IoT

The rise of wearable devices has enabled real-time processing of sensor data for critical health monitoring applications, such as human activity recognition (HAR) and cardiac disorder classification (CDC). However, the limited computational and memory resources of wearables necessitate lightweight yet accurate classification models. While deep neural networks (DNNs), including convolutional neural networks (CNNs) and long short-term memory networks, have shown high accuracy for HAR and CDC, their large parameter sizes hinder deployment on edge devices. On the other hand, various DNN compression techniques have been proposed, but exploiting the combination of various compression techniques with the aim of achieving memory efficient DNN models for HAR and CDC tasks remains under-investigated. This work studies the impact of CNN architecture parameters, focusing on the convolutional and dense layers, to identify configurations that balance accuracy and efficiency. We derive two versions of each model—lean and fat—based on their memory characteristics. Subsequently, we apply three complementary compression techniques: filter-based pruning, low-rank factorization, and dynamic range quantization. Experiments across three diverse DNNs demonstrate that this multi-faceted compression approach can significantly reduce memory and computational requirements while maintaining validation accuracy, leading to DNN models suitable for intelligent health monitoring on resource-constrained wearable devices. Full article

Recently, electromagnetic wireless power transfer (WPT) has emerged as a promising technology for supplying power to multiple terminals. Previous studies have devised packet transmission methods, commonly used in telecommunication, for power analysis. This study develops a simulator that calculates the received power by integrating a power–packet conversion method, based on previous research. The simulator incorporates several scheduling functions to facilitate the investigation of the efficiency of the power-feeding methods. This study analyzes the efficacy of a first-come–first-served (FCFS) method, a round-robin (RR) method, and a multilevel feedback queue (MFQ) scheme for wireless power transfer, all of which were devised based on existing scheduling methods used in operating systems. Simulation results show that, although the FCFS method is simple, it may lead to battery depletion due to delayed power supply, particularly in terminals with lower initial battery levels. The RR method improves fairness by allocating the power supply in time slices; however, its performance is sensitive to the slice duration. The MFQ method, which incorporates a promotion mechanism based on battery status and power demand, exhibits higher adaptability, achieving efficient and balanced power distribution even when terminals differ in distance from the transmitter or in power consumption. These evaluations were conducted using a proposed power–packet conversion method that discretizes continuous power into packet units, allowing for the application of communication network-inspired scheduling and control techniques. The capacity to construct such models enables the simulator to analyze the flow and distribution of power, predict potential issues that may arise in real systems in advance, and devise optimal control methodologies. Moreover, the model can be employed to enhance the efficiency of power management systems and construct smart grids, and it is anticipated to be utilized for the integration of power and communication systems. Full article

This article highlights the development of a platform for monitoring three-phase energy consumption within a university campus. The core of this platform is low-cost IoT energy sensors, which are designed to transmit real-time data to the data center’s server through different IoT communication technologies, enhancing the preexisting electrical measurement network. The newly recommended measurement structure enables the electrical consumption data collection required for analyzing patterns and proposing forecast models to optimize electricity usage. The major contribution of this work is the design and implementation of smart three-phase energy meters based on the selection of various energy sensors and wireless communication technologies, and then the set up of a global IoT architecture that offers real-time data acquisition, storage, download, and visualization, capitalizing on the campus’s diverse energy profiles for detailed characterization. The proposed platform is considered the cornerstone toward the implementation of a collaborative smart microgrid, allowing forecasting and electrical consumption optimization, enabling research into potential opportunities for energy efficiency in our campus, and enhancing the performance of existing electrical infrastructure. Full article

Recent advancements in large language models (LLMs) have added a transformative dimension to the social Internet of Things (SIoT), which is the combination of social networks and IoT. With LLMs’ natural language understanding and data synthesis capabilities, LLMs are regarded as strong tools to enhance SIoT applications such as recommendation, search, and data management. This application-focused review synthesizes the latest related research by identifying both the synergies and the current research gaps at the intersection of LLMs and SIoT, as well as the evolutionary road from machine learning-based solutions to LLM-enhanced ones. Full article

In this study, a Multi-Sensor IoT Device (MSID) is developed that is designed to collect various environmental data and interconnect with the cloud and blockchain to ensure reliable data management. The MSID is designed with a flexible, modular structure that supports a variety of sensor configurations and is easily expandable with 3D-printed components. The system performance was monitored in real-time, with a high cloud upload success rate of 98.35% and an average transmission delay of only 0.64 s, confirming stable data collection every minute. Blockchain-based sensor data storage ensured data integrity and tamper-proofness, with all transactions successfully recorded and verified via smart contract. The proposed Blockchain-based Mobile IoT System (BMIS) has shown strong potential for use in environmental monitoring, industrial asset management, and other areas that require reliable data collection and long-term preservation. Full article

Integrating smart devices from the Internet of Things (IoT) with Customer Relationship Management (CRM) systems presents significant opportunities for enhancing customer experience through real-time data utilization. This article explores the technological frameworks and practical solutions for achieving seamless integration of IoT data within CRM platforms. By leveraging distributed Web systems, this study demonstrates how companies can improve scalability, responsiveness, and personalization in managing customer relationships. This paper outlines key architectural designs for distributed Web systems that ensure efficient real-time data processing while addressing challenges such as security, system integration, and the demands of analytics. This research provides insights into overcoming these challenges with strategies like load balancing, edge processing, and advanced encryption protocols. Results from simulations and practical implementations underscore the effectiveness of these approaches in optimizing operational efficiency and delivering hyper-personalized customer experiences. This study aims to bridge the gap between theoretical possibilities and real-world applications, offering actionable guidelines for organizations to fully leverage IoT-driven CRM systems. Full article

The rapid proliferation of Internet of Things (IoT) devices in healthcare, from wearable sensors to implantable medical devices, has revolutionised patient monitoring, personalised treatment, and remote care delivery. However, the resource-constrained nature of IoT devices, coupled with the sensitivity of medical data, presents critical security challenges. Traditional encryption methods, while robust, are computationally intensive and unsuitable for IoT environments, leaving sensitive patient information vulnerable to cyber threats. Addressing this gap, lightweight encryption methods have emerged as a pivotal solution to balance security with the limited processing power, memory, and energy resources of IoT devices. This paper explores lightweight encryption methods tailored for IoT healthcare applications, evaluating their effectiveness in securing sensitive data while operating under resource constraints. A comparative analysis is conducted on encryption techniques such as AES-128, LEA, Ascon, GIFT, HIGHT, PRINCE, and RC5-32/12/16, based on key performance metrics including block size, key size, encryption and decryption speeds, throughput, and security levels. The findings highlight that AES-128, LEA, ASCON, and GIFT are best suited for high-sensitivity healthcare data due to their strong security features, while HIGHT and PRINCE provide balanced protection for medium-sensitivity applications. RC5-32/12/16, on the other hand, prioritises efficiency over comprehensive security, making it suitable for low-risk scenarios where computational overhead must be minimised. The paper underscores the significant trade-offs between efficiency, security, and resource consumption, emphasising the need for careful selection of encryption methods based on the specific requirements of IoT healthcare environments. Additionally, the paper highlights the growing demand for lightweight encryption methods that balance energy efficiency with robust protection against cyber threats. These insights offer valuable guidance for researchers and practitioners seeking to enhance the security of IoT-based healthcare systems while ensuring optimal performance in resource-constrained settings. Full article

The Manufacturing Message Specification (MMS) protocol is frequently used to automate processes in IEC 61850-based substations and smart-grid systems. However, it may be susceptible to a variety of cyber-attacks. A frequently used protection strategy is to deploy intrusion detection systems to monitor network traffic for anomalies. Conventional approaches to detecting anomalies require a large number of labeled samples and are therefore incompatible with high-dimensional time series data. This work proposes an anomaly detection method for high-dimensional sequences based on a bidirectional LSTM autoencoder. Additionally, a text-mining strategy based on a TF-IDF vectorizer and truncated SVD is presented for data preparation and feature extraction. The proposed data representation approach outperformed word embeddings (Doc2Vec) by better preserving critical domain-specific keywords in MMS traffic while reducing the complexity of model training. Unlike embeddings, which attempt to capture semantic relationships that may not exist in structured network protocols, TF-IDF focuses on token frequency and importance, making it more suitable for anomaly detection in MMS communications. To address the limitations of existing approaches that rely on labeled samples, the proposed model learns the properties and patterns of a large number of normal samples in an unsupervised manner. The results demonstrate that the proposed approach can learn potential features from high-dimensional time series data while maintaining a high True Positive Rate. Full article

The objective of this study is to conduct an analysis of the scientific literature on the application of the Internet of Things (IoT) and artificial intelligence (AI) in enhancing supply chain operations. This research applies a dual approach combining bibliometric analysis and topic modeling to explore both quantitative citation trends and qualitative thematic insights. By examining 810 qualified articles, published between 2011 and 2024, this research aims to identify the main topics, key authors, influential sources, and the most-cited articles within the literature. The study addresses critical research questions on the state of IoT and AI integration into supply chains and the role of these technologies in resolving digital supply chain management challenges. The convergence of IoT and AI holds immense potential to redefine supply chain management practices, improving productivity, visibility, and sustainability in interconnected global supply chains. This research not only highlights the continuous evolution of the supply chain field in light of Industry 4.0 technologies—such as machine learning, big data analytics, cloud computing, cyber–physical systems, and 5G networks—but also provides an updated overview of advanced IoT and AI technologies currently applied in supply chain operations, documenting their evolution from rudimentary stages to their current state of advancement. Full article

The use of digital technology resources in public services enhances efficiency, responsiveness, and citizens’ quality of life through improved resource management, real-time monitoring, and service performance. The objective is to create and apply an IoT-based framework for connected municipal public services in a strategic digital city context. The research employed a modeling process validated in a Brazilian city, identifying seven related frameworks and four themes through a bibliometric review. The original framework comprises three constructs, eight subconstructs, and 12 variables, validated through a case study inquiry. The results revealed that the researched city has yet to enlarge IoT into its municipal public services as part of a digital city project initiative. Key recommendations for IoT implementation include prioritizing the preferences of digital citizens, expanding critical services suited for IoT, and updating municipal strategies to incorporate IT resources to streamline decision-making. The conclusion reiterates that the IoT framework for municipal services is effective when actionable information supports strategic planning and decision-making and highlights the transformative potential of IoT in driving more resilient and sustainable cities aligned with citizens’ needs. This approach allows public managers to enhance citizens’ quality of life while improving the efficiency and responsiveness of urban management processes and services. Full article

There are over 300 million smart homes worldwide and 60.4 million smart homes in the US, using devices like smart thermostats, smart plugs, smart door locks, etc. Yet in this age of smart and connected devices, we still use paper-based sticky notes on doors to display messages such as “Busy, do not disturb”, “In a Zoom meeting”, etc. In this project, a novel IoT-connected digital sticky note system was developed where the user can wirelessly send messages from a smartphone to a sticky note display. The sticky note displays can be hung on the doors of offices, hotels, homes, etc. The display could be updated with the user’s message sent from anywhere in the world. The key design challenge was to develop the display unit to consume as little power as possible to increase battery life. A prototype of the proposed system was developed comprising ultra-low-power sticky note display units consuming only 404 µA average current and having a battery life of more than six months, with a Wi-Fi-connected hub unit, an MQTT server, and a smartphone app for composing the message. Full article

The proliferation of Internet of Things (IoT) devices presents significant challenges for cybersecurity and digital forensics, particularly as these devices have become increasingly weaponised for malicious activities. This research focuses on the forensic analysis capabilities of Raspberry Pi devices configured with Kali Linux, comparing their forensic capabilities to conventional PC-based forensic investigations. The study identifies key gaps in existing IoT forensic methodologies, including limited tool compatibility, constrained data retention, and difficulties in live memory analysis due to architectural differences. The research employs a testbed-based approach to simulate cyberattacks on both platforms, capturing and analysing forensic artefacts such as system logs, memory dumps, and network traffic. The research findings reveal that while traditional PCs offer extensive forensic capabilities due to superior storage, tool support, and system logging, Raspberry Pi devices present significant forensic challenges, primarily due to their ARM architecture and limited forensic readiness. The study emphasises the need for specialised forensic tools tailored to IoT environments and suggests best practices to enhance forensic investigation capabilities in weaponised IoT scenarios. This research contributes to the field by bridging the gap between theoretical frameworks and real-world forensic investigations, offering insights into the evolving landscape of IoT forensics and its implications for digital evidence collection, analysis, and forensic readiness. Full article

With the rapid increase in the human population and urbanization worldwide, the demand for food production has played a significant role in driving the integration of technology into agriculture. Various Cloud-based systems, such as livestock tracking systems, have been proposed. In those systems, data were collected by the sensors and sent to the Cloud for processing. However, significant issues with those systems were noted, such as high bandwidth utilization and security concerns, such as a high volume of row data traveling from the data collection devices (such as sensors) to the Cloud through the Internet. Additionally, the long distance between the Cloud and the data collection devices makes it unsuitable for latency-sensitive livestock disease monitoring and tracking systems. Therefore, this paper proposes a Fog–Cloud-based approach, where the processing is conducted at the Fog layer, closer to the data collection devices, and only the result is sent to the Cloud for remote viewing. The proposed method aims to reduce power consumption and latency in communication. To validate the proposed method, both the Cloud-based and Fog–Cloud-based scenarios are simulated using iFogSim (a novel simulation tool for IoT and Cloud computing), and the result shows that there is less than twice the power consumption in some scenarios and that the time consumed in the proposed Fog–Cloud-based system, depending on the number of sensors, is five to ten times lower. This study further supports the point that the Fog–Cloud-based is suitable for latency-dependent farming systems such as livestock tracking systems. Full article

Date palms are a vital resource in the oases of Morocco, providing economic and ecological benefits to local communities. However, monitoring these trees at scale presents challenges, particularly in rural areas where traditional IoT-based agricultural systems rely on extensive sensor networks. These systems face significant limitations, including high deployment costs, network coverage issues, and energy constraints. To address these challenges, we propose an agricultural robot equipped with multiple IoT sensors to monitor date palm health and environmental parameters, reducing the need for widespread fixed sensors. To overcome the latency, bandwidth, and energy inefficiencies associated with cloud-based solutions, the system integrates the fog computing paradigm. Fog computing brings computational resources closer to the data source, enabling real-time processing and decision-making while reducing network congestion. Using iFogSim, we evaluate the proposed system on key metrics, including latency, energy consumption, and network performance. The results demonstrate significant improvements over traditional approaches, highlighting the potential of IoT-based robotic systems combined with fog computing to revolutionize date palm monitoring in remote oases. Full article

Patient safety (PS) is essential in medical care, and preventing medication errors (MEs) is key to guaranteeing it. In Colombia, pharmaceutical services must comply with regulations that require adequate environmental monitoring to ensure medication quality. This study aims to propose an IoT-based smart system that automatizes temperature and relative humidity monitoring in the Colombian pharmaceutical service (CPS). Using the model for IoT platform design as a methodology, an efficient and flexible architecture that integrates data quality management (DQM) dimensions to improve the accuracy and reliability of the system was designed. In addition, tests based on the agile quadrant methodology demonstrate, as a result, its effectiveness, highlighting its ability to optimize environmental monitoring, prevent MEs, and improve PS. The successful implementation of this IoT-based smart system shows its potential in the pharmaceutical sector, offering an innovative solution that reduces risks and improves the quality of drug storage. Full article

The development of technologies using the Internet of Things (IoT) concept evolves daily. These numerous technologies, such as LoRa (Long Range) transceivers, find applications in various domains, including monitoring natural disasters and those caused by human error. Security vulnerabilities arise concurrently with the advancement of these new technologies. Cyberattacks seeking to disrupt device availability, such as denial-of-service (DoS) attacks, can effectively exploit vulnerabilities in LoRa devices, hindering disaster monitoring efforts. Therefore, our goal is to assess the network parameters that impact the development of a disaster monitoring environment using LoRaWAN. Specifically, we aim to identify the parameters that could result in network availability issues, whether caused by malicious actors or configuration errors. Our results indicate that certain LoRa network parameters (collision checks, packet size, and the number of nodes) can significantly affect network performance, potentially rendering this technology unsuitable for building robust disaster monitoring systems. Full article

Advances in deep learning (DL) models and next-generation edge devices enable real-time image classification, driving a transition from the traditional, purely cloud-centric IoT approach to edge-based AIoT, with cloud resources reserved for long-term data storage and in-depth analysis. This innovation is transformative for agriculture, enabling autonomous monitoring, localized decision making, early emergency detection, and precise chemical application, thereby reducing costs and minimizing environmental and health impacts. The workflow of an edge-based AIoT system for agricultural monitoring involves two main steps: optimal training and tuning of DL models through extensive experiments on high-performance AI-specialized computers, followed by effective customization for deployment on advanced edge devices. This review highlights key challenges in practical applications, including: (i) the limited availability of agricultural data, particularly due to seasonality, addressed through public datasets and synthetic image generation; (ii) the selection of state-of-the-art computer vision algorithms that balance high accuracy with compatibility for resource-constrained devices; (iii) the deployment of models through algorithm optimization and integration of next-generation hardware accelerators for DL inference; and (iv) recent advancements in AI models for image classification that, while not yet fully deployable, offer promising near-term improvements in performance and functionality. Full article

IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN) are specifically designed for applications that require lower data rates and reduced power consumption in wireless internet connectivity. In the context of 6LoWPAN, Internet of Things (IoT) devices with limited resources can now seamlessly connect to the network using IPv6. This study focuses on examining the performance and power consumption of routing protocols in the context of 6LoWPAN, drawing insights from prior research and utilizing simulation techniques. The simulation involves the application of routing protocols, namely Routing Protocol for Low-power and Lossy (RPL) Networks, Ad hoc On-demand Distance Vector (AODV), Lightweight On-demand Ad hoc Distance-vector Next Generation (LOADng), implemented through the Cooja simulator. The simulation also runs in different network topologies to gain an insight into the performance of the protocols in the specific topology including random, linear, and eclipse topology. The raw data gathered from the tools including Powertrace and Collect-View were then analyzed with Python code to transfer into useful information and visualize the graph. The results demonstrate that the power consumption, specifically CPU power, Listen Power, and Total Consumption Power, will increase with the incremental of motes. The result also shows that RPL is the most power-efficient protocol among the scenarios compared to LOADng and AODV. The result is helpful because it brings insights into the performance, specifically power consumption in the 6LoWPAN network. This result is valuable to further implement these protocols in the testbed as well as provide an idea of the algorithmic enhancements. Full article

The electric shower is the main form of heating water for bathing in Brazilian homes and one of the significant appliances related to electricity and water consumption. Internet of Things (IoT) projects make it possible to connect objects to the Internet and collect data from machines remotely. In this work, we developed an Internet of Things system for monitoring an electric shower, called Shower–IoT, whose sensor data are water temperature, electric tension, electric current, and water flow. To implement the software infrastructure, we used the services present in cloud computing, such as a broker, processing, and storage, in which the information about the electric shower was made available through an Android application. The results demonstrate that our system can monitor an electric shower integrated with cloud services, allowing the users to visualize its behavior in real time and detect possible failures by comparing sensor data from previous evaluations. Full article