Internet Of Things Applications Research Articles

Performance evaluation framework of MQTT client libraries for IoT applications in manufacturing

The Internet of Things (IoT) is a revolutionary technology in the field of manufacturing that enables real-time data communication and control of various industrial sensors and devices. MQTT (Message Queuing Telemetry Transport) is one of the most popular communication protocols for IoT applications which offers lightweight, efficient, and reliable communication. There are several MQTT client libraries available for implementing MQTT communication for IoT applications. The selection of an appropriate client library can significantly impact the performance of an IoT application. This paper presents a performance evaluation framework for MQTT client libraries for IoT applications in the manufacturing industry. This paper aims to create a standardized virtual testing environment to compare the performance of different MQTT client libraries using various test conditions. It offers a structured and standardized methodology for evaluating performance aspects such as latency, scalability, and resource utilization. By systematically evaluating the performance of MQTT client libraries against these metrics, users can make informed decisions while selecting the most appropriate library for their IoT applications. The framework takes into consideration the unique requirements and constraints of manufacturing applications, such as the need for low-latency communication, secure data exchange, and compatibility with resource-constrained devices. Ultimately, this research contributes to the optimization of IoT applications in the manufacturing industry, facilitating the implementation of efficient, reliable, and scalable data communication.

Self-Powered Traffic Lights Through Wind Energy Harvesting Based on High-Performance Fur-Brush Dish Triboelectric Nanogenerators.

Traffic lights play vital roles in urban traffic management systems, providing clear directional guidance for vehicles and pedestrians while ensuring traffic safety. However, the vast quantity of traffic lights widely distributed in the transportation system aggravates energy consumption. Here, a self-powered traffic light system is proposed through wind energy harvesting based on a high-performance fur-brush dish triboelectric nanogenerator (FD-TENG). The FD-TENG harvests wind energy to power the traffic light system continuously without needing an external power supply. Natural rabbit furs are applied to dish structures, due to their outstanding characteristics of shallow wear, high performance, and resistance to humidity. Also, the grid pattern of the dish structure significantly impacts the TENG outputs. Additionally, the internal electric field and the influences of mechanical and structural parameters on the outputs are analyzed by finite element simulations. After optimization, the FD-TENG can achieve a peak power density of 3.275Wm-3. The portable and miniature features of FD-TENG make it suitable for other natural environment systems such as forests, oceans, and mountains, besides the traffic light systems. This study presents a viable strategy for self-powered traffic lights, establishing a basis for efficient environmental energy harvesting toward big data and Internet of Things applications.

Optimizing Catfish Farming Through IoT-Driven Water Quality Management

General Background: Catfish play a significant role in the global fishing industry, necessitating effective farming practices to enhance productivity and sustainability. Specific Background: However, challenges such as maintaining optimal water quality in aquaculture ponds, particularly concerning ammonia levels and temperature fluctuations, pose threats to catfish health and production efficiency. Knowledge Gap: Previous research has often focused on single-parameter monitoring, lacking comprehensive systems that integrate real-time data for both ammonia and temperature management. Aims: This study aims to develop an advanced Internet of Things (IoT) system for real-time monitoring and control of ammonia levels and water temperature in catfish ponds, facilitating optimal farming conditions. Results: The implemented system utilizes two sensors to measure ammonia and temperature, integrated with the Blynk IoT platform for remote monitoring. The findings indicate that when water temperature exceeds 26 °C, the water pump activates to regulate temperature, while ammonia levels above 5 NH3 trigger the aerator to enhance oxygen levels and reduce ammonia toxicity. Novelty: This research innovatively combines multiple sensors and modern IoT applications, offering a more robust solution for aquaculture management compared to previous studies. Implications: The monitoring system effectively maintains water quality, enhancing catfish farming sustainability and efficiency, emphasizing the need for technology integration in aquaculture to effectively tackle environmental challenges. Highlights: Real-time Monitoring: Continuous tracking of ammonia and temperature levels. Automated Control: Activates pumps and aerators based on set thresholds. Enhanced Sustainability: Supports healthier catfish and optimizes production efficiency. Keywords: Catfish Farming, IoT, Water Quality Monitoring, Ammonia Control, Temperature Management

Energy Conscious Squirrel Search based Task Scheduling Mechanism in Fog Environment

ABSTRACTThe swift explosion of Internet of Things (IoT) devices in the past few years has resulted in a deluge of data that requires timely processing. However, transmitting such massive amounts of data in and out from cloud leads to high latency and network bandwidth. Consequently, the field of fog computing has turned out as a feasible platform for utilizing resources within the vicinity of IoT devices for providing services in a timely manner. The resource constrained trait of fog nodes demands judicious management of resources in satisfying the requirements of IoT applications. This makes energy efficiency a core issue in fog networks. This article addresses the issue of energy efficiency by proposing an energy conscious squirrel search algorithm (EcSSA) for scheduling tasks in fog environment. A salient feature of EcSSA lies in the novel objective function that aims to optimize the energy consumption by minimizing the cumulative active time of all nodes in the network. Simulation studies conducted as part of this work demonstrate that the proposed approach significantly improves average energy consumption, average makespan, and carbon dioxide emission rate in comparison to the existing algorithms.

Cybersecurity in a Scalable Smart City Framework Using Blockchain and Federated Learning for Internet of Things (IoT)

Smart cities increasingly rely on the Internet of Things (IoT) to enhance infrastructure and public services. However, many existing IoT frameworks face challenges related to security, privacy, scalability, efficiency, and low latency. This paper introduces the Blockchain and Federated Learning for IoT (BFLIoT) framework as a solution to these issues. In the proposed method, the framework first collects real-time data, such as traffic flow and environmental conditions, then normalizes, encrypts, and securely stores it on a blockchain to ensure tamper-proof data management. In the second phase, the Data Authorization Center (DAC) uses advanced cryptographic techniques to manage secure data access and control through key generation. Additionally, edge computing devices process data locally, reducing the load on central servers, while federated learning enables distributed model training, ensuring data privacy. This approach provides a scalable, secure, efficient, and low-latency solution for IoT applications in smart cities. A comprehensive security proof demonstrates BFLIoT’s resilience against advanced cyber threats, while performance simulations validate its effectiveness, showing significant improvements in throughput, reliability, energy efficiency, and reduced delay for smart city applications.

The convergence of the fourth industrial revolution and mixed reality: Transforming agricultural economics in developing nations and retail experiences in the U.S.

The Fourth Industrial Revolution (4IR) and Mixed Reality (MR) are revolutionizing industries worldwide by merging advanced digital technologies with physical environments. This paper explores the convergence of 4IR technologies, such as Artificial Intelligence (AI), the Internet of Things (IoT), robotics, and big data analytics, with MR, which includes Augmented Reality (AR) and Virtual Reality (VR). Specifically, this article examines the profound impact of these technologies on two distinct sectors: agricultural economics in developing nations and retail experiences in the United States. In developing nations, the agricultural sector faces numerous challenges, including resource constraints, climate change, and limited access to technology. The integration of 4IR technologies is transforming this sector by enabling precision farming, improving resource management, and optimizing supply chain processes. IoT devices and AI-powered algorithms are enhancing irrigation, fertilization, and pest control, while drones and robotics are automating labor-intensive tasks such as planting and harvesting. MR technologies, particularly AR and VR, are playing a vital role in providing virtual training for farmers, real-time monitoring of machinery, and on-site troubleshooting. These innovations significantly improve agricultural productivity and sustainability, leading to higher yields, more efficient resource use, and better livelihoods for farmers. In contrast, the retail industry in the United States is undergoing a digital transformation fueled by the rise of e-commerce and evolving consumer preferences. MR technologies are at the forefront of this transformation, offering personalized and immersive shopping experiences that enhance customer engagement. AR allows consumers to virtually try on clothing, accessories, and cosmetics, while VR creates immersive brand experiences and virtual stores. These innovations not only enhance the shopping experience but also streamline supply chain management and inventory control through AI and IoT applications. The article explores how these technologies enable retailers to deliver personalized recommendations, optimize inventory levels, and improve logistics operations. Through an in-depth analysis of case studies, this paper illustrates how the convergence of 4IR and MR technologies is driving economic growth and operational efficiency in agriculture and retail. It also highlights the challenges and barriers to technology adoption, such as financial constraints, resistance to change, and regulatory considerations. The article concludes with policy recommendations for governments, businesses, and international organizations to foster the broader implementation of 4IR and MR technologies. These recommendations include investing in digital infrastructure, promoting cross-sector collaborations, and providing incentives for technology adoption. By leveraging these advancements, developing nations can improve agricultural sustainability, and the U.S. retail industry can deliver more engaging, efficient, and personalized experiences, ultimately driving sustainable economic development.

Feasibility and Affordability of Low-Cost Air Sensors with Internet of Things for Indoor Air Quality Monitoring in Residential Buildings: Systematic Review on Sensor Information and Residential Applications, with Experience-Based Discussions

In residential buildings that are private, autonomous, and occupied spaces for most of the time, it is necessary to maintain good indoor air quality (IAQ), especially when there are children, elderly, or other vulnerable users. Within the development of sensors, their low-cost features with adequate accuracy and reliability, as well as Internet of Things applications, make them affordable, flexible, and feasible even for ordinary occupants to guarantee IAQ monitoring in their homes. This systematic review searched papers based on Scopus and Web of Science databases about the Low-Cost Sensors (LCS) and IoT applications in residential IAQ research, and 23 studies were included with targeted research contents. The review highlights several aspects of the active monitoring strategies in residential buildings, including the following: (1) Applying existing appropriate sensors and their target pollutants; (2) Applying micro-controller unit selection; (3) Sensors and devices’ costs and their monitoring applications; (4) Data collection and storage methods; (5) LCS calibration methods in applications. In addition, the review also discussed some possible solutions and limitations of LCS applications in residential buildings based on the applications from the included works and past device development experiences.

DEAC-IoT: Design of lightweight authenticated key agreement protocol for Intra and Inter-IoT device communication using ECC with FPGA implementation

The growing reliance on wireless communication in Internet-of-Things (IoT) devices highlights the critical need for secure and efficient communication protocols, especially in environments vulnerable to cyber threats. Existing IoT protocols often lack sufficient security, creating a need for robust authentication and key exchange mechanisms that can resist attacks while maintaining low computational overhead. In this paper, we propose a fog-enabled network architecture integrated with IoT devices (Intra and Inter IoT device) and develop the DEAC-IoT scheme using Elliptic Curve Cryptography (ECC) for secure authentication and key agreement. Our protocol is designed to protect device-to-device communication from security threats in resource-constrained IoT environments. We validate DEAC-IoT’s security through both informal analysis and formal verification using the Real-Or-Random (RoR) model, demonstrating its resistance to major attacks. Simulation via the Scyther tool confirms that private parameters remain secure throughout the protocol’s execution. For practical feasibility, we implement DEAC-IoT on a Field Programmable Gate Array (FPGA) and conduct performance evaluations. The results show that our protocol surpasses existing protocols in both computational and communication efficiency, making it highly suitable for real-world IoT applications.

Discussion on the Integration of “Post, Course, Competition, Certificate”: Taking the Internet of Things Application Technology Courses in Higher Vocational Colleges as an Example

This article explores the development of the Internet of Things (IoT) application technology course in higher vocational colleges under the background of “post, course, competition, certificate.” It first emphasizes the importance of IoT talent training and course construction in higher vocational colleges and deeply analyzes the core concept of “post, course, competition, certificate” integration. In view of the problems faced in the course construction of IoT application technology in higher vocational colleges and the practical experience of Tianjin Vocational College of Mechanics and Electricity, the implementation strategy of the course construction of IoT application technology in higher vocational colleges is elaborated in detail based on the integrated concept of “post, course, competition, certificate.”

Enhancing IoT connectivity through spectrum sharing in 5G networks

The integration of the Internet of Things (IoT) into high-performance devices and monitoring systems, spanning domains such as smart building, e-health care, and smart agriculture, necessitates a critical emphasis on advancing mobile communications through efficient spectrum utilization. This research addresses pivotal challenges within agricultural IoT applications, specifically focusing on the substantial decline in spectrum efficiency observed with the increasing escalation of network bandwidth. Acknowledging the absence of comprehensive reviews on 5G resource allocation strategies in the existing literature, our study aims to contribute to a nuanced understanding of their implications for service quality. The identified research gaps underscore an urgent need for heightened efforts to optimize resource allocation in 5G networks. This investigation delves into the intricacies of spectrum sharing and real-time analysis techniques within the 5G and beyond network, with a targeted focus on augmenting agricultural IoT services. Three distinct models, namely (i) Non-Priority Algorithm (NPA), (ii) Reserved Channel Algorithm (RCA) - No Permanent Channels, and (iii) Reserved Channel Algorithm (RCA) - Permanent Channels, were meticulously designed and simulated for Agricultural IoT application scenarios. The methodology encompasses the comprehensive evaluation of performance metrics, including call blocking, termination, and handover, to strategically identify and allocate spectrum resources effectively. The research endeavors to address ongoing challenges pertaining to effective communication, standardization, and data management for diverse 5G IoT devices. In light of these persisting concerns, the study not only seeks to enhance the overall efficiency of 5G IoT networks but also proposes innovative perspectives on intelligent and ingenious spectrum allocation techniques. The anticipated outcomes pledge to optimize the utilization of limited spectrum through novel spectrum-sharing strategies, thereby contributing to the advancement of 5G networks and bolstering agricultural IoT devices and services.

Securing Clinical Data in Honduras: Exploring Cybersecurity Regulations and the Role of Blockchain

The existence of cybersecurity regulations in Honduras aimed at safeguarding clinical data remains uncertain, leaving the populace’s health information vulnerable due to the absence of robust legal frameworks. This study sheds light on the current landscape concerning health information protection in the country, underscoring its profound impact on securing medical information systems and the realm of Internet of Things (IoT). It underscores the potential of blockchain technology to provide an innovative solution to address security challenges in this domain. Employing a systematic approach, the research delineates a timeline to elucidate the evolution of legal measures against cybercrime in Honduras, consolidating pertinent governmental documents associated with cybersecurity and the protection of medical data. Furthermore, it underscores the healthcare sector’s susceptibility to cyber threats and the primary privacy apprehensions pertaining to IoT applications in medicine. The study concludes that Honduras lacks a dedicated data protection statute, posing a significant impediment to cybersecurity regulatory efforts. This legal void impedes the effective implementation of measures to safeguard clinical information and shield it from potential cyber risks amidst the proliferation of IoT initiatives and healthcare-related ventures. Nonetheless, this circumstance may present an opportunity for the adoption of technologies such as blockchain. By enabling the establishment of tailored regulatory frameworks, blockchain can adapt to its unique attributes, thereby facilitating its integration in fortifying clinical data protection and enhancing overall cybersecurity protocols.

Technological advances in high mountains: Development of an augmented reality and IoT Application for snow cover monitoring on Huascaran, Peru

Climate change has had a negative impact on Andean glaciers, especially on the snow-capped Huascaran in Peru, which has experienced a loss of 42 % of its glacier mass. Lack of access to accurate snowpack data complicates understanding and informed decision-making in mountainous environments. To address this problem, an application has been developed based on an AR-IoT architecture that integrates augmented reality (AR) and the Internet of Things (IoT) for the periodic monitoring of snow cover on the Huascaran snow-capped mountain. This application uses data collected from meteorological repositories and simulates IoT interactions, while a mobile app offers an interactive and understandable visualization of the data. The Scrum methodology was applied iteratively, from the creation of the 3D model to the connection with the Ubidots platform to store and share the data. The analysis of 58 readings, obtained between April and May 2023, showed correlations between variables such as snow depth, temperature, and wind speed, which served to validate the operation of the AR-IoT technology implemented. These variables offer an initial understanding of atmospheric conditions in high mountains. In addition, the technology developed has the potential to expand to include more relevant data in future analyses. The tool demonstrates the effectiveness of combining these technologies for monitoring high mountain snow cover and making informed decisions about water resources management.

Design of high performances electronically reconfigurable antenna arrays by means of parasitic structures and RF-MEMS switches

AbstractThis work explores the development of smart array antennas, able to electronically change their beam patterns through reconfigurable linear, planar, or conformal parasitic structures controlled by mean of a set of electronic switches namely the radio frequency micro-electromechanical switches (RF-MEMS). The motivation of this study is to extend and make competitive the use of reconfigurable antenna arrays in areas such as the Internet of Things (IoT) and wireless sensor networks. These hot research areas require a capillary distribution of sensors characterized by compact dimensions and limited cost. The radiating systems play a key role in such applications since the improvement of the radiating properties can dramatically reduce the power consumption of such sensors and extend their operative range. Standard smart antenna arrays such as phased, or fully adaptive arrays are extremely expensive, expensive and difficult to control. In this work, the use of smart reconfigurable antenna arrays that do not require expensive digital variable phase shifters and attenuators has been considered. The proposed antenna structures are based on reconfigurable parasitic structures, that thanks to suitable RF-MEMs switches are able to obtain a desired beam pattern, by steering the main beam in a given direction, removing eventually interfering signals and maximizing the signal-to-noise ratio SNR. The versatility, simplicity, and low cost of such antennas make them particularly suitable for the emergent wireless sensor networks and IoT applications. Theoretical background, design guidelines, and suggestions for their fabrication are detailed in this work. Some antenna models for different practical applications are designed, optimized, numerically assessed with commercial electromagnetic simulators, and commented to provide a valid alternative to researchers involved in antenna design for IoT applications.

Development of Synchronization Selection Method in IoT with Secure Channel Bidirectional Communication

Background: The rapid development of wireless communications and mobile computation has given rise to the novel Internet of Things (IoT) systems, which is causing considerable research attention and industrial development. However, the lack of synchronization between the timers of IoT devices compromises the network's security. Aim: The purpose of this patent application is to present a technique for synchronizing the timepieces of IoT gadgets and establishing a secure channel for the transmission of data from source to destination. Objective: This study proposes a Synchronization Selection Method (SSM) for IoT systems to enhance network security and reduce packet loss. Methods: The method utilizes time-lay synchronization and RSA algorithm-based secure channel establishment. Time lay is a technique that was developed for IoT devices to achieve efficient clock synchronization of sensor nodes. Before synchronizing the sensor nodes' timings, the cluster leaders initiate the process. Utilizing a finite number of nodes, the proposed method was executed in MATLAB. Results: Time-lay synchronization involves all network nodes synchronizing their clocks with a third-party clock. In the context of time-lay synchronization, the term “third-party clock” refers to a single specific point that contains the time signal that all nodes in the network use as a reference. This third-party clock is outside of the network nodes and acts as the standard for the precise and synchronized time within the network. Therefore, it can be deduced that each of the techniques possesses its advantages and disadvantages. Each of the synchronization techniques has the potential to significantly benefit the IoT by offering smart clock synchronization that is more secure. Experimental results demonstrate that the proposed method improves throughput and reduces packet loss compared to existing techniques. Conclusion: The potential of this patent is highly significant for solving the synchronization problem of IoT devices and enhancing network security with decreased network packet loss. Other: The SSM would be assessed using the parameters of packet loss and throughput.

Leveraging IoT for Enhanced Supply Chain Management in Manufacturing

Information Technology especially through use of the Internet of Things (IoT) bring significant change to the manufacturing supply chain in terms of efficiency, visibility and responsiveness. This paper aims to assess the position of IoT with reference to contemporary manufacture; with regard to its advantages and the issues with its implementation. This paper aims to review IoT applications which foster supply chain management improvement through the identification of real time tracking, predictive maintenance, and data analysis. Furthermore, the paper discusses the technical, organizational, and security issues are described that manufacturers encounter while implementing IoT with the current systems. Introducing future trends of the IoT and its implication for supply chain management, the prospects for evolution are described and envisioned. This work supports the centrality of IoT architectures vital for manufacturers in today’s globalized market where unpredictability breaches financial plans. Lastly, the implemented research findings on how IoT can be effectively incorporated in SCM are discussed followed by a proposal on future research.

5G Security Features, Vulnerabilities, Threats, and Data Protection in IoT and Mobile Devices: A Systematic Review

The evolution of wireless communications, from the first to the fifth generation, has driven Internet of Things (IoT) advancements. IoT is transforming sectors like agriculture, healthcare, and transportation, but also presents challenges like spectrum bandwidth demand, speed requirements, and security issues. IoT environments, with embedded sensors and actuators, connect to other devices to transmit and receive data over the internet. These data are processed locally or in the cloud, enabling decision-making and automation. Various wireless technologies, including Bluetooth, Zigbee, LoRa, and WiFi, cater to specific IoT applications. 5G, with its high speeds, low latency, and efficient bandwidth sharing, is ideal for high-speed data transmission, and thus it provides robust infrastructure for low-latency and bandwidth-sensitive applications. This latest mobile network technology is being rapidly implemented worldwide, providing unprecedented throughput and low latency, which are crucial for the IoT. This paper presents a comprehensive systematic review on 5G security features, vulnerabilities, threats to mobile devices, security in IoT devices, common attacks to IoT devices and protection of data in this environment. Despite enhanced security, 5G faces challenges like Distributed Denial of Service (DDoS), Man In The Middle (MITM), cross-slice disruptions, and side-channel attacks.

Resource Allocation Based on Task Priority and Resource Consumption in Edge Computing

The computational power of Internet of Things (IoT) devices is usually low, which makes it necessary to process data and extract relevant information on devices with higher processing capacity. Edge Computing emerged as a complementary solution to cloud computing, providing devices at the network edge with computational resources to handle the data processing and analysis that constrained IoT devices eventually cannot perform. This solution allows data processing closer to the IoT devices, reducing latency for IoT applications. However, the resource constraints of edge nodes, which have lower computational power than the cloud nodes, make resource allocation and processing massive requests challenging. This study proposes an edge resource allocation mechanism based on task priority and machine learning. The proposed approach efficiently allocates resources for IoT requests based on their task priorities while monitoring the resource consumption of edge nodes. This study evaluates the performance of different classification algorithms by using well-known metrics for classifying models. The most efficient classifier achieved an accuracy of 92% and a precision of 90%. The results indicate good performance when using this classifier in the evaluated approach. The proposed mechanism demonstrated that resource management can be done more efficiently with significantly lower resource utilization when compared to an allocation method based only on distance. The study tested different scenarios regarding the number of requests, edge nodes, and a proposed failure mechanism to schedule failed node tasks to functional nodes. This failure control mechanism is a significant contribution of the proposal. Therefore, the proposed method in this study can become a valuable tool for efficient resource management with reduced computational cost and efficient resource allocation.

3D Lightweight Cryptosystem Design For IoT Applications Based On Composite S-Box

The security of the Internet of Things (IoT) depends on strong cryptographic functions that require extensive computation and resources. The security of Internet of Things (IoT) relies on the use of strong cryptographic functions that demand extensive computation and resources. Therefore, the selection of a cryptographic system is influenced by the computational and communicational capabilities of IoT devices, including their energy requirements, memory constraints, and execution time. This paper aims to develop a lightweight and secure encryption algorithm for IoT applications, focusing on advancing cryptanalysis techniques. We propose a new lightweight algorithm named enhanced 3D RECTANGLE, designed to deliver robust security for IoT applications and optimized for cell phones with minimal memory usage, low power consumption, and efficient performance. The RECTANGLE block cipher was chosen for this research due to its high efficiency and speed relative to other lightweight algorithms, despite some security issues. The proposed enhanced 3D RECTANGLE algorithm improves confusion and diffusion properties through a new 3D array block rotation method for 4x4 plaintext, based on a 128-bit key and 16 rounds. Additionally, we designed a 4x4 composite S-Box with a Galois field pipelining structure, offering a more advanced solution compared to the Look-Up Table method. The cryptanalysis, avalanche effect, and bit error rate tests were conducted to verify the security strength of the proposed algorithm. The proposed algorithm was evaluated against two existing algorithms, RECTANGLE and Extended 3D RECTANGLE. The enhanced 3D RECTANGLE algorithm provides better cryptanalysis, demonstrating a 40% avalanche effect and achieving a bit error rate (BER) of 31%, indicating its greater effectiveness compared to the other two lightweight algorithms.

Multi-objective joint optimization of task offloading based on MADRL in internet of things assisted by satellite networks

The Internet of Things (IoT) integrates a large number of heterogeneous terminals and systems, possessing ubiquitous sensing and computing capabilities. Satellite networks are the crucial supplement to terrestrial networks, particularly in remote areas where network infrastructures are sparingly distributed or unavailable. Combining edge computing with satellite networks provides on-orbit computing capabilities for IoT applications, reducing service delay and enhancing service quality. Due to the resource constraints of satellites, achieving collaborative services through task offloading among multiple satellites becomes essential. Both the privacy leakage risk arising from frequent data interactions and the load imbalance resulting from offloading preferences cannot be overlooked. The key challenge of task offloading is to safeguard the privacy of offloaded data and ensure the system’s load balance while minimizing the delay and energy consumption. In this paper, the task offloading problem is formulated as a Partially Observable Markov Decision Process (POMDP), and a task offloading algorithm based on multi-objective joint optimization using multi-agent deep reinforcement learning in a distributed architecture is proposed. The simulation results validate the efficacy of our model and algorithm, demonstrating that our proposed algorithm achieves better performance in minimizing comprehensive offloading costs.

Systematic Review of IoT-Based Solutions for User Tracking: Towards Smarter Lifestyle, Wellness and Health Management.

The Internet of Things (IoT) base has grown to over 20 billion devices currently operational worldwide. As they greatly extend the applicability and use of biosensors, IoT developments are transformative. Recent studies show that IoT, coupled with advanced communication frameworks, such as machine-to-machine (M2M) interactions, can lead to (1) improved efficiency in data exchange, (2) accurate and timely health monitoring, and (3) enhanced user engagement and compliance through advancements in human-computer interaction. This systematic review of the 19 most relevant studies examines the potential of IoT in health and lifestyle management by conducting detailed analyses and quality assessments of each study. Findings indicate that IoT-based systems effectively monitor various health parameters using biosensors, facilitate real-time feedback, and support personalized health recommendations. Key limitations include small sample sizes, insufficient security measures, practical issues with wearable sensors, and reliance on internet connectivity in areas with poor network infrastructure. The reviewed studies demonstrated innovative applications of IoT, focusing on M2M interactions, edge devices, multimodality health monitoring, intelligent decision-making, and automated health management systems. These insights offer valuable recommendations for optimizing IoT technologies in health and wellness management.