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Artificial intelligence (AI) is transforming electrochemical biosensing systems, offering novel solutions for foodborne pathogen detection. This review examines the integration of AI technologies, particularly machine learning and deep learning algorithms, in enhancing sensor design, material optimization, and signal processing for detecting key pathogens such as Escherichia coli, Salmonella, and Staphylococcus aureus. Key advancements include improved sensitivity, multiplexed detection, and adaptability to complex environments. The application of AI to the design of recognition molecules (e.g., enzymes, antibodies, aptamers), as well as to electrochemical parameter tuning and multicomponent signal analysis, is systematically reviewed. Additionally, the convergence of AI with the Internet of Things (IoT) is discussed as a pathway to portable, real-time detection platforms. The review highlights the pivotal role of AI across multiple layers of biosensor development, emphasizing the opportunities and challenges that arise from interdisciplinary integration and the practical deployment of IoT-enabled technologies in electrochemical sensing systems. Despite significant progress, challenges remain in data quality, model generalization, and interpretability. The review concludes by outlining future research directions for building robust, intelligent biosensing systems capable of supporting scalable food safety monitoring. Full article

Air pollution threatens human and environmental health worldwide. A Harvard study in partnership with UK institutions found that fossil fuel pollution killed over 8 million people in 2018, accounting for 1 in 5 fatalities worldwide. Iraq, including the Kurdistan Region of Iraq, has a major environmental issue in that it ranks second worst in 2022 due to the high level of particulate matter, specifically PM_2.5. In this paper, a LoRa-based infrastructure for environmental monitoring in the Kurdistan Region has been designed and developed. The infrastructure encompasses end-node devices, an open-source network server, and an IoT platform. Two AirQNodes were prototyped and deployed to measure particulate matter values, temperature, humidity, and atmospheric pressure using manufacturer-calibrated PM sensors and combined temperature, humidity, and atmospheric sensors. An open-source network server is adopted to manage the AirQNodes and the entire network. In addition, an IoT platform has also been designed and implemented to visualize and analyze the collected data. The platform processes and stores the data, making it accessible for the public and decision-making parties. The infrastructure was tested and results validated by deploying two AirQNodes at separate locations adjacent to existing air quality monitoring stations as reference points. The findings demonstrated that the AirQNodes reliably mirrored the trends and patterns observed in the reference monitors. This research establishes a comprehensive infrastructure for monitoring air quality in the Kurdistan Region of Iraq. Furthermore, complete ownership of the system can be attained by possessing and overseeing the critical components of the infrastructure, which encompass the end devices, network server, and IoT platform. This integrated strategy is especially crucial for the Kurdistan Region of Iraq, where cost-efficiency and enduring sustainability are vital, yet such a structure is absent. Full article

Hydroponic agriculture based on the Internet of Things (IoT) is an innovative solution to face the challenges of land limitations and climate uncertainty. This study aims to analyze the role of IoT in accelerating the growth of hydroponic plants through monitoring and automation of the planting environment, as well as evaluating its impact on productivity, especially for the planting process in land with minimal sunlight. The system integrates sensors to monitor environmental parameters such as pH, temperature, and humidity, which are then processed in real-time to optimize nutrient delivery and irrigation. The results show that the use of IoT in hydroponic systems is able to significantly improve the quality and quantity of crop yields compared to conventional methods. However, there are several challenges in implementation, such as high initial costs, limited infrastructure in certain areas, and potential cybersecurity threats. Nonetheless, innovation and collaboration opportunities between the public and private sectors can accelerate the adoption of these technologies in sustainable agriculture. Full article

Water quality monitoring is an important step in maintaining environmental sustainability and public health. Water turbidity is one of the main parameters in assessing water quality, because a high level of turbidity can indicate pollution that is harmful to aquatic ecosystems and humans. In the digital era, Internet of Things (IoT) technology has been applied to improve the effectiveness of real-time monitoring of water turbidity. This study aims to examine IoT-based water turbidity monitoring strategies and technologies using the Systematic Literature Review (SLR) method with the PRISMA protocol. In the process of searching for literature, this study identified 222 articles from the Scopus database, which, after going through the screening stage based on relevance, document type, and accessibility, resulted in seven main articles for further analysis. The results of the review show that the utilization of IoT sensors and wireless communication enables real-time monitoring of water turbidity, improves early detection of pollution, and improves effectiveness in water monitoring. However, challenges such as data security, sensor reliability, and communication network stability still need to be overcome to ensure the system works optimally. This study confirms that IoT can be a more efficient and sustainable solution in monitoring water turbidity. Full article

Buildings are among the most significant sources of energy consumption worldwide. Unfortunately, many are inefficient in terms of energy use, leading to high operational expenses. With modern technologies such as IoT sensors, smart meters, secure real-time communication, and advanced mathematical algorithms for data processing integrated into an efficient energy management platform, traditional buildings can be transformed into smart structures. In this context, a platform called “Building Energy Efficiency in Totality” (BENEFIT), which incorporates the smart building energy management (SBEM) concept, has been designed, developed, integrated, and tested as an innovative tool for monitoring and optimally controlling energy consumption. The platform is based on open-source software, enabling rapid and straightforward development of comprehensive solutions that address all aspects of the SBEM concept. The BENEFIT architecture allows the management of a wide range of devices within the building, including energy generation units, heating, ventilation, and air conditioning systems, indoor lighting, environmental sensors, surveillance cameras, and others. BENEFIT has been implemented and tested in a building belonging to the Faculty of Electrical Engineering at the Technical University of Iasi, Romania. The analysis of the results after one year of integrating the BENEFIT platform has resulted in a plan focused on measures to reduce energy consumption and improve the building’s performance and efficiency. The implementation of two measures (upgrading window insulation and improving lighting) resulted in a 12.14% reduction in total energy consumption. Full article

Predictive maintenance is a crucial component of smart manufacturing in Industry 4.0, utilizing data from IoT sensor networks and machine learning algorithms to predict equipment failures before they happen. This proactive approach enables timely maintenance of equipment and machinery, reducing unplanned downtime, extending equipment lifespan, and enhancing overall system reliability, ultimately leading to more efficient and cost-effective operations. Conventional machinery and equipment maintenance approaches often rely on periodic manual inspections, human observations, and monitoring, which can be time-consuming, inefficient, and resource-intensive. Therefore, implementing automation through predictive models based on IoT and machine learning techniques is crucial for optimizing the maintenance of machinery and equipment. This paper aims to leverage machine learning techniques to develop predictive maintenance models, including electric motor temperature and vibration prediction, using data from established sensor networks and production data from ERP systems. The models are designed to predict potential issues within the next ten minutes, such as whether temperature or vibration levels will exceed predefined thresholds. Full article

The increasing demand for decentralized and user-controlled cryptographic key management in blockchain ecosystems has created interest in alternative entropy sources that do not rely on dedicated hardware. This study investigates whether commercial smartwatches can generate sufficient entropy for secure local key generation by utilizing their onboard sensors. An open-source Wear OS application was developed to harvest sensor data in two acquisition modes: still mode, where the device remains stationary, and shake mode, where data collection is triggered by motion events exceeding a predefined acceleration threshold. A total of 4800 still-mode and 4800 shake-mode samples were collected, each producing 11,400 bits of sensor-generated data. Entropy was evaluated using statistical metrics commonly employed in entropy analysis, including Shannon entropy, min-entropy, Markov dependency analysis, and compression-based redundancy estimation. The shake mode achieved Shannon entropy of 0.997 and min-entropy of 0.918, outperforming the still mode (0.991 and 0.851, respectively) and approaching the entropy levels of software-based random number generators. These results demonstrate that smartwatches can act as practical entropy sources for cryptographic applications, provided that appropriate post-processing, such as cryptographic hashing, is applied. The method offers a low-cost, transparent, and user-friendly alternative to specialized hardware wallets, aligning with the principles of decentralization and self-sovereign identity. Full article

This paper examines some of the most popular protocols for transmitting sensor data to cloud structures from publish/subscribe and request/response IoT models. The selection of a highly efficient message transmission protocol is essential, as it depends on the specific characteristics and purpose of the developed IoT system, which includes communication requirements, message size and format, energy efficiency, reliability, and cloud specifications. No standardized protocol can cover all the diverse application scenarios; therefore, for each developed project, the most appropriate protocol must be selected that meets the project’s specific requirements. This work focuses on finding the most appropriate protocol for integrating sensor data into a suitable open-source IoT platform, ThingsBoard. First, we conduct a comparative analysis of the studied protocols. Then, we propose a project that represents an experiment for transmitting data from a stationary XBee sensor network to the ThingsBoard cloud via HTTP, MQTT-SN, and CoAP protocols. We observe the parameters’ influence on the delayed transmission of packets and their load on the CPU and RAM. The results of the experimental studies for stationary sensor networks collecting environmental data give an advantage to the MQTT-SN protocol. This protocol is preferable to the other two protocols due to the lower delay and load on the processor and memory, which leads to higher energy efficiency and longer life of the sensors and sensor networks. These results can help users make operational judgments for their IoT applications. Full article

A smart irrigation system based on soil moisture sensors supported by photovoltaic energy is an innovation to address water use efficiency in the agricultural sector, especially in remote areas. This technology utilizes photovoltaic panels as a renewable energy source to operate water pumps, while soil moisture sensors provide real-time data that is used to automatically manage irrigation according to plant needs. This technology not only increases the efficiency of water and energy use but also supports environmental conservation by reducing dependence on fossil fuels. This research was conducted using a Systematic Literature Review (SLR) approach guided by the PRISMA framework to analyze trends, benefits, and challenges in implementing this technology. The analysis results show that this system offers various advantages, including energy efficiency, reduced carbon emissions, and ease of management through the integration of Internet of Things (IoT) technology. Several challenges remain, such as high initial investment costs, limited network access, and obstacles. Technical matters related to installation and maintenance. Various solutions have been proposed, including providing subsidies for small farmers, implementing radiofrequency modules, and using modular designs to simplify implementation. This study contributes to the development of a conceptual framework that can be adapted to various geographic and socio-economic conditions. Potential further developments include the integration of artificial intelligence and additional sensors to increase efficiency and support the sustainability of the agricultural sector globally. Full article

Device-free localization and tracking (DFLT) has emerged as a promising technique for location-aware Internet-of-Things (IoT) applications. However, most existing DFLT systems based on narrowband sensing networks suffer from reduced accuracy in indoor environments due to the susceptibility of received signal strength (RSS) measurements to multipath interference. In this paper, we propose a real-time DFLT system leveraging ultra-wideband (UWB) sensors. The system estimates target-induced shadowing using two UWB RSS measurements, which are shown to be more resilient to multipath effects compared to their narrowband counterparts. To enable real-time tracking, we further design an efficient measurement protocol tailored for UWB networks. Field experiments conducted in both indoor and outdoor environments demonstrate that our UWB-based system significantly outperforms its traditional narrowband DFLT solutions in terms of accuracy and robustness. Full article

Energy harvesting (EH) is a rapidly evolving domain that is primarily focused on capturing and converting ambient energy sources into more convenient and usable forms. These sources, which range from traditional renewable sources such as solar or wind power to thermal gradients and vibrations, present an alternative to typical power generation. The temptation to use energy harvesting systems is in their potential to power low-power devices, such as environment monitoring devices, without relying on conventional power grids or standard battery implementations. This improves the sustainability and self-sufficiency of IoT devices and reduces the environmental impact of conventional power systems. Applications of EH include wearable health monitors, wireless sensor networks, and remote structural sensors, where frequent battery replacement is impractical. However, these systems also face challenges such as intermittent energy availability, limited storage capacity, and low power density, which require innovative design approaches and efficient energy management. The paper provides a general overview of the subsystems present in the energy harvesting systems and a comprehensive overview of the energy transducer technologies used in energy harvesting systems. Full article

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. Full article

It is critical to provide proper environmental conditions in poultry houses to maintain birds’ health, boost productivity, and improve the overall economic viability of the poultry industry. Among the myriad of environmental elements, indoor air quality has been a determining factor that directly affects poultry well-being. Elevated concentrations of harmful gases—in particular Carbon Dioxide ($C{O}_2$), Methane ($C{H}_4$), and Ammonia ($N{H}_3$)—decomposition products of poultry litter, feed wastage, and biological processes have draconian effects on bird health, feed efficiency, the growth rate, reproduction efficiency, and mortality rate. Despite their importance, traditional air quality monitoring systems are often operated manually, labor intensive, and cannot detect sudden environmental changes due to the lack of real-time sensing. To overcome these limitations, this paper presents an interdisciplinary approach combining cloud computing, Artificial Intelligence (AI), and Internet of Things (IoT) technologies to measure real-time poultry gas concentrations. Real-time sensor feeds are transmitted to a cloud-based platform, which stores, displays, and processes the data. Furthermore, a machine learning (ML) model was trained using historical sensory data to predict the next-day gas emission levels. A web-based platform has been developed to enable convenient user interaction and display the gas sensory readings on an interactive dashboard. Also, the developed system triggers automatic alerts when gas levels cross safe environmental thresholds. Experimental results of $C{O}_2$ concentrations showed a significant diurnal trend, peaking in the afternoon, followed by the evening, and reaching their lowest levels in the morning. In particular, $C{O}_2$ concentrations peaked at approximately 570 ppm during the afternoon, a value that was significantly elevated (p < 0.001) compared to those recorded in the evening (~560 ppm) and morning (~555 ppm). This finding indicates a distinct diurnal pattern in $C{O}_2$ accumulation, with peak concentrations occurring during the warmer afternoon hours. Full article

Continuous cardiovascular monitoring is essential for the early detection of cardiac events, but conventional electrode-based ECG systems cause skin irritation and are unsuitable for long-term wear. We propose an electrode-free ECG monitoring approach that leverages synchronized phonocardiogram (PCG) and seismocardiogram (SCG) signals captured by wireless mechano-acoustic sensors. PCG provides precise valvular event timings, while SCG provides mechanical context, enabling the robust identification of systolic/diastolic intervals and pathological patterns. A deep learning model reconstructs ECG waveforms by intelligently combining mechano-acoustic sensor data. Its architecture leverages specialized neural network components to identify and correlate key cardiac signatures from multimodal inputs. Experimental validation on an IoT sensor dataset yields a mean Pearson correlation of 0.96 and an RMSE of 0.49 mV compared to clinical ECGs. By eliminating skin-contact electrodes through PCG–SCG fusion, this system enables robust IoT-compatible daily-life cardiac monitoring. Full article

This study presents a gesture-based authentication system utilizing triboelectric nanogenerator (TENG) sensors. As self-powered devices capable of generating high-voltage outputs without external power, TENG sensors are well-suited for low-power IoT sensors and smart device applications. The proposed system recognizes single tap, double tap, and holding gestures. The electrical characteristics of the sensor were evaluated under varying pressure conditions, confirming a linear relationship between applied force and output voltage. These results demonstrate the sensor’s high sensitivity and precision. A threshold-based classification algorithm was developed by analyzing signal features enabling accurate gesture recognition in real time. To enhance the practicality and scalability of the system, the algorithm was further configured to automatically segment raw sensor signals into gesture intervals and assign corresponding labels. From these segments, time-domain statistical features were extracted to construct a training dataset. A random forest classifier trained on this dataset achieved a high classification accuracy of 98.15% using five-fold cross-validation. The system reduces security risks commonly associated with traditional keypad input, offering a user-friendly and reliable authentication interface. This work confirms the feasibility of TENG-based gesture recognition for smart locks, IoT authentication devices, and wearable electronics, with future improvements expected through AI-based signal processing and multi-sensor integration. Full article