoT sensors, it becomes possible to enhance data processing, optimize performance, and increase system autonomy. This integration enables IoT devices to analyze data in real-time, extract meaningful insights, and autonomously make intelligent decisions. In other words, the need for constant human intervention will be reduced. The following discussion explores the benefits, challenges, and practical applications of this transformative synergy.

Traditional IoT architectures typically rely on centralized cloud computing resources for data processing and analysis. Despite its effectiveness, this approach has several challenges: increases in latency, limitations in bandwidth, and concerns about data privacy. In addition, as the volume of data generated by IoT sensors continues to expand, transmitting large amounts of information to centralized cloud servers for processing creates network congestion and increases latency. Moreover, concerns regarding data privacy and security have intensified, as sensitive information must be transferred and stored in remote servers.

To address these challenges, the integration of AI at the edge has emerged as an essential paradigm shift in the evolution of the IoT. By directly embedding AI-driven analytics within sensor networks or edge computing devices, organizations are able to process data locally, and this will reduce reliance on cloud infrastructure. This approach significantly minimizes bandwidth usage, mitigates security vulnerabilities, and enhances energy efficiency because of reduced need for continuous data transmission. Furthermore, edge intelligence enables real-time decision-making, making it valuable in time-sensitive applications such as industrial automation, healthcare monitoring, and autonomous systems.

The fusion of AI with IoT-driven sensor networks has the potential to revolutionize various industries, enhancing system resilience, responsiveness, and efficiency. As this convergence continues to evolve, addressing technical challenges related to computational power, data privacy, and network security will be critical to realizing the full potential of AI-enabled IoT solutions.

3.3.1. Benefits of Embedding AI in IoT and Sensors

The integration of AI with IoT sensor applications offers numerous significant advantages in high-tech fields such as robotics, autonomous vehicles, and industrial automation. One of the primary benefits is on-device data processing, which enables real-time decision-making while significantly reducing reliance on cloud-based transmission. This reduction in cloud dependency minimizes latency and therefore enables systems to respond instantly to dynamic environmental conditions–a critical factor in applications where speed and precision are paramount [61].

Another key advantage is bandwidth optimization. Processing data locally within IoT devices or edge computing units substantially decreases the data volume that needs to be transmitted over networks. This is beneficial in environments with limited connectivity, which require bandwidth conservation for maintaining seamless operations. In addition, data privacy and security are enhanced through localized processing; in this case, sensitive information remains within the device rather than being transmitted and stored in cloud servers; therefore, the risk of security breaches is reduced.

Energy efficiency is considered essential in IoT systems. This appears in scenarios where power resources are constrained. In the minimization of continuous data transmission, AI-driven IoT devices consume less power, which in turn extends battery life and ensures sustained operation in energy-limited environments. This capability is particularly considered advantageous in remote or industrial settings because regular replacement or recharging of batteries is not feasible or applicable.

Furthermore, AI-enabled IoT devices enhance system resilience and autonomy. These devices is able to continue functioning and making intelligent decisions even when network connectivity is temporarily unavailable. This ensures reliable performance in mission-critical applications such as disaster response, medical monitoring, and unmanned exploration.

Finally, AI algorithms play a pivotal role in data refinement and optimization. AI enhances the quality of information used for analysis and machine learning model training, which is performed by Noise removal, anomaly detection, and preprocessing sensor data. This results in more accurate predictions, improved operational efficiency, and greater adaptability across diverse IoT applications. Thus, integration of AI with the IoT represents a transformative shift in modern technology, unlocking new possibilities for efficiency, security, and autonomous decision-making across a wide range of industries.

3.3.2. Applications of AI in IoT and Sensors

The integration of AI with IoT sensor systems advances at an unprecedented pace, fundamentally reshaping various industries [62,63]. At the forefront of this transformation is the application of AI-driven predictive maintenance in industrial and manufacturing settings. By analyzing real-time sensor data collected from machinery, AI algorithms can identify potential faults before they escalate into critical failures, enabling predictive maintenance strategies that minimize downtime, optimize operational efficiency, and extend equipment lifespan [64].

In the field of precision agriculture, AI-powered sensors are considered essential for enhancing productivity and sustainability. This is performed by monitoring essential environmental parameters such as soil moisture, temperature, humidity, and nutrient levels. These intelligent systems are able to assess soil conditions, detect pest infestations, and provide real-time insights to optimize irrigation, fertilization, and crop management.

AI-integrated sensor networks are considered as a part of advanced ecological monitoring systems. These systems enable proactive ecological management, which is performed by continuously collecting and analyzing environmental data, such as air and water quality metrics. This allows early detection of pollution, real-time assessment of environmental risks, and informed decision-making–to reduce ecological damage.

AI-enabled wearable sensors have revolutionized patient monitoring by tracking vital signs and biometric parameters in real time. These devices facilitate early disease detection, chronic disease management, and personalized healthcare interventions [65]. They are considered valuable in remote patient monitoring, where real-time alerts can prompt timely medical intervention and improve patient outcomes [66].

The integration of AI within the IoT is further transforming urban infrastructure. Smart city systems leverage AI-driven sensors to monitor and analyze various environmental and operational parameters in real-time. Examples are traffic flow, energy consumption, and public safety conditions. This data-driven approach enhances urban planning, improves traffic management, reduces energy waste, and promotes overall public safety.

Moreover, the adoption of edge computing in AI-enabled IoT ecosystems addresses challenges related to latency, bandwidth consumption, and data privacy. This is performed through processing information closer to the data source, rather than relying solely on centralized cloud computing. This approach significantly reduces response time and bandwidth usage. The benefits of edge intelligence are particularly evident in autonomous navigation and industrial automation, where real-time decision-making is critical. It is also vital in healthcare applications, where AI-powered wearables continuously monitor vital signs and detect anomalies indicative of potential health risks [67].

In summary, the fusion of AI with IoT sensor networks has immense potential to revolutionize various industries. These industries range from manufacturing and healthcare to environmental monitoring and smart cities. The ability to process data at the edge not only ensures efficiency and security but also guarantees a new level of autonomy and adaptability in complex environments.

4. Comprehensive Sensor Classifications

This section discusses the application of sensors and their classifications.

Fundamentally, a sensor is a device that detects external stimuli and generates a corresponding response. It functions by converting a non-electrical input into an electrical signal, enabling further processing and interpretation [13]. Sensors are versatile computing components that can be deployed on humans, animals, and objects, making them indispensable across diverse environments, including residential spaces, workplaces, commercial establishments, and vehicles. They play a critical role in modern technological ecosystems by facilitating essential functions such as motion detection, image acquisition, voice recognition, environmental monitoring, physiological data collection, and gesture recognition. In addition, sensors enable real-time data transmission and processing, contributing to advancements in automation, smart systems, and human-computer interaction.

The advent of the thermostat in 1883 is widely recognized as a pivotal moment in the development of modern sensor technology. Since then, sensors have undergone a profound transformation, evolving from simple mechanical and electrical devices into sophisticated, multi-functional systems. Early sensors primarily measured specific physical quantities, producing mechanical, electrical, or optical signals in response to environmental stimuli. However, over the past decade, advancements in computational power, ubiquitous communication networks, internet connectivity, mobile smart devices, and cloud integration have revolutionized sensor capabilities. These technological advancements have enabled a broad spectrum of applications, ranging from healthcare and wellness monitoring to environmental and industrial sensing [68]. The evolution of sensors into interconnected and adaptive systems has not only enhanced their integration into sensing networks but has also enabled them to function within complex, distributed environments, where they collaborate to support diverse collective sensing applications. Modern network technologies allow sensors to be seamlessly embedded into smart infrastructures, where they can perform multiple functions simultaneously [69].

From a functional perspective, sensors operate by detecting minute changes in input stimuli and generating corresponding variations in their electrical output. A crucial distinction must be made between sensors and transducers. While a sensor specifically converts a physical stimulus into an electrical signal, a transducer is a broader device capable of converting one form of energy into another. For example, a loudspeaker serves as a transducer by transforming electrical signals into acoustic waves. In certain cases, transducers can function as actuators, which perform the inverse role of sensors by converting electrical energy into non-electrical forms of energy. A common example is an electric motor, which transforms electrical energy into mechanical motion [70].

Sensors can be categorized based on various criteria, including their intended applications, functional characteristics, material composition, and performance parameters such as accuracy, range, and cost. Furthermore, the data generated by sensors is represented in multiple formats, including numerical values, categorical classifications, graphical representations, and video outputs. This diversity in data formats leads to different approaches in processing and interpretation. In certain applications, such as monitoring and surveillance, two distinct methodologies emerge: the vision-based approach and the sensor-based approach. These approaches are determined by the sensor type as well as the nature of the data being collected, as discussed in [69].

Sensors play a fundamental role in numerous applications, serving as essential devices. The sensors translate real-world phenomena into electrical signals for analysis and processing. The sensor’s ability to detect, measure, and respond to a broad spectrum of stimuli has made them a critical area of research, with numerous studies dedicated to advancing sensor technology. Given their widespread use and diverse functionalities, it is essential to establish a structured classification system that enables a comprehensive understanding of different sensor types and facilitates meaningful comparisons between them.

A widely accepted method of categorizing sensors divides them into three primary groups: physical, chemical, and biological sensors [71]. This classification scheme offers a structured yet adaptable framework that accommodates a broad range of sensor technologies. Developed within the research system of the Hitachi Re-communications Laboratory. This scheme provides a balanced level of complexity, making it applicable to both industrial applications and academic research.

A sensor must possess specific essential properties to achieve high performance [71]. Key attributes include sensitivity, accuracy, reliability, and efficiency, all of which contribute to the sensor’s ability to provide precise and consistent measurements [19,72,73,74]. As advancements in sensing technologies continue to evolve, optimizing these properties remains a focal point for researchers striving to enhance sensor performance across various domains.

Sensor materials have gained increasing significance across various industries, driving advancements in modern sensing technologies. These specialized materials are designed to convert physical, chemical, or biological stimuli into measurable signals, making them essential for data acquisition, system monitoring, and informed decision-making. Given the growing need for efficient and adaptable sensor systems, it is crucial to explore both the challenges and opportunities associated with sensor materials. The sensing capabilities of a material determine its ability to identify and quantify changes in its environment. These properties manifest through different mechanisms, including optical, chemical, piezoelectric, capacitive, and resistive interactions. While sensor materials are widely applied in fields such as transportation, healthcare, agriculture, industrial automation, and environmental monitoring, ongoing challenges remain in improving response time, selectivity, and sensitivity.

However, the future of sensor materials appears promising, with ongoing research and technological advancements expected to enhance their efficiency, durability, and energy performance. The development of nanomaterials and cutting-edge fabrication techniques is paving the way for highly selective and sensitive sensors with expanded functionalities. Such innovations play a pivotal role in the evolution of the IoT.

Sensors have a broad classification based on different categories, as detailed in [71], depending on the physical quantity or analyte to be measured; they include power source, physical contact, comparability, digital and analog sensors, and detection of signal.

Sensors are advanced technological components designed to detect, quantify, and transform physical, chemical, or biological properties into interpretable signals for further processing and analysis. These devices play a critical role in numerous applications, ranging from industrial automation and environmental monitoring to healthcare and smart infrastructure.

The classification of sensors is based on several fundamental criteria, including their functionality, operating principles, and output characteristics. This systematic categorization ensures optimal selection and integration, allowing for precise measurements and improved system performance across diverse fields. As sensor technology continues to evolve, advancements in materials science, signal processing, and AI will further enhance their efficiency, accuracy, and applicability in next-generation innovations. Figure 3 shows the general classification of sensors that is considered in this paper.

4.1. Classificationof Sensors Based on Measured Parameters

As mentioned earlier, sensors are precision-engineered devices designed to detect, measure, and analyze a wide range of physical, chemical, and biological parameters [19]. In particular, the ability to convert real-world variables into quantifiable data makes sensors indispensable in numerous scientific, industrial, and technological fields. One of the primary methods of categorizing sensors is based on the specific parameter they are designed to measure. This classification facilitates the optimal selection of sensors for diverse applications, ensuring accuracy, efficiency, and reliability in data collection and processing. Figure 4 presents a visual representation of the most critical sensor applications across various domains, demonstrating their widespread impact on modern industries and research fields.

• Mechanical Sensors: These sensors detect mechanical properties such as position, velocity, acceleration, force, pressure, stress, and strain. They are integral to engineering, structural health monitoring, and industrial automation, where precise mechanical measurements are essential.
• Thermal Sensors: Designed to monitor temperature fluctuations, thermal conductivity, and specific heat, these sensors are critical in climate control systems, industrial manufacturing processes, and medical diagnostics, where temperature regulation is a key factor [75].
• Electrical Sensors: These sensors measure electrical characteristics such as charge, voltage, current, electric field strength, conductivity, and permittivity. They play a fundamental role in power management systems, electronic devices, and circuit protection.
• Magnetic Sensors: Responding to variations in magnetic fields, flux density, and permeability, these sensors are widely employed in navigation technologies, automotive applications (e.g., anti-lock braking systems), and industrial machinery monitoring [19].
• Optical Sensors: These sensors detect and analyze variations in light intensity, wavelength, polarization, and phase. They are extensively used in imaging systems, fiber-optic communication, security surveillance, and precision instrumentation.
• Radiation Sensors: Measuring different levels and intensities of radiation, these sensors are essential in nuclear energy applications, medical imaging (e.g., X-ray and CT scanners), and environmental radiation monitoring to ensure safety and compliance [76].
• Chemical Sensors: Designed to assess substance composition, concentration, and identity, these sensors are widely applied in environmental monitoring, industrial safety systems, and healthcare diagnostics to detect toxic gases, pollutants, or specific chemical compounds.
• Biological Sensors (Biosensors): These sensors detect biochemical reactions and biological markers, making them indispensable in medical diagnostics (e.g., glucose monitoring for diabetes), food safety assessment, and biotechnology research [19].

4.2. Classification by Energy and Power Source

Sensors can also be categorized based on their dependence on external power sources for operation. This classification is crucial in determining their suitability for specific applications, particularly in environments where energy efficiency and self-sustainability are critical factors.

• Active Sensors: These sensors require an external power source to function, as they generate their output signals through external excitation. Examples include thermistors, which detect temperature variations by altering electrical resistance; strain gauges, which measure mechanical strain through changes in electrical conductivity; capacitive sensors, which assess variations in capacitance to detect proximity or pressure; and inductive sensors, which rely on electromagnetic induction to identify metallic objects or measure displacement.
• Passive Sensors: Unlike active sensors, passive sensors operate without the need for an external power source. These sensors derive the energy directly from the measured physical phenomenon, and they include thermocouples, piezoelectric sensors, and photodiodes. In thermocouples, voltage is generated in response to temperature differences between two dissimilar metals. In piezoelectric sensors, an electrical charge is produced when subjected to mechanical stress, while in photodiodes, light is converted into electrical current, making them essential in optical detection systems.

4.3. Classification by Signal Conversion Mechanism

Sensors utilize a range of mechanisms to transform detected stimuli into quantifiable signals. This ensures accurate measurement and analysis across diverse applications. The process of conversion depends on the nature of the parameter being measured, with biological, chemical, and physical sensors employing distinct methodologies [71].

• Biological Sensors: These sensors function by detecting biochemical reactions and physiological changes [77]. Their signal conversion mechanisms include biochemical transformations, where enzymatic or antigen-antibody interactions produce measurable outputs; physical transduction, which converts biological activity into electrical or mechanical signals; and spectroscopic techniques, which analyze molecular interactions through optical absorption or fluorescence to determine the presence of specific biomolecules.
• Chemical Sensors: Designed to identify and quantify chemical substances, these sensors rely on chemical transformations, where target compounds undergo specific reactions leading to detectable changes; electrochemical reactions, which generate electrical signals based on ionic movement or redox processes, as seen in pH meters and gas sensors; and spectroscopic methods, such as infrared or Raman spectroscopy, which analyze molecular vibrations to determine substance composition [78].
• Physical Sensors: These sensors convert physical variations into electrical signals through fundamental physical principles [79]. Key conversion mechanisms include electrothermal effects, where temperature variations induce changes in electrical resistance or voltage (e.g., thermistors and resistance temperature detectors); thermoelectric principles, where the Seebeck effect generates a voltage proportional to temperature differences (e.g., thermocouples); and photoelectric conversion, where incident light generates an electrical response, as observed in photodiodes and photovoltaic cells.

4.4. Classification by Contact Type

Sensors can also be classified based on their method of interaction with the object being measured, distinguishing between contact and non-contact types. Contact sensors necessitate direct physical contact with the target to detect variations in properties such as temperature, pressure, or force. Examples include thermistors, which measure temperature through direct thermal conduction, and strain gauges, which assess mechanical deformation by detecting changes in resistance. In contrast, non-contact sensors operate remotely, capturing data without direct interaction with the subject. These sensors, such as piezoelectric sensors and photodiodes, are extensively utilized in automation, security systems, and industrial monitoring due to their ability to provide accurate, real-time measurements while minimizing wear and potential contamination [19].

4.5. Classification by Output Signal Type

Sensors can also be categorized based on the nature of their data transmission, distinguishing between analog and digital types. Analog sensors produce continuous signals that vary proportionally to the measured physical quantity, allowing for precise real-time monitoring. Examples include thermocouples, which detect temperature fluctuations, and strain gauges, which measure mechanical deformation. In contrast, digital sensors convert physical measurements into discrete numerical values, facilitating seamless integration with digital processing systems. These sensors, such as digital satellite service sensors, are widely employed in modern technology due to their accuracy, noise resistance, and compatibility with microcontrollers and computing devices [70].

4.6. Classification by Sensor Material

The composition of a sensor plays a crucial role in defining its functionality, sensitivity, and application across various industries. Sensors are fabricated using diverse materials, each selected based on its specific physical, chemical, or biological properties. Common sensor materials are provided in Figure 5, which are discussed briefly in the following [19,80].

• Biological: Used in biosensors for medical diagnostics, enabling the detection of biochemical markers.
• Electrical: Conductive polymers play a crucial role in electronic sensing applications.
• Magnetic: Ferromagnetic substances are essential for detecting and measuring magnetic fields.
• Mechanical: Stress-sensitive alloys are used to monitor force, pressure, and strain.
• Optical: Semiconductors, such as those in photodiodes, enable imaging and light detection.
• Chemical: Electrochemical coatings facilitate the identification of specific substances in environmental and industrial applications.
• Thermal: Thermoelectric compounds help measure temperature variations.
  Radiation-Sensitive: Employed in sensors designed to detect and quantify various forms of radiation.

4.7. Classification by Measurement Type

Sensors can also be classified based on whether they measure absolute values or relative variations in a given parameter. Absolute sensors deliver direct, independent measurements without requiring a reference point; examples include thermistors, which monitor temperature, and strain gauges, which detect mechanical deformation. In contrast, relative sensors determine variations concerning a predefined reference, enabling comparative assessments. Notable examples include thermocouples, which measure temperature differences rather than absolute values, and pressure sensors, which evaluate pressure variations relative to atmospheric or vacuum conditions. This distinction is essential in selecting the appropriate sensor for specific applications; this will ensure accuracy and reliability across diverse industrial and scientific fields [68].

4.8. Classification by Industrial Applications

Sensors are fundamental to modern technological advancements. Sensors serve as critical components in numerous industries by enhancing efficiency, safety, and automation. These intelligent devices facilitate real-time data acquisition, enabling precise monitoring, control, and optimization of various processes. For example, in agriculture, sensors contribute to precision farming, which is performed by analyzing soil moisture and environmental conditions to improve crop yields and conserve resources. The automotive sector relies on sensors for adaptive cruise control, collision avoidance, and emission regulation. This reliance ensures vehicle safety and performance. In consumer electronics, sensors enable features such as motion detection, biometric authentication, and augmented reality applications. Industrial automation benefits from proximity, temperature, and pressure sensors, which enhance manufacturing precision, reduce downtime, and improve energy efficiency. In healthcare, biosensors revolutionize patient monitoring by detecting vital signs and early disease markers, advancing personalized medicine and remote diagnostics. Defense and security systems integrate infrared and motion sensors for surveillance, threat detection, and target tracking, ensuring national security. Manufacturing industries employ sophisticated sensors to uphold quality control, streamline operations, and prevent equipment failures. As sensor technology continues to evolve, its role in driving innovation and transforming industries will only expand. Figure 6 shows the sensors classification by industrial applications [81].

4.9. Classification by Key Performance Specifications

An ideal sensor exhibits several fundamental characteristics that determine its performance, accuracy, and reliability. The most critical of these attributes are illustrated in Figure 7 [19,72,74]. Overall, sensors serve as the foundation of modern technological innovation, playing a crucial role in automation, safety enhancement, and the advancement of various industries. Their classification is essential for optimizing performance across diverse applications and is typically based on key factors such as measurement parameters, power source, signal processing methods, material composition, and intended application. This systematic categorization facilitates the precise selection of sensors for specific industrial, scientific, and commercial uses.

As sensor technology continues to advance, these classifications will remain pivotal. This helps guide researchers, engineers, and industry professionals in making informed decisions about sensor selection and integration. The continuous evolution of sensor capabilities, driven by innovations in materials science, AI, and miniaturization, will further expand their applicability across domains. Examples of domains are healthcare, environmental monitoring, industrial automation, and smart infrastructure.

5. Sensors Types and Applications

Sensors play an important role in a wide range of applications, with their classification often based on specific physical properties. The physical properties include pressure, temperature, heat flow, and electrical resistance. These fundamental characteristics determine the suitability of a sensor for various technological and industrial applications. The following sections provide an in-depth discussion of the most widely used sensor types in contemporary systems, highlighting their principles, functions, and areas of implementation. Figure 8 depicts the general types of sensors that are discussed in this section.

5.1. Acceleration Sensor

An acceleration sensor, commonly referred to as an accelerometer, is designed to measure the rate of change of velocity of an object [82]. This sensor operates by detecting both linear acceleration resulting from motion and gravitational acceleration, which enables the determination of an object’s orientation. Accelerometers are classified as microelectromechanical systems (MEMS), leveraging silicon-based integrated circuits to convert mechanical movement into electrical signals. These sensors utilize various transduction mechanisms, including piezoelectric, piezoresistive, and capacitive methods. Depending on the number of axes they monitor, accelerometers are available in single-axis, dual-axis, and three-axis configurations [82].

Accelerometers are fundamental to the IoT, supporting a wide range of applications across industries, including consumer electronics, automotive safety, and healthcare monitoring [83]. In sports technology, for instance, these sensors are embedded in smart helmets to assess impact forces in real time, thereby enhancing player safety and reducing the risk of traumatic brain injuries [84]. The continuous evolution of microelectromechanical system (MEMS) technology has facilitated the development of highly miniaturized accelerometers with improved energy efficiency, making them increasingly suitable for wearable and embedded systems [83]. Furthermore, the advent of digital accelerometers has addressed traditional challenges related to noise and measurement inaccuracies by integrating advanced computational techniques, such as mathematical regularization, to enhance precision and reliability [85].

In industrial environments, MEMS-based accelerometers are embedded within mechanical systems to enable real-time condition monitoring. These sensors play a critical role in performance assessment and predictive maintenance, helping to optimize operational efficiency and reduce equipment failure [86]. In the agricultural sector, accelerometers are instrumental in livestock monitoring, where they contribute to behavior recognition systems capable of classifying animal activities with high precision. Such applications enhance livestock management strategies, improving overall productivity and welfare [87].

Beyond industrial and agricultural applications, accelerometers are increasingly utilized in wearable IoT devices for security purposes. Specifically, gait-based biometric cryptography leverages acceleration data to generate robust cryptographic keys, facilitating secure communication in digital environments [88]. The integration of accelerometers into these diverse fields highlights their indispensable role in advancing IoT-driven innovations.

Recent technological advancements, such as the development of 3D-printed capacitive accelerometers, have further expanded manufacturing capabilities. By utilizing both conductive and insulating materials, these innovations enable the fabrication of complex geometries while improving sensor performance and adaptability to specialized applications [89].

Acceleration sensors can be categorized based on their operational principles and specific applications. The primary classifications include MEMS accelerometers, piezoelectric accelerometers, capacitive accelerometers, piezoresistive accelerometers, optical accelerometers, laser accelerometers, servo accelerometers, and frequency-change accelerometers. Each type possesses unique characteristics, making them suitable for various domains, from precision engineering to biomedical monitoring and advanced aerospace applications.

5.2. Proximity Sensors

Proximity sensors are designed to detect the presence or absence of nearby objects without requiring direct physical contact. These sensors operate by emitting an electromagnetic field or a beam of electromagnetic radiation, such as infrared waves, and subsequently detecting changes in the reflected signal. Recent technological developments have introduced fringing-effect-based capacitive proximity sensors (FE-CPS), which are recognized for their cost-effectiveness, low power consumption, and adaptable design. These attributes make them highly suitable for intelligent control systems and material detection applications [90]. Furthermore, the emergence of flexible proximity sensors has enabled advanced functionalities, including obstacle avoidance and human-computer interaction, with ongoing research focused on optimizing their performance in wearable devices [91].

In the field of robotics, proximity sensors play a vital role in enhancing safety and operational efficiency, particularly in human-robot interactions. These sensors facilitate essential functions such as object detection, grasping, and collision avoidance, ensuring seamless robotic operation. Similarly, in agricultural applications, proximity sensors have demonstrated exceptional accuracy in determining maternal lineage in sheep, offering valuable insights for livestock management and genetic improvement [92]. The widespread integration of proximity sensors across diverse technological and industrial sectors underscores their increasing significance in modern applications [93].

Within the domain of the IoT, proximity sensors are instrumental in enabling devices to detect nearby objects and individuals with high precision. Various types of sensors, including ultrasonic and optical sensors, are commonly utilized for these purposes. Ultrasonic sensors, renowned for their adaptability, are extensively deployed in environments such as smart cities, where they contribute to distance measurement and object detection within complex systems [94]. Conversely, low-power optical sensors, such as those based on monolithic CMOS designs, offer high-resolution proximity detection with minimal energy consumption, making them particularly suitable for mobile IoT applications [95]. These technological advancements have significantly improved sensor accuracy and response times, with ultrasonic sensors achieving up to 98.7% accuracy in specific scenarios [96].

Proximity sensors can be categorized based on their underlying detection principles, with key classifications including inductive, capacitive, ultrasonic, photoelectric, laser, and Hall effect proximity sensors [97,98]. The continuous evolution of proximity sensing technologies underscores their fundamental role in advancing automation, smart environments, and IoT-driven innovations.

5.3. Infrared Sensor (IR)

Infrared (IR) sensors consist of two primary components: a transmitter (Tx) and a receiver (Rx) [99]. The transmitter emits infrared rays, while the receiver detects the reflected infrared spectrum using specialized photo-sensitive chips. These sensors are widely utilized across various domains due to their ability to detect thermal radiation, making them essential for applications such as temperature measurement, energy harvesting, and environmental monitoring.

Infrared sensors operate based on the principles of thermal radiation detection and can be categorized into different types, including uncooled microbolometers and wavelength-selective sensors. Recent technological advancements have significantly enhanced their performance. For example, the integration of photometric correction models has improved 3D reconstruction from infrared imagery, producing results that are comparable to traditional RGB sensors [100]. In addition, innovations in nanotechnology have led to the development of narrowband absorbers and emitters, which play a crucial role in nondispersive infrared gas sensing and high-resolution imaging, further expanding the applicability of IR technology [101].

The role of infrared sensors in the IoT has been particularly transformative, especially in intelligent traffic management and environmental monitoring [102]. Empirical studies highlight their effectiveness in optimizing traffic control systems. For example, in Iraq, an intelligent traffic management network was implemented. In this system, the IR sensors were used to regulate signal timings and alleviate congestion at major intersections. Furthermore, passive infrared sensors (PIR) integrated with IoT-based home automation systems have been employed to detect occupancy and improve energy efficiency in residential spaces [103]. In addition, advances in infrared sensing have facilitated real-time environmental monitoring, including the assessment of CO₂ levels and surface temperature variations through portable sensors deployed on unmanned aerial systems (UAS). This approach provides a more flexible and precise alternative to traditional satellite-based observations [103,104].

Infrared sensors are classified into various types based on their detection mechanisms and applications. These include PIR, Active Infrared Sensors, Thermopile Infrared Sensors, Infrared Gas Sensors, Fiber Optic Infrared Sensors, Bolometer Infrared Sensors, Infrared Imaging Sensors, and Infrared Spectroscopy Sensors [98,105]. The continuous advancement of infrared sensor technology underscores its growing significance in diverse industrial, environmental, and scientific applications, solidifying its role in modern technological ecosystems.

5.4. Pressure Sensor

Pressure sensors are integral to various industrial and manufacturing applications, ensuring precise pressure regulation to uphold product quality and operational safety [106]. Advancements in sensor technology have led to the development of highly sensitive designs, such as the two-dimensional photonic crystal ring resonator sensor. This sensor, which operates effectively within a pressure range of 0 to 6 GPa, has demonstrated exceptional sensitivity, making it particularly well-suited for high-pressure applications, including those in the medical field [107]. The calibration of pressure sensors is a critical aspect of their functionality, as it guarantees accuracy and reliability in real-world applications [107].

Beyond industrial use, pressure sensors have become an essential component of the IoT, enabling real-time monitoring and seamless data transmission across multiple domains. Recent innovations have led to the emergence of flexible pressure sensors, which significantly enhance performance and adaptability, particularly in wearable technology and smart home systems [108]. In addition, hydrogel-based pressure sensors have been introduced, playing a vital role in self-monitoring and remote health applications. For example, in massage therapy, these sensors provide continuous pressure feedback through an IoT-connected system, improving treatment precision [109].

Another notable advancement is the development of triboelectric pressure sensors, which are self-powered and characterized by their high sensitivity and adaptability to diverse environmental conditions. These sensors address energy supply challenges in IoT-based applications, reducing reliance on external power sources [110]. Furthermore, the integration of wired and wireless transmission technologies has facilitated real-time data transfer to cloud-based IoT platforms, enhancing monitoring capabilities across various sectors.

Pressure sensors are classified into different types based on their operating principles and applications. These include Piezoresistive Pressure Sensors, Strain Gauge Pressure Sensors, Capacitive Pressure Sensors, Optical Pressure Sensors, Bourdon Tube Pressure Sensors, Aneroid Barometer Pressure Sensors, and Vacuum Pressure Sensors [108,111]. As pressure sensing technology continues to evolve, its impact on industrial automation, healthcare, and IoT ecosystems will further expand, reinforcing its critical role in modern technological advancements.

5.5. Ultrasonic Sensor

Ultrasonic sensors are designed to detect and measure the distance of objects using high-frequency ultrasonic waves, making them highly effective for various industrial and technological applications. These sensors consist of two key transducers: a transmitter that emits ultrasonic waves and a receiver that detects the reflected waves. Their applications span multiple domains, including robotics, fluid characterization, object detection, and industrial maintenance.

In fluid dynamics, ultrasonic sensors are instrumental in assessing viscosity and density with high temporal resolution, enabling non-destructive molecular analysis. Research has demonstrated their effectiveness in tracking protein unfolding, such as in studies involving bovine albumin, highlighting their significance in biochemical applications [112]. In addition, ultrasonic sensors have been widely employed in assistive technologies, particularly in the development of cost-effective rangefinders for visually impaired individuals. These devices enable object detection regardless of lighting conditions; however, their accuracy decreases at extended distances [113]. Recent advancements in ultrasonic measurement techniques have further enhanced object localization through triangulation, improving precision in determining positions within a two-dimensional plane [114,115]. Furthermore, the integration of AI into ultrasonic sensing technology is shaping the future of industrial maintenance, often referred to as Maintenance 4.0, by enabling predictive maintenance and proactive monitoring in manufacturing environments [116].

Beyond industrial applications, ultrasonic sensors are increasingly being utilized in the IoT, where they play a crucial role in proximity sensing and distance measurement. Their implementation in environmental monitoring systems, such as flood detection networks, allows for real-time water level measurements and automated alerts, significantly improving disaster preparedness and response strategies. Similarly, in-home security applications, ultrasonic sensors enhance safety by detecting intrusions and triggering alarm systems, offering an efficient and cost-effective security solution [117]. The versatility of ultrasonic sensors has led to their widespread adoption in smart cities, industrial automation, and various IoT applications, with customized specifications tailored to meet specific operational requirements [118,119]. When integrated with microcontrollers and cloud-based platforms, these sensors facilitate real-time data processing and remote monitoring, further reinforcing their importance in modern IoT ecosystems [120].

Ultrasonic sensors can be categorized based on their operational principles and applications. The primary types include Through-beam Sensors, Proximity Sensors, Ranging Sensors, Transceiver Sensors, High-Frequency Sensors, Piezoelectric Sensors, Dual-element Sensors, and Diffuse Proximity Sensors [121]. Stealth moths: the multi-plumed wings of the moth alucita hexadactyla may decrease the intensity of their echo to simulated bat echolocation cries. As research continues to advance, these sensors will play an even greater role in enhancing automation, safety, and precision across various technological fields.

5.6. Touch Sensor

Touch sensors are electronic devices that function as switches, responding to force, touch, or pressure. These sensors typically register physical interaction from a human finger or a stylus, triggering the flow of electrical current upon contact. In contrast, when no contact is made, the circuit remains inactive, preventing any current from passing through [73,122].

The increasing adoption of touch sensors is gradually replacing traditional mechanical input devices such as keyboards and mice. These sensors are now integral to a wide range of applications, including interactive displays, IoT systems, and robotics. Their implementation spans numerous technological domains, from household appliances like touch-sensitive lamps to sophisticated touchscreen interfaces found in smartphones, laptops, automated teller machines (ATMs), elevators, tablets, automobiles, and digital cameras. Given their widespread use, touch sensors have become a fundamental component in modern touchscreen technologies [123].

From a technical perspective, touch sensors can be classified into three primary categories: resistive, capacitive, and piezoelectric touch sensors [123]. Capacitive touch sensors are highly responsive and capable of detecting both movement and object positioning with precision; however, they require a continuous power source to function effectively. Resistive touch sensors, in contrast, are known for their high sensitivity, fast response times, durability, and cost-effectiveness, making them a preferred choice for budget-sensitive applications. Unlike capacitive sensors, resistive sensors also require an external power source. Piezoelectric touch sensors, on the other hand, operate without the need for an external power supply. They are particularly effective in detecting dynamic physical interactions but are unable to register static forces.

Touch sensors can also be categorized based on their functional capabilities [124]. Single-touch sensors can detect a single point of contact at any given moment, whereas multi-touch sensors have the ability to register multiple points of contact simultaneously. This multi-touch capability enables advanced gesture recognition, such as pinch-to-zoom and swipe motions, which are fundamental to modern touchscreen interfaces. In addition, force touch sensors are designed to measure the intensity of applied pressure, allowing for differentiated responses based on how firmly the user presses on the sensor surface.

As technology continues to advance, touch sensors will remain at the forefront of human-computer interaction, enhancing user experiences across numerous industries. Their integration into IoT systems, wearable technologies, and smart environments is expected to further drive innovation, making them indispensable in contemporary digital ecosystems.

5.7. Color Sensors

Color sensors are sophisticated electronic devices designed to analyze the color characteristics of objects or surfaces by measuring the light they reflect or transmit [125]. These sensors identify various color patterns and convert them into corresponding frequency signals, enabling precise color detection and classification. Their versatility has made them integral to numerous applications, particularly in robotics and industrial manufacturing, where accurate color differentiation is crucial for automation and quality control.

The fundamental operation of color sensors involves illuminating a target object using light-emitting elements and then analyzing the reflected light. The sensor quantifies the intensity of different colors by interpreting RGB (Red, Green, Blue) values, each ranging from 0 to 255. This measurement allows for precise color characterization by determining the relative concentration of each color component in the reflected light [126,127]. Advanced color sensors leverage image-processing algorithms to compute hue values from RGB data, enhancing color detection accuracy under varying lighting conditions [128].

Basic color sensors primarily detect brightness and simple color variations, making them useful for automated sorting tasks based on color differentiation. Conversely, more advanced models incorporate real-time data processing and wireless communication capabilities, making them highly suitable for IoT applications. For instance, color sensors have been employed in monitoring real-time electrolyte color changes in lithium-ion pouch cells, offering valuable insights into battery performance and health.

Despite their advantages, color sensors are susceptible to performance limitations, particularly in environments with inconsistent or inadequate lighting. Ensuring stable illumination is essential for maintaining accuracy and reliability in color detection.

There are several types of color sensors, each tailored to specific applications. These include Spectral Color Sensors, RGB Color Sensors, and Colorimeters, each offering distinct advantages based on their detection principles and intended use.

As technological advancements continue, the integration of color sensors with AI and IoT frameworks is expected to enhance their functionality, further expanding their applications across diverse industries, from automated manufacturing to environmental monitoring.

5.8. Chemical Sensor

The International Union of Pure and Applied Chemistry (IUPAC) defined chemical sensors in 1991 as devices that convert chemical information into an analytically useful signal [129]. Fundamentally, these sensors translate a given chemical input, derived from a reaction or molecular interaction, into a measurable electrical, optical, or thermal output. This chemical information may originate from various sources, such as molecular composition or chemical activity. A chemical sensor is typically composed of two essential components: a chemical recognition system (receptor) and a chemical-physical transducer, which together facilitate the detection and measurement of target analytes.

Chemical sensors have a broad spectrum of applications, ranging from household appliances to complex industrial and environmental monitoring systems. They play a crucial role in healthcare, where they are used for blood glucose monitoring and real-time diagnostic wearables. In environmental sciences, they are instrumental in detecting air and water pollutants such as nitrogen oxides or carbon monoxide. Within industrial processes, these sensors ensure chemical process safety and enable continuous monitoring of gas pipelines to prevent hazardous leaks. In addition, they are integral to safety systems, where they serve as toxic gas detectors and early fire warning mechanisms [129,130].

One of the defining attributes of chemical sensors is their selectivity and sensitivity. Selectivity refers to the sensor’s ability to respond specifically to a particular molecular structure or reactivity, minimizing interference from other chemical species in the environment [130]. Sensitivity, on the other hand, determines the lowest concentration of an analyte that can be reliably detected. The rapid advancements in instrumentation, microelectronics, and computational technologies have significantly enhanced the performance of these sensors, allowing for more precise and efficient chemical detection based on well-established chemical, physical, and biological principles.

Chemical sensors can be categorized based on the working principle of their transducers [131]. Among the various types, optical and electrochemical sensors are the most widely utilized for chemical composition analysis [18]. Optical sensors rely on light-matter interactions to detect specific chemical species, whereas electrochemical sensors measure electrical signals generated by chemical reactions, often used for applications requiring high precision and real-time monitoring [132].

Given their diverse applications and fundamental importance across multiple disciplines, chemical sensors continue to be an area of intense research and technological innovation, with ongoing developments aimed at improving their efficiency, miniaturization, and integration into smart sensing systems.

5.9. Magnetics Sensor

For several decades, magnetic field sensors have played a crucial role in various technological and industrial applications, enabling precise, non-contact detection of electronic guiding systems and moving objects. These sensors have become an essential component in modern systems due to their ability to detect and measure magnetic field properties, such as flux, strength, and direction, and subsequently convert this information into an electrical signal for processing via electronic circuits [133].

Among the most widely used types of magnetic field sensors are Hall effect sensors and magnetoresistive sensors, both of which are designed to detect the presence, direction, or intensity of a magnetic field. These sensors are commonly utilized in applications such as position sensing, velocity detection, and movement tracking of objects. Their unique capability to sense movement using the Earth’s magnetic field has led to their integration into portable navigation systems, indoor and urban positioning technologies, and even product promotion mechanisms.

Several different technologies have been employed in the design and development of magnetic field sensors, each offering distinct advantages depending on the intended application. Fluxgate sensors, Hall effect sensors, resistive sensors, inductive sensors, and proton precession magnetometers represent some of the key categories, with each type leveraging a different approach to magnetic field detection.

A particularly notable example is the Hall effect sensor, which has gained widespread adoption due to its compatibility with CMOS technology. However, despite its advantages, it faces certain challenges, such as low sensitivity and power constraints. The Hall effect itself, first discovered in 1879, forms the basis of Hall effect sensors, which function by detecting changes in voltage when subjected to a fixed magnetic field. This principle allows for precise measurement of both the presence and magnitude of magnetic fields [133,134].

Magnetic field sensors can generally be categorized based on whether they measure the total magnetic field or focus on vector components of the magnetic field. The majority of these sensors exploit the fundamental relationship between magnetic and electrical phenomena, allowing for a diverse range of sensing methodologies [135]. Some of the most prominent types of magnetic sensors [134,135] include the following:

• Fluxgate Sensors: Utilized in applications requiring high sensitivity and stability, such as geophysical research and aerospace navigation.
• Hall Effect Sensors: Widely used in industrial automation, automotive applications, and electronic compasses.
• Magnetostrictive Sensors: Employed in precision measurement systems, structural health monitoring, and advanced industrial sensing. As advancements in miniaturization, energy efficiency, and signal processing continue to progress, magnetic field sensors are expected to play an increasingly critical role in next-generation smart technologies, autonomous systems, and environmental monitoring applications.

5.10. Seismic Sensors

Seismic sensors are sophisticated instruments designed to detect, measure, and record seismic waves and ground movements with high precision. These sensors, often referred to as seismometers, play a crucial role in monitoring earthquakes, geophysical explorations, volcanic activity, and other ground vibrations. By amplifying even the slightest movements of the Earth’s surface, seismic sensors provide valuable data essential for scientific research, disaster prevention, and engineering applications. There are two primary categories of seismic sensors and their calibration [136]:

• Inertial Seismometers: These sensors operate based on the relative motion between a suspended mass and the ground, allowing for precise measurement of ground acceleration.
• Strain Meter or Extensometer Seismic Sensors: These devices detect ground strain and deformation by measuring changes in the distance between fixed points within the Earth’s crust.

Seismic sensors have vast potential in a range of critical applications, including geophysical research, geotechnical monitoring, and early warning systems for tremors and earthquakes [137]. Their deployment is essential for understanding tectonic activity, assessing structural integrity, and enhancing predictive models for natural disasters. As technological advancements continue, seismic sensors are expected to play an even greater role in earth sciences, engineering, and global disaster resilience efforts [138].

5.11. Position Sensors

The position or displacement of an object can be determined using this type. Object position is determined with respect to some fixed point either linearly or in a rotational manner. There are many types of it, such as encoders, eddy current sensors, potentiometers, and linear variable differential transformers (LVDTs). Basically, the linear sensor measures the displacement in a straight line, whereas the angular displacement is calculated using rotational sensors. A position sensor (known as a linear potentiometer) is used to measure the displacement of the object by producing a resistive output proportional to the target object’s displacement. An example of this sensor is LVDT, which converts linear motion into an electrical signal, and it is considered an accurate sensor that is used in industrial automation and machine tools.

5.12. Temperature Sensors

Temperature sensors, as integrated circuit devices, play a crucial role in measuring the thermal energy of objects or systems by detecting variations in heat and cold [97]. The output voltage of these sensors is directly proportional to temperature fluctuations, making them indispensable in a wide range of applications. These sensors are widely utilized in household appliances, including microwave ovens, refrigerators, air conditioners, and various smart home systems. More recently, they have found applications in critical sectors such as healthcare, agriculture, and manufacturing, where precise temperature control is necessary for efficiency, safety, and product quality. In agriculture, for instance, monitoring soil temperature is essential to ensure optimal crop growth and yield, as temperature variations significantly influence plant development [1].

Temperature sensors can be broadly classified into two main categories: contact and non-contact sensors [139]. Contact sensors require direct physical interaction with the object being measured, allowing them to monitor temperature variations in solids, liquids, and gases across a defined range. These sensors are commonly used in industrial processes where precise thermal readings are needed. Conversely, non-contact temperature sensors operate by detecting radiation and convection properties, enabling them to measure temperature changes without direct contact with the target object. These sensors rely on various physical principles, including electrical, magnetic, optical, and sonic mechanisms, to obtain accurate temperature readings. Such technology is particularly useful in environments where direct contact is impractical or where high temperatures could damage the sensor.

Several types of temperature sensors exist, each designed to meet specific operational requirements [140]. These include thermostats, which regulate temperature in heating and cooling systems; thermistors, known for their high sensitivity to temperature variations; and resistive temperature detectors (RTDs), which provide stable and accurate readings. In addition, infrared sensors are widely used for remote temperature measurement in medical and industrial settings, while bimetallic strips operate through mechanical expansion and contraction, often employed in thermostats. Thermopiles, composed of multiple thermocouples, are used in infrared thermometry, whereas thermocouples themselves are among the most versatile temperature sensors, generating voltage differences based on temperature gradients. These diverse sensor technologies continue to enhance temperature monitoring capabilities across various industries, ensuring precise thermal regulation and improved operational efficiency.

5.13. Light Sensors

A light sensor is a specialized device designed to convert light energy into an electrical signal, allowing for the measurement of light intensity in various environmental conditions [141]. These sensors play a crucial role in detecting different types of light, including ambient light, reflected light, and artificial lighting within indoor spaces. By analyzing these variations, they enable precise control over numerous applications that rely on light-sensitive technology.

There are multiple types of light sensors, each operating based on distinct principles to convert light signals into electrical outputs. Some of the most common variants include photovoltaic cells, charge-coupled devices (CCDs), photoresistors, also known as light-dependent resistors (LDRs), as well as phototubes, photomultiplier tubes, photodiodes, and phototransistors. Each of these sensor types is designed to serve specific functions across various industries. For instance, phototransistors exhibit high sensitivity, making them particularly effective in applications such as street lighting, where they automatically adjust illumination levels in response to changes in natural light. Similarly, photoresistors are widely used in cameras due to their ability to vary resistance based on light intensity, ensuring optimal exposure settings.

The versatility of these sensors has led to their integration into numerous technological advancements, from consumer electronics to industrial automation and scientific instrumentation. Their ability to detect and respond to changes in light conditions has made them indispensable in applications ranging from smart lighting systems and optical communication to biomedical imaging and environmental monitoring. As research continues to refine their efficiency and responsiveness, light sensors remain a fundamental component in the evolving landscape of sensor technology.

5.14. Humidity Sensors

A humidity sensor is a sophisticated device designed to measure the moisture content present in the air or other gaseous environments [73]. Since humidity is inherently a non-electrical parameter, this sensor operates by converting it into an electrical signal through variations in resistance, capacitance, or impedance. This transformation enables precise measurement and monitoring, making humidity sensors indispensable in a wide range of applications.

These sensors play a crucial role in regulating heating, ventilation, and air conditioning (HVAC) systems, ensuring optimal indoor air quality and thermal comfort. Beyond residential and commercial climate control, they are widely employed in industrial and specialized settings such as hospitals, the pharmaceutical industry, museums, and greenhouses, where maintaining precise humidity levels is essential for preserving artifacts, ensuring product stability, and optimizing plant growth [1].

Humidity sensors are categorized into several types based on their operational principles [142]. The most common variants include resistive hygrometers, aluminum oxide hygrometers, capacitive hygrometers, microwave refractometers, and crystal hygrometers. Each type is designed to function in specific environments and applications, ensuring accurate and reliable humidity detection.

With continuous advancements in sensor technology, humidity sensors have become increasingly precise and responsive, contributing significantly to fields ranging from meteorology and environmental monitoring to industrial automation and biomedical applications. Their ability to provide real-time data on atmospheric moisture levels makes them an essential tool in modern science and engineering.

5.15. Medical Sensors

The operational principles of medical sensors embedded in wearable technology vary based on underlying physical effects such as photoelectric, Hall, and piezoelectric phenomena. These sensors are designed to capture key physiological parameters and typically comprise both sensing and signal conversion components [143]. The core challenge in designing wearable medical devices lies in selecting and integrating appropriate sensors. Various types are utilized for this purpose, including pulse oximeters, electroencephalography (EEG) sensors, respiratory sensors, heart rate monitors (ECG), electrodermal activity (EDA) sensors, SpO2 detectors, and blood pressure monitors [144,145]. A comprehensive historical overview of wearable sensors is available in [146]. Initially, traditional wearable sensors were limited to heart rate monitoring, as it was considered the most fundamental physiological parameter. However, with technological advancements, photoplethysmography (PPG) was introduced, enabling the simultaneous measurement of SpO2 and heart rate [147]. Modern wearable technologies integrate chemical and biological materials with optical systems, microfluidics, electronics, and micromechanical components, creating compact, wireless, and battery-powered systems capable of continuous physiological monitoring [29,148]. Data transmission within wearable devices can occur through the human body as a conductive medium or via established wireless communication protocols such as Wi-Fi, Bluetooth Low Energy (BLE), Zigbee, and other network technologies [27]. These devices are designed to collect, classify, and store long-term physiological and activity data, facilitating continuous health monitoring. Broadly, wearable technology can be implemented using two key approaches: wireless communication systems and electronic textile-based solutions, as detailed in [149]. Extensive research has been conducted on wearable sensors, emphasizing their technological advancements, significance in various industries, material composition, target biomarkers, and design considerations. A detailed examination of these aspects can be found in multiple studies, including [27,147,150,151,152,153].

6. Integrating Sensor Networks and IoT into Industrial Applications

The integration of IoT technologies with advanced sensor systems has fundamentally transformed the landscape of industrial and engineering applications [154]. In particular, the construction industry has witnessed a paradigm shift, as sensor-based solutions enhance project planning, execution, and overall efficiency. One of the most promising areas of this technological convergence is the field of engineering, where the incorporation of AI-driven and IoT-enabled sensors has introduced unprecedented capabilities for monitoring, optimization, and control [155].

In engineering applications, the synergy between AI and IoT-based sensors has redefined how data is collected, processed, and utilized in real-time. These sensors provide continuous insights into various operational fields, ensuring greater precision, efficiency, and adaptability. The integration of AI in structural and industrial processes has significantly improved the accuracy of predictive models, optimized system performance, and enhanced the overall reliability of engineering designs [155].

A particularly noteworthy advancement in this domain is the rapid evolution of wireless sensor network technologies. The emergence of the IoT and AI has facilitated the widespread adoption of low-cost, embedded automation systems, enabling real-time monitoring and control. Small wireless sensor nodes, strategically deployed based on specific application needs, measure critical parameters such as temperature, distance, speed, torque, acceleration, pressure, and vibration. These data points are then wirelessly transmitted to a central processing node for comprehensive analysis [156].

Sensor technologies have become indispensable across numerous industrial sectors, driving innovation and efficiency in manufacturing, construction, and automation. The principal applications of sensors in industrial contexts highlight their role in enhancing real-time decision-making, predictive maintenance, and intelligent process control. As advancements in AI and IoT continue, the role of sensors in engineering and industrial applications will expand further, paving the way for smarter, more interconnected systems that enhance productivity and operational resilience.

6.1. Advanced Structural Health Monitoring Systems Using Sensor Technology

Structural Health Monitoring (SHM) integrates advanced sensor technologies, data analytics, and intelligent algorithms to assess the condition of infrastructure in real time [157,158]. This sophisticated approach plays a crucial role in continuously evaluating the integrity of structures, allowing for proactive maintenance and risk mitigation. By leveraging adaptive algorithms and state-of-the-art sensing techniques, SHM enhances safety, reliability, and structural performance while extending the operational lifespan of critical infrastructure. Furthermore, it facilitates the development of automated, unsupervised monitoring systems, reducing the need for manual inspections and human intervention.

One of the key advantages of Structural health monitoring is its ability to conduct periodic and continuous monitoring of structural integrity. By deploying an array of smart sensors equipped with advanced computational and communication capabilities, Structural health monitoring systems can detect and analyze deterioration in structures over time. These sensors continuously collect data, enabling real-time assessment of a structure’s condition and facilitating early detection of anomalies. This capability is particularly beneficial in critical applications such as bridges, high-rise buildings, and transportation networks, where early diagnosis of structural changes can prevent catastrophic failures and ensure public safety [159,160].

The methodologies employed in structural health monitoring systems range from vibration-based monitoring to IoT-enabled sensor networks, allowing for precise measurement of key parameters such as strain, temperature variations, and vibrational responses. These insights enable engineers to optimize maintenance strategies, reduce operational costs, and improve diagnostic accuracy. In addition, advanced structural health monitoring techniques support the development of more effective repair and retrofitting solutions by optimizing sensor configurations to detect localized structural weaknesses [161].

Various types of sensors are used in structural health monitoring applications, including accelerometers, strain gauges, temperature sensors, microelectromechanical systems (MEMS), fiber optic sensors, and displacement sensors. These sensors operate using different sensing principles, such as mechanical deformation, temperature-induced variations, and fiber optics-based deformation detection. Typically, both passive and active sensors are incorporated into structural health monitoring frameworks to enable real-time monitoring and rapid structural evaluation, ensuring comprehensive and continuous health assessments of infrastructure systems [162].

6.2. Environmental Monitoring-Based Sensing Techniques

Environmental monitoring involves the systematic observation, measurement, and analysis of various natural and built environment parameters, including air, water, soil, and climate conditions. With rapid advancements in technology, particularly through the integration of the IoT, machine learning, and photonic sensors, data collection has become more precise and frequent. These technological innovations significantly enhance the ability to detect and track environmental changes in real time, providing valuable insights for research and decision-making [163,164].

Sensor technologies, particularly wireless sensor networks (WSN), have become instrumental in environmental monitoring, offering comprehensive solutions for assessing pollution levels, identifying contamination sources, and evaluating environmental risks. These sensors are extensively used to monitor soil, air, and water quality, which are all critical factors in understanding and mitigating the adverse effects of pollution. By continuously collecting data, sensors play a key role in recognizing patterns and detecting anomalies that could indicate environmental degradation, ultimately supporting sustainability efforts and conservation initiatives [165].

The significance of environmental monitoring extends beyond mere data collection; it serves as a foundation for informed policymaking and resource management. By leveraging real-time sensor data, authorities can implement early warning systems, enforce environmental regulations, and develop strategies to minimize ecological harm. In addition, sensor-based monitoring contributes to predictive modeling, allowing for proactive interventions that help safeguard ecosystems and public health. As a result, the integration of advanced sensor technology into environmental monitoring frameworks is essential for promoting a healthier, more sustainable planet while enabling data-driven policies that support long-term ecological resilience [165].

6.3. Proximity and Safety Detection Sensors

In environments where humans and robotic systems coexist, safety sensors and proximity detection technologies play an essential role in ensuring both operational efficiency and accident prevention. These systems rely on a range of advanced sensing mechanisms to identify and respond to the presence of individuals or objects near robotic platforms and automated vehicles. As a result, this mitigates risks associated with unintended collisions. Among these technologies, flexible ultrasonic sensors have emerged as a key innovation in human-robot collaboration (HRC). They provide real-time object detection at high accuracy and expansive coverage across the detection plane [166].

To further enhance physical human-robot interaction (PHRI), researchers have developed multi-functional safety sensors that integrate both capacitive and inductive sensing. These hybrid sensors are capable of distinguishing between human bodies and inanimate objects. This enables critical safety functions such as emergency stops and collision avoidance in industrial and collaborative robotics settings [167]. In addition, capacitive proximity sensors have become widely adopted in industrial automation to regulate the spatial relationship between workers and robotic systems. These sensors are equipped with motion tracking and profile recognition capabilities. This allows them to improve detection accuracy and response precision [168].

In the realm of automated guided vehicles (AGVs), safety-related sensor systems have been increasingly developed to explicitly detect human presence. This ensures compliance with evolving safety regulations and industry standards. Continuous adaptation to changing normative requirements enables these sensor technologies to contribute to safer and more adaptive industrial environments [169]. Collectively, the integration of these cutting-edge sensing technologies underscores the critical importance of ensuring safe and effective human-robot and human-vehicle interactions, paving the way for more intelligent, responsive, and secure automation systems.

6.4. GPS Tracking and Geospatial Data Technologies

Geospatial technology represents a sophisticated suite of tools designed to facilitate the mapping, tracking, and analysis of both natural and built environments. By integrating Global Navigation Satellite Systems (GNSS), Remote Sensing (RS), and Geographic Information Systems (GIS), this technology plays a crucial role in data-driven decision-making, operational management, and strategic planning across various disciplines [170,171].

One of its most critical applications lies in environmental disaster management, where geospatial tools support the monitoring, response, and mitigation of natural and human-induced disasters, including landslides and oil spills. By leveraging real-time spatial data, these systems enhance disaster preparedness and enable rapid intervention to minimize environmental and societal impacts [172]. A key component of geospatial technology is GPS tracking, which relies on beacon signals to transmit precise location data via satellite networks. Over the years, GPS technology has undergone significant advancements, leading to the development of miniaturized, highly portable devices that can now be embedded in everyday consumer electronics such as smartphones and wearable devices [173].

Beyond environmental and disaster response applications, geospatial technology is widely employed in real-time monitoring and on-site operations management, where it enhances workflow optimization and service delivery through mobile platforms. The ability to track assets, personnel, and logistics with unparalleled precision underscores the transformative impact of geospatial systems across industries ranging from urban planning to transportation and emergency response [171]. These technological advancements highlight the profound significance of geospatial solutions in modern society, reinforcing their role in driving innovation, efficiency, and informed decision-making.

6.5. Inspection Based on Camera and Imaging Sensors

Cameras and imaging sensors have become indispensable in modern inspection systems, significantly enhancing efficiency, precision, and reliability in defect detection across various manufacturing sectors. In industries such as aerospace, where composite part inspection is critical, automated camera-based inline inspection has revolutionized traditional workflows by drastically reducing manual inspection time, which can otherwise account for up to 50% of total production time [174].

To further optimize image quality and ensure accurate defect detection, these systems often incorporate internal reference illumination, minimizing interference from external optical arrangements. In addition, recent innovations such as multi-module inspection devices have enabled the simultaneous evaluation of multiple camera modules, thereby increasing throughput, efficiency, and overall system performance. The continuous evolution of sophisticated imaging technologies plays a pivotal role in advancing industrial inspection processes, reinforcing quality control measures, and driving improvements in manufacturing precision across aerospace, automotive, and other high-precision industries.

6.6. Using Vibration and Acoustic Sensors for Industrial Equipment Health

Acoustic and vibration sensors have become essential in industrial equipment monitoring, significantly improving predictive maintenance and operational efficiency. By leveraging vibration analysis, advanced sensors such as accelerometers and optical fiber sensors provide valuable insights into the condition of rotating machinery, facilitating early fault detection and enhancing system reliability [175,176].

In contrast, acoustic-based monitoring offers a non-contact approach, utilizing microphones to analyze sound signals that indicate machine health. However, challenges such as background noise interference must be carefully managed to ensure accuracy [177]. Recent technological advancements have introduced sensing yarns embedded with MEMS technology, enabling real-time vibration monitoring in wearable formats, which is particularly advantageous for industries like construction, where continuous equipment health tracking is crucial [178].

The integration of these advanced sensing technologies with IoT frameworks has further revolutionized industrial health monitoring, allowing for comprehensive, real-time diagnostics that enhance safety, efficiency, and maintenance strategies across various sectors [179].

6.7. Identify Hazardous Conditions Based on Environmental Sensors

Gas and environmental sensors are indispensable for detecting and monitoring hazardous gases, particularly in industrial settings where toxic exposure poses significant risks. These advanced sensors leverage cutting-edge materials and technologies, including metal oxide semiconductors (MOS) and integrated photonic systems, to deliver exceptional sensitivity and rapid response times in detecting harmful gases such as hydrogen sulfide, sulfur dioxide, and carbon monoxide [180,181].

Recent advancements in sensor design have incorporated nanomaterials and specialized morphologies, significantly enhancing selectivity, stability, and energy efficiency. These innovations enable more precise gas detection while reducing power consumption, making them particularly valuable for continuous monitoring in industrial, environmental, and public health applications. The integration of these sophisticated sensing technologies plays a pivotal role in accident prevention, occupational safety, and environmental protection, ensuring safer and healthier surroundings [181].

7. Challenges and Future Research Direction

As mentioned earlier, the widespread adoption of smart technologies and the IoT has made sensors indispensable across various fields. However, despite the numerous advantages of sensors discussed earlier, several critical challenges still persist. In particular, sensors present specific implementation challenges that must be addressed to enhance their performance. Below, we outline some of the key challenges associated with sensor-based IoT systems. Figure 9 summarizes the common challenges presented in this section.

7.1. Security and Privacy Challenges

The data generated by sensor networks often contain sensitive information, necessitating stringent measures to ensure privacy and security. A critical concern arises when the integrity of the sensor network is compromised, leading to unauthorized access, data breaches, and potential misuse of collected information. One of the primary challenges in securing sensor networks is the limited computational capacity of sensor nodes, which restricts the implementation of complex cryptographic algorithms. Traditional encryption techniques require high computational power, making them unsuitable for resource-constrained sensor devices. As a result, alternative security solutions such as one-way key chains or effective key management schemes must be explored. However, these simplified approaches may inadvertently weaken the security framework of sensor networks. The preference for localized security algorithms in these networks is largely driven by constraints related to computational efficiency, communication overhead, spatial limitations, and transmission latency [182].

To ensure the reliability and trustworthiness of sensor data, robust security mechanisms must be in place to safeguard the confidentiality and integrity of transmitted information. Data must be protected from unauthorized interception, eavesdropping, and manipulation through the implementation of strong encryption protocols and authentication techniques. The absence of these security layers can significantly undermine the effectiveness and reliability of smart sensor systems [183].

Furthermore, the integration of wireless sensor networks within smart grid infrastructures introduces additional security and privacy risks. Unauthorized access to sensitive consumer data could lead to serious privacy violations and cyber threats, compromising both individual users and the broader system. These vulnerabilities underscore the urgent need for comprehensive security frameworks and robust privacy protection mechanisms to safeguard both user data and the operational integrity of smart grid networks [184].

7.2. Scalability in Sensor-Based IoT Systems

In real-time scenarios, a huge stream of data collected by sensors needs to be stored, processed, and analyzed. Therefore, the vast volumes of data produced by smart sensors necessitate the implementation of robust data management strategies. A primary challenge in this domain is the escalating data load and the complexities associated with its management. Given that these sensors continuously generate extensive streams of sensory information, efficiently processing, analyzing, and extracting meaningful insights from such data presents a significant challenge [185]. Developing advanced analytical frameworks and scalable data management solutions is essential to ensure the effective utilization of sensor-generated data while maintaining computational efficiency and system performance.

Additionally, IoT-enabled sensor systems comprise a vast network of interconnected devices that continuously generate extensive volumes of data. To ensure effective utilization, this data must be systematically collected, stored, and analyzed. Given the diversity of IoT applications, different types of data are produced; for example, medical imaging devices generate high-resolution image data, while other IoT sensors capture real-time video streams. This inherent diversity contributes to the well-documented challenges associated with big data, necessitating robust data management strategies.

The scalability remains to date a significant challenge in IoT-based systems [186]. With billions of interconnected IoT devices continuously generating vast volumes of sensor data, the ability to efficiently process, store, and analyze this information becomes increasingly complex. As the volume of sensor-generated data escalates, traditional storage and processing methods may become insufficient, necessitating the development of scalable architectures capable of managing large-scale data flows. To address this issue, cloud-based storage, advanced big data analytics, and edge computing techniques must be integrated to optimize data processing and support real-time decision-making [187]. These advancements will be instrumental in enhancing data-driven therapeutic strategies, predictive analytics, and automated solutions. Therefore, further research is essential to explore how scalable data architectures can be effectively implemented within sensor-based IoT ecosystems.

Big data, generated through the continuous operation of numerous IoT-connected sensors and actuators, presents a substantial computational challenge. Each device within these networks collects, processes, and transmits extensive volumes of data, requiring advanced techniques for efficient management. One of the primary difficulties lies in handling diverse data structures, formats, and transmission rates while operating within the constraints of limited computational resources and processing power.

In this context, AI-driven methodologies offer a promising avenue for enhancing big data management within sensor-based IoT networks. AI-driven approaches can facilitate intelligent data classification, predictive analytics, and automated decision-making, significantly improving efficiency. Consequently, continued research is required to investigate the role of AI and machine learning in optimizing big data processing for large-scale IoT environments.

7.3. Managing Data Heterogeneity in Sensor-Based IoT Systems

Sensors within IoT ecosystems generate vast amounts of data originating from diverse and heterogeneous sources. This heterogeneity primarily manifests in two key forms: sensor heterogeneity and data heterogeneity. Sensor heterogeneity arises from the integration of multimodal sensors that vary in structure, operational mechanisms, and data acquisition processes. Meanwhile, data heterogeneity pertains to variations in data formats, organization, and semantic representation, which pose significant challenges in data sharing, interoperability, and reuse. The absence of standardized frameworks for summarizing and structuring sensor-generated data further exacerbates these challenges, limiting the seamless exchange of information across different platforms.

Despite ongoing efforts to develop standardized data models for sensor semantics, there remains a pressing need to establish universally accepted frameworks that accurately represent heterogeneous sensor data. Addressing this complexity necessitates the adoption of advanced computational techniques capable of processing, organizing, and interpreting diverse data formats. One of the most promising approaches involves leveraging AI and machine learning (ML) algorithms, which can facilitate the integration and interpretation of heterogeneous data by identifying patterns and structuring unorganized datasets. Consequently, further research is required to explore the potential applications of AI and ML in effectively managing data variability within sensor-based IoT systems.

7.4. Regulatory Challenges in Sensor Technology Implementation

Ensuring compliance with regulatory standards poses a significant challenge in the advancement and large-scale deployment of sensor technologies, often imposing limitations on their adoption across industries. One of the primary concerns is the absence of universally accepted evaluation metrics, which complicates the assessment of sensor performance and algorithmic accuracy across diverse systems and operational settings. Without standardized benchmarks, verifying adherence to regulatory requirements becomes increasingly difficult, leading to disparities in performance validation and inconsistencies in compliance enforcement [188].

Furthermore, the regulatory framework governing sensor technologies is fragmented due to the involvement of multiple governing bodies, each enforcing distinct and often conflicting compliance mandates. This lack of harmonization presents significant challenges in achieving global safety, quality assurance, and accessibility standards for sensor-based applications [189]. Consequently, regulatory inconsistencies prolong certification processes and restrict the adaptability of sensor technologies across different sectors and geographical regions.

The complexity of regulatory adherence is further heightened by the diversity of devices and communication protocols within the IoT ecosystem. The heterogeneous nature of IoT infrastructures adds another layer of difficulty in designing, developing, and integrating sensor applications within interconnected systems, making compliance efforts even more challenging.

Addressing these challenges necessitates a collaborative approach among key stakeholders, including technology developers, regulatory authorities, and industry leaders. Establishing harmonized compliance frameworks, standardized evaluation methodologies, and industry-driven best practices is essential to balancing technological innovation with regulatory integrity. By fostering cooperative regulatory initiatives and developing clear, adaptable guidelines, the industry can ensure that sensor technologies meet stringent compliance requirements while facilitating their seamless integration into real-world applications.

7.5. Sensor Compatibility and Integration

The demand for machine elements equipped with diverse sensory capabilities has risen significantly in recent years. However, the integration of various sensor technologies remains a complex challenge due to discrepancies in system architectures, software compatibility, and hardware constraints. The difficulty of ensuring seamless interoperability between multiple sensor technologies and existing infrastructures continues to be a major obstacle, necessitating further advancements in both hardware and software integration strategies [190].

Addressing the complexities of integrating external sensors with internal systems requires careful consideration of several key factors. One critical aspect is miniaturization, which presents a significant challenge as technological advancements drive the demand for compact and portable devices. The integration of sensors into increasingly miniaturized systems must be achieved without compromising performance. While sensor technology continues to advance in terms of size reduction and repeatability, maintaining cost-effectiveness and overcoming associated technical barriers remain essential for sustained progress [18].

Another crucial factor is compatibility with existing systems. Wireless sensor networks (WSNs), for instance, often face difficulties in integrating with current data management frameworks and security protocols. This necessitates the development of new application programming interfaces (APIs) and communication protocols to facilitate seamless integration. In addition, efficient data-sharing mechanisms must be implemented to ensure interoperability between sensors and broader system infrastructures, requiring strict adherence to standardized data formats and transport protocols [191]. For example, in the case of geothermal monitoring sensors, new sensing technologies must be designed to interface effectively with existing monitoring systems and infrastructure to achieve accurate and reliable results [192].

These integration challenges highlight the ongoing need for research and innovation in sensor technology, particularly in the areas of miniaturization, interoperability, and data management. A comprehensive overview of additional issues related to these challenges is provided in Figure 9 [193,194,195,196,197].

The flexibility of sensors presents significant challenges when they are embedded as internal components within various materials. In such cases, sensors are often perceived as foreign elements, leading to structural issues such as metal fatigue, breakage, and delamination in composite materials. To mitigate these effects, sensors must be designed and integrated in a manner that preserves the integrity and mechanical properties of the host material while still providing reliable monitoring capabilities. Moreover, as sensor technology advances, miniaturization plays a crucial role. The transition from conventional sensor chips to ultra-thin, flexible sheets necessitates that the physical and mechanical characteristics of the embedded sensors align seamlessly with the material in which they are incorporated [193].

The convergence of sensors with emerging technologies such as wireless communication, AI, and ML introduces further complexities. Wireless connectivity is particularly crucial for enabling remote monitoring and autonomous sensor operation. However, integrating sensor-generated data with sophisticated analytical frameworks, including AI-driven data processing and ML-based pattern recognition, presents substantial challenges in terms of interpretation, particularly within multi-sensor environments [195].

In addition, flexibility is a key attribute in modern sensor applications, particularly in the fields of wearable technology and electronic dermis. Flexible electronics offer significant advantages due to their ability to conform to diverse geometries and irregular surfaces. However, traditional power supply and communication modules, which are typically based on rigid structures, often struggle to integrate effectively with flexible electronic components. This misalignment leads to constraints in achieving optimal sensor performance and advanced functionality. Overcoming these technical barriers is essential for the development of fully integrated, flexible sensor systems that maintain both durability and efficiency [196].

Another critical challenge is the variability and adaptability of sensors across different applications. Sensor networks continuously collect data that may fluctuate over time due to environmental changes or variations in human behavior. This dynamic nature necessitates the implementation of adaptive algorithms capable of responding to these fluctuations in real time. Furthermore, in applications involving activity recognition, sensors must operate in complex, multi-condition environments where simultaneous or overlapping activities occur. This significantly increases the complexity of data processing and requires advanced computational models that can dynamically adjust to evolving contextual factors [197].

Addressing these challenges demands continuous advancements in sensor materials, integration techniques, and computational algorithms. Future research must focus on developing innovative solutions that enhance sensor resilience, improve data fusion methodologies, and enable seamless adaptability across various real-world applications.

7.6. Challenges of Standardization in Sensor Technology

The lack of standardization presents a major obstacle to the widespread adoption and seamless integration of smart sensor technologies. The absence of universally accepted standards and communication protocols creates compatibility issues, particularly when integrating sensors from different manufacturers into a unified system. These inconsistencies hinder interoperability, making it challenging to develop cohesive sensor networks that function efficiently across diverse platforms [185].

Moreover, the existence of overlapping or conflicting standards can lead to further confusion, complicating efforts to establish uniform frameworks for sensor integration. Such inconsistencies prevent the seamless operation of interconnected systems, reducing their overall effectiveness and scalability.

A critical aspect affected by the lack of standardization is sensor security, which comprises both network security and data security. Network security encompasses key elements such as authentication, secure localization, and anonymity, while data security focuses on privacy protection and data integrity. In the absence of standardized security protocols, manufacturers often implement their own proprietary solutions, which can introduce vulnerabilities and inconsistencies across different sensor networks. Establishing universal security standards is therefore essential to ensuring robust protection for both network infrastructure and data integrity [198].

In addition, the lack of a standardized data format creates substantial challenges for developers and engineers attempting to efficiently process and share sensor-derived information. Without a unified approach to data structuring and transmission, interoperability between different sensor platforms remains a significant hurdle. Thus, the implementation of standardized communication and data formatting techniques is crucial for enhancing the efficiency, security, and reliability of smart sensor networks [199].

7.7. Reliability and Precision in Advancing Sensing Technologies

The IoT comprises an interconnected network of physical devices equipped with embedded software and network connectivity, enabling seamless data collection, exchange, and processing. These systems leverage advancements in low-cost computing, cloud infrastructure, big data analytics, and mobile technologies to facilitate efficient data-driven decision-making [200]. However, the reliability and accuracy of sensor data remain critical factors in ensuring the integrity and effectiveness of IoT applications [201]. Given that sensor-based devices operate in diverse and often unpredictable environments, they are susceptible to inaccuracies that may compromise the quality of the collected data. Addressing these issues is essential to maintaining sensor reliability throughout their operational lifespan [188].

Ensuring the precision of sensor-generated data is particularly vital in advanced technological domains such as cyber-physical systems (CPS) and autonomous systems. These fields face significant challenges due to sensor inaccuracies, environmental variations, and the complex nature of integrating data from multiple sources. Overcoming these obstacles requires a systematic approach that includes reclassifying sensor errors, implementing redundancy mechanisms, and deploying sophisticated error detection and correction algorithms. By adopting these strategies, sensor networks can enhance data fidelity, improve system resilience, and support the continued evolution of IoT-based applications in critical sectors such as healthcare, smart infrastructure, and industrial automation.

7.8. Optimizing Energy

The design and operation of sensor networks are inherently constrained by power source limitations and energy efficiency challenges, which play a crucial role in ensuring reliable and sustainable sensor functionality. Achieving an optimal balance between power consumption and performance is essential, particularly given the increasing demand for energy-efficient technologies. This challenge is further exacerbated by the substantial volume of data generated by sensors, which requires efficient processing mechanisms. Additionally, power efficiency is particularly critical for wireless sensor nodes, especially in dynamic and unpredictable environments where energy availability is inconsistent [202].

Several key issues should be addressed to enhance the energy efficiency and sustainability of sensor networks, which include the following points.

• Power Source Limitations: The reliance on conventional batteries presents significant constraints, particularly in harsh environments where maintenance and battery replacement are impractical. This limitation restricts the long-term deployment of sensors in remote or extreme conditions, necessitating the exploration of alternative power sources [203].
• Power Management Strategies: Effective energy management is fundamental to extending the operational lifespan of sensor networks. Advanced techniques, including power scheduling, optimized routing protocols, and clustering algorithms, are essential in mitigating excessive energy consumption while maintaining sensor functionality [202].
• Increased Power Consumption Due to Security Measures: The integration of security protocols into sensor systems often results in increased power consumption. This is particularly problematic for resource-constrained devices, where frequent cryptographic operations can rapidly deplete battery life, compromising long-term usability [191].
• Dimensional and Energy Constraints: The miniaturization of sensor components presents an additional challenge, as compact designs must accommodate stringent energy requirements. Microsystems, particularly in biomedical applications, must balance size constraints with the need for efficient power utilization. Moreover, identifying biocompatible and reliable energy sources remains a significant hurdle, especially for long-term applications such as implantable medical devices [193,194]. These issues are addressed to enhance the energy efficiency and sustainability of sensor networks, and they are shown in Figure 10.

Addressing these challenges requires continuous innovation in energy harvesting techniques, low-power circuit design, and adaptive power management solutions. Extending the battery life of sensor technologies without sacrificing performance remains a critical research priority, with significant implications for fields such as environmental monitoring, healthcare, and industrial automation.

7.9. Environmental Challenges

One of the primary concerns in sensor deployment is environmental interference, which can introduce significant measurement biases. In particular, the performance of sensors is significantly influenced by environmental factors, necessitating a thorough understanding of these challenges to develop effective mitigation strategies. To this end, various scientific studies have highlighted the extent to which external conditions impact sensor accuracy and reliability, emphasizing the need for robust design and advanced technological interventions.

For instance, high humidity levels, temperature fluctuations, chemical contaminants, and other external factors can distort sensor responses, requiring sophisticated compensation techniques to ensure accurate readings. In applications such as food monitoring, where sensors must maintain direct contact with perishable items, sustainability and scalability become critical considerations. Ensuring that sensors uphold both quality and safety standards in such conditions remains a formidable challenge [204]. In addition, extreme environmental conditions, including dust, moisture, vibrations, and fluctuating temperatures, can degrade sensor performance over time. Consequently, designing sensors with high durability and precision is essential to guarantee their longevity and effectiveness in such demanding environments.

To address these issues, recent technological advancements have introduced several solutions that enhance sensor resilience and performance. Innovations, sensors, and advanced calibration methodologies offer promising avenues for improving environmental monitoring systems. Furthermore, the development of highly robust sensors that are capable of operating in extreme conditions without compromising accuracy or reliability is necessary. The integration of these advanced materials and calibration techniques is instrumental in overcoming environmental limitations, ensuring that sensors remain reliable across diverse applications.

7.10. Complexity Related Issues

The development and implementation of sensor systems present a multifaceted challenge that encompasses a broad spectrum of interrelated factors, primarily necessitating advanced technical expertise and specialized knowledge. Effective deployment of sensor technology requires proficiency in various aspects, including sensor handling, installation, maintenance, and accurate data interpretation [205]. For instance, in the case of electrochemical sensors used in environmental and medical applications, achieving high sensitivity and selectivity remains a significant challenge due to inherent technological and physical limitations [18]. Moreover, ensuring long-term sensor stability is crucial, as material degradation and environmental exposure can impact performance over time. Addressing these issues demands extensive experience and a deep understanding of sensor dynamics [206].

The complexity associated with sensor implementation and training serves as a fundamental barrier to their widespread and effective use across diverse applications, including environmental monitoring, healthcare, and food quality assessment. The need for specialized training and continuous advancements in sensor technology underscores the urgency of investing in research to mitigate these challenges. Furthermore, key considerations such as stability, reproducibility, and selectivity remain critical in sensor technology to ensure reliability across varying environmental conditions. Overcoming these limitations requires interdisciplinary collaboration, sustained innovation, and the integration of emerging technologies to enhance sensor performance and broaden their applicability in real-world scenarios.

7.11. Economic Impact of Sensor Technologies

While smart sensors play a crucial role in reducing human effort and enhancing automation, they necessitate regular maintenance and operational oversight, leading to ongoing financial commitments [185]. One of the primary concerns is the cumulative cost associated with hardware acquisition, software deployment, and the long-term operation of these systems. In addition, expenditures related to energy consumption, system upkeep, service subscriptions, and the skilled workforce required for managing integrated devices and software contribute significantly to the financial burden [207]. Scalability presents another critical challenge, as an increase in the number of sensors proportionally elevates overall costs [208]. These devices demand continuous maintenance, periodic upgrades, and eventual replacements, which further escalates the total cost of ownership, posing a significant constraint for individuals and institutions with limited financial resources [209]. Moreover, uncertainty regarding the return on investment in relation to implementation costs exacerbates concerns, making financial feasibility one of the key barriers to the widespread adoption of IoT-based sensor technologies [210]. The substantial upfront investment required for high-quality sensor deployment often deters large-scale adoption, particularly in industries such as construction, where cost efficiency is a major consideration. To facilitate broader implementation and long-term sustainability of IoT-integrated sensors, it is imperative to address these cost-related challenges with strategic planning and innovative financial models.

7.12. Data Processing in Sensor-Based IoT Systems

Processing large-scale sensor data demands substantial computational power and extensive storage capacity. The development and deployment of sensor technologies are significantly influenced by various resource constraints, which are energy availability, memory capacity, and processing power [211]. A prime example is the reliance of sensors on battery power, which is inherently limited. As the volume of sensor-generated data continues to grow, conventional data processing methods become inadequate. This necessitates the adoption of advanced data management frameworks alongside the design of energy-efficient communication protocols. This is performed to prolong network life span and optimize sensor performance [212]. Furthermore, handling large-scale sensor data introduces additional complexities in data analysis and interpretation. To address these challenges, it is necessary to utilize cutting-edge technologies, such as cloud computing and machine learning algorithms. These technologies enhance the efficiency of data processing and improve analytical accuracy [185]. These technological advancements highlight the increasing demand for sophisticated data management systems within sensor networks. Such systems need to efficiently handle massive data streams as well as maintain high performance and precision [211].

Consequently, the networks of IoT-integrated sensors require complex data processing mechanisms, which are specifically designed to meet the performance demands of various devices and applications. In this context, advanced computational models, including edge computing and cloud-based processing, have been proposed to mitigate big data challenges by distributing computational loads more efficiently. However, further research is essential to optimize data management techniques as well as ensure seamless data processing across diverse applications of sensor-based IoT.

Moreover, sensor nodes within these systems are often constrained by limited storage and processing capabilities. Therefore, efficient resource management is essential to maintain the functionality and reliability of IoT networks. To optimize resource allocation, minimize energy consumption, and enhance computational efficiency, the investigation of innovative strategies is still a crucial area of research. This is essential to advance the scalability and sustainability of sensor-driven IoT systems.

8. Conclusions

This paper presents an extensive review of sensor technologies, which provides a detailed analysis of recent advancements as well as the current state of research in IoT-based sensor systems. A particular focus was placed on the fundamental architecture of sensors, highlighting their critical role in modern technological applications. Furthermore, this review paper underscored the necessity of integrating IoT-enabled sensors across various domains, such as industrial applications, exploring different types of monitoring and tracking systems currently in use. Furthermore, the paper outlined the core advantages of sensors and presented an in-depth explanation of their fundamental classifications. Beyond reviewing existing developments, it identifies key open research challenges and proposes future directions for further exploration, offering valuable insights for both academic researchers and industry professionals. Ultimately, this paper served as an essential resource for understanding sensor technologies and their potential to enhance IoT-driven solutions, benefiting both scholarly research and commercial innovation.