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The currently observed development of time-sensitive applications also affects wireless communication with the IoT carried by UAVs. Although research on wireless low-latency networks has matured, there are still issues to solve at the transport layer. Since there is a general agreement that classical transport solutions are not able to achieve end-to-end delays in the single-digit millisecond range, in this paper, the use of WebRTC is proposed as a potential solution to this problem. This article examines UAV-borne WebRTC-based IoT in an outdoor environment. The results of field experiments conducted under various network conditions show that, in highly reliable networks, UAV and WebRTC-based IoT achieved stable end-to-end delays well below 10 ms during error-free air-to-ground transmissions, and below 10 ms in the immediate vicinity of the retransmitted packet. The significant advantage of the WebRTC data channel over the classic WebSocket is also demonstrated. Full article

LoRa modulation is a widely used technology known for its long-range transmission capabilities, making it ideal for applications with low data rate requirements, such as IoT-enabled sensor networks. However, its inherent low data rate poses a challenge for applications that require higher throughput, such as video surveillance and disaster monitoring, where large image files must be transmitted over long distances in areas with limited communication infrastructure. In this paper, we introduce the LoRa Resource Allocation (LRA) algorithm, designed to address these limitations by enabling parallel transmissions, thereby reducing the total transmission time (T_tx) and increasing the bit rate (BR). The LRA algorithm leverages the quasi-orthogonality of LoRa’s Spreading Factors (SFs) and employs specially designed end devices equipped with dual LoRa transceivers, each operating on a distinct SF. For experimental analysis we choose an image transmission application and investigate various parameter combinations affecting T_tx to optimize interference, BR, and image quality. Experimental results show that our proposed algorithm reduces T_tx by 42.36% and 19.98% for SF combinations of seven and eight, and eight and nine, respectively. In terms of BR, we observe improvements of 73.5% and 24.97% for these same combinations. Furthermore, BER analysis confirms that the LRA algorithm delivers high-quality images at SNR levels above −5 dB in line-of-sight communication scenarios. Full article

In the area of agriculture and livestock management, the integration of the Internet of Things (IoT) has emerged as a groundbreaking strategy to enhance operational efficiency and advance intelligent process management. However, this sector faces significant challenges, including ambiguity in product origins and limited regulatory oversight of IoT devices. This paper explores the innovative integration of blockchain technology within the agricultural and livestock IoT, highlighting how this convergence significantly enhances operational security and transparency. We provide an in-depth review of the latest applications and advancements of blockchain in these domains, offering a comprehensive analysis of the current state of technology and its implications. Furthermore, this paper discusses the potential future development trajectories in agricultural and livestock IoT, emphasizing blockchain’s role in addressing current challenges and shaping future innovations. The findings suggest that blockchain technology not only improves data security and trustworthiness but also opens new avenues for efficient and transparent management in agriculture and animal husbandry. Full article

Edge computing systems must offer low latency at low cost and low power consumption for sensors and other applications, including the IoT, smart vehicles, smart homes, and 6G. Thus, substantial research has been conducted to identify optimum task allocation schemes in this context using non-linear optimization, machine learning, and market-based algorithms. Prior work has mainly focused on two methodologies: (i) formulating non-linear optimizations that lead to NP-hard problems, which are processed via heuristics, and (ii) using AI-based formulations, such as reinforcement learning, that are then tested with simulations. These prior approaches have two shortcomings: (a) there is no guarantee that optimum solutions are achieved, and (b) they do not provide an explicit formula for the fraction of tasks that are allocated to the different servers to achieve a specified optimum. This paper offers a radically different and mathematically based principled method that explicitly computes the optimum fraction of jobs that should be allocated to the different servers to (1) minimize the average latency (delay) of the jobs that are allocated to the edge servers and (2) minimize the average energy consumption of these jobs at the set of edge servers. These results are obtained with a mathematical model of a multiple-server edge system that is managed by a task distribution platform, whose equations are derived and solved using methods from stochastic processes. This approach has low computational cost and provides simple linear complexity formulas to compute the fraction of tasks that should be assigned to the different servers to achieve minimum latency and minimum energy consumption. Full article

This paper explores the development and testing of two Internet of Things (IoT) applications designed to leverage Vehicle-to-Infrastructure (V2I) communication for managing intelligent intersections. The first scenario focuses on enabling the rapid and safe passage of emergency vehicles through intersections by notifying approaching drivers via a mobile application. The second scenario enhances pedestrian safety by alerting drivers, through the same application, about the presence of pedestrians detected at crosswalks by a traffic sensor equipped with neural network capabilities. Both scenarios were tested at two distinct intelligent intersections in Lioni, Avellino, Italy, and demonstrated notable effectiveness. Results show a significant reduction in emergency vehicle response times and a measurable increase in driver awareness of pedestrians at crossings. The findings underscore the potential of V2I technologies to improve traffic flow, reduce risks for vulnerable road users, and contribute to the advancement of safer and smarter urban transportation systems. Full article

In recent decades, technology has undergone significant transformations, aimed at optimizing and enhancing the quality of human life. A prime example of this progress is the Internet of Things (IoT) technology. Today, the IoT is widely applied across diverse sectors, including logistics, communications, agriculture, education, and infrastructure, demonstrating its versatility and profound relevance in various domains. Agriculture has historically been a fundamental sector for meeting humanity’s basic needs, and it is indispensable for survival and development. A critical factor in this regard is climatic and meteorological conditions directly influencing agricultural productivity. Therefore, real-time monitoring and analysis of these variables becomes imperative for optimizing production and reducing vulnerability to climate change. This paper presents the development and implementation of a low-power wide-area network (LPWAN) solution using LoRaWAN (long-range wide-area network) technology, designed for real-time environmental monitoring in agricultural applications. The system consists of energy-efficient end nodes and a custom-configured gateway, designed to optimize data transmission and power consumption. The end nodes integrate advanced sensors for temperature, humidity, and pressure, ensuring accurate data collection. Full article

The Internet of Things (IoT) represents a rapidly growing field, where billions of intelligent devices are interconnected through the Internet, enabling the seamless sharing of data and resources. These smart devices are typically employed to sense various environmental characteristics, including temperature, motion of objects, and occupancy, and transfer their values to the nearest access points for further analysis. The exponential growth in sensor availability and deployment, powered by recent advances in sensor fabrication, has greatly increased the complexity of IoT network architecture. As the market for these sensors grows, so does the problem of ensuring that IoT networks meet high requirements for network availability, dependability, flexibility, and scalability. Unlike traditional networks, IoT systems must be able to handle massive amounts of data generated by various and frequently-used resource-constrained devices, while ensuring efficient and dependable communication. This puts high constraints on the design of IoT, mainly in terms of the required network availability, reliability, flexibility, and scalability. To this end, this work considers deploying a recent technology of distributed edge computing to enable IoT applications over dense networks with the announced requirements. The proposed network depends on distributed edge computing at two levels: multiple access edge computing and fog computing. The proposed structure increases network scalability, availability, reliability, and scalability. The network model and the energy model of the distributed nodes are introduced. An energy-offloading method is considered to manage IoT data over the network energy, efficiently. The developed network was evaluated using a developed IoT testbed. Heterogeneous evaluation scenarios and metrics were considered. The proposed model achieved a higher energy efficiency by 19%, resource utilization by 54%, latency efficiency by 86%, and reduced network congestion by 92% compared to traditional IoT networks. Full article

The research investigates the advantages of real-time soil quality monitoring for various land management applications. We emphasize the crucial role of soil modeling and mapping by visualizing and understanding aridity trends across different regions. The primary objective is to develop an innovative soil monitoring system utilizing Internet of Things (IoT) technology. This system, equipped with intelligent sensors, will operate autonomously, collecting real-time data to identify key trends in soil conditions. Our system employs smart soil sensors to measure macronutrient values up to a depth of 80 cm. These sensors will transmit data wirelessly. Laboratory research involved a two-month evaluation of the system’s performance across three distinct soil types collected from diverse geographical locations. Analysis of the three soil types yielded a model accuracy estimate of 0.01. A strong positive linear correlation (0.92) between moisture and macronutrients has been observed in two out of the three soil types. The results, particularly related to soil moisture, were averaged over the testing period. While precipitation values were not directly integrated into the modeling framework, they were calculated in l/m² to ensure accurate real-time estimates. The need for such advanced monitoring systems is critical for optimizing key soil macronutrients and enabling spatiotemporal mapping. This information is essential for developing effective strategies to mitigate soil aridification and prevent desertification. Full article

This review explores the applications of Convolutional Neural Networks (CNNs) in smart agriculture, highlighting recent advancements across various applications including weed detection, disease detection, crop classification, water management, and yield prediction. Based on a comprehensive analysis of more than 115 recent studies, coupled with a bibliometric study of the broader literature, this paper contextualizes the use of CNNs within Agriculture 5.0, where technological integration optimizes agricultural efficiency. Key approaches analyzed involve image classification, image segmentation, regression, and object detection methods that use diverse data types ranging from RGB and multispectral images to radar and thermal data. By processing UAV and satellite data with CNNs, real-time and large-scale crop monitoring can be achieved, supporting advanced farm management. A comparative analysis shows how CNNs perform with respect to other techniques that involve traditional machine learning and recent deep learning models in image processing, particularly when applied to high-dimensional or temporal data. Future directions point toward integrating IoT and cloud platforms for real-time data processing and leveraging large language models for regulatory insights. Potential research advancements emphasize improving increased data accessibility and hybrid modeling to meet the agricultural demands of climate variability and food security, positioning CNNs as pivotal tools in sustainable agricultural practices. A related repository that contains the reviewed articles along with their publication links is made available. Full article

Wind power prediction is essential for ensuring the stability and efficient operation of modern power systems, particularly as renewable energy integration continues to expand. This paper presents a comprehensive review of machine learning techniques applied to wind power prediction, emphasizing their advantages over traditional physical and statistical models. Machine learning methods, especially deep learning approaches such as Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and ensemble learning techniques like XGBoost, excel in addressing the nonlinearity and complexity of wind power data. The review also explores critical aspects such as data preprocessing, feature selection strategies, and model optimization techniques, which significantly enhance prediction accuracy and robustness. Challenges such as data acquisition difficulties, complex terrain influences, and sensor quality issues are examined in depth, with proposed solutions discussed. Additionally, the paper highlights future research directions, including the potential of multi-model fusion, emerging deep learning technologies like Transformers, and the integration of smart sensors and IoT technologies to develop intelligent, automated, and reliable prediction systems. By addressing existing challenges and leveraging advanced machine learning techniques, this work provides valuable insights into the current state of wind power prediction research and offers strategic guidance for enhancing the applicability and reliability of prediction models in practical scenarios. Full article

Efficient management of soil nutrients is essential for optimizing crop production, ensuring sustainable agricultural practices, and addressing the challenges posed by population growth and environmental degradation. Smart agriculture, using advanced technologies, plays an important role in achieving these goals by enabling real-time monitoring and precision management of nutrients. In open-field soil cultivation, spatial variability in soil properties demands site-specific nutrient management and integration with variable-rate technology (VRT) to optimize fertilizer application, reduce nutrient losses, and enhance crop yields. Hydroponic solution cultivation, on the other hand, requires precise monitoring and control of nutrient solutions to maintain optimal conditions for plant growth, ensuring efficient use of water and fertilizers. This review aims to explore recent trends in soil and solution nutrient sensing technologies for open-field soil and facilitated hydroponic cultivation, highlighting advancements that promote efficiency and sustainability. Key technologies include electrochemical and optical sensors, Internet of Things (IoT)-enabled monitoring, and the integration of machine learning (ML) and artificial intelligence (AI) for predictive modeling. Blockchain technology is also emerging as a tool to enhance transparency and traceability in nutrient management, promoting compliance with environmental standards and sustainable practices. In open-field soil cultivation, real-time sensing technologies support targeted nutrient application by accounting for spatial variability, minimizing environmental risks such as runoff and eutrophication. In hydroponic solution cultivation, precise solution sensing ensures nutrient balance, optimizing plant health and productivity. By advancing these technologies, smart agriculture can achieve sustainable crop production, improved resource efficiency, and environmental protection, fostering a resilient food system. Full article

This review explores the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in food safety detection and risk prediction. This paper highlights the advantages of CNNs in image processing and feature recognition, as well as the powerful capabilities of RNNs (especially their variant LSTM) in time series data modeling. This paper also makes a comparative analysis in many aspects: Firstly, the advantages and disadvantages of traditional food safety detection and risk prediction methods are compared with deep learning technologies such as CNNs and RNNs. Secondly, the similarities and differences between CNNs and fully connected neural networks in processing image data are analyzed. Furthermore, the advantages and disadvantages of RNNs and traditional statistical modeling methods in processing time series data are discussed. Finally, the application directions of CNNs in food safety detection and RNNs in food safety risk prediction are compared. This paper also discusses combining these deep learning models with technologies such as the Internet of Things (IoT), blockchain, and federated learning to improve the accuracy and efficiency of food safety detection and risk warning. Finally, this paper mentions the limitations of RNNs and CNNs in the field of food safety, as well as the challenges in the interpretability of the model, and suggests the use of interpretable artificial intelligence (XAI) technology to improve the transparency of the model. Full article

Linear antenna arrays exhibit radiation patterns that are restricted to a half-space and feature axial radiation, which can be a significant drawback for applications that require omnidirectional coverage. To address this limitation, the synthesis method utilizing the Taguchi approach, originally designed for linear arrays, can be effectively extended to two-dimensional or planar antenna arrays. In the context of a linear array, the synthesis process primarily involves determining the feeding law and/or the spatial distribution of the elements along a single axis. Conversely, for a planar array, the synthesis becomes more complex, as it requires the identification of the complex weighting of the feed and/or the spatial distribution of sources across a two-dimensional plane. This adaptation to planar arrays is facilitated by substituting the direction $\theta$ with the pair of directions $(\theta ,\varphi )$, allowing for a more comprehensive coverage of the angular domain. This article focuses on exploring various configurations of planar arrays, aiming to enhance their performance. The primary objective of these configurations is often to minimize the levels of secondary lobes and/or array lobes while enabling a full sweep of the angular space. Secondary lobes can significantly impede system performance, particularly in multibeam applications, where they restrict the minimum distance for frequency channel reuse. This restriction is critical, as it affects the overall efficiency and effectiveness of communication systems that rely on precise beamforming and frequency allocation. By investigating alternative planar array designs and their synthesis methods, this research seeks to provide solutions that improve coverage, reduce interference from secondary lobes, and ultimately enhance the functionality of antennas in diverse applications, including telecommunications, radar systems, and wireless communication. Full article

With the extensive deployment and application of the Internet of Things (IoT), 5G and 6G technologies and edge intelligence, the volume of data generated by IoT and the number of intelligence applications derived from these data are rapidly growing. However, the absence of effective mechanisms to safeguard the vast data generated by IoT, along with the security and privacy of edge intelligence applications, hinders their further development and adoption. In recent years, Trusted Execution Environment (TEE) has emerged as a promising technology for securing cloud data storage and cloud processing, demonstrating significant potential for ensuring data and application confidentiality in more scenarios. Nevertheless, applying TEE technology to enhance security in IoT and edge intelligence scenarios still presents several challenges. This paper investigates the technical challenges faced by current TEE solutions, such as performance overhead and I/O security issues, in the context of the resource constraints and data mobility that are inherent to IoT and edge intelligence applications. Using Intel Software Guard Extensions (SGX) technology as a case study, this paper validates these challenges through extensive experiments. The results provide critical assessments and analyses essential for advancing the development and usage of TEE in IoT and edge intelligence scenarios. Full article

Smart Cities leverage large networks of wirelessly connected nodes embedded with sensors and/or actuators. Cellular IoT, such as NB-IoT and 5G RedCap, is often preferred for these applications thanks to its long range, extensive coverage, and good quality of service. In these networks, wireless communication dominates power consumption, motivating research on energy-efficient yet resilient and robust wireless systems. Many IoT use cases require low latency but cannot afford high-power radios continuously operating to accomplish this. In these cases, wake-up receivers (WURs) are a promising solution: while the high-power main radio (MR) is turned off/idle, a lightweight WUR is continuously monitoring the RF channel; when it detects a wake-up sequence, the WUR will turn on the MR for subsequent communications. This article provides an overview of WUR hardware design considerations and challenges for 4G and 5G cellular IoT, summarizes the recent 3GPP activities to standardize NB-IoT and 5G wake-up signals, and presents a state-of-the-art WUR chip. Full article