Search Results (3,221)

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

The development of reliable gas sensors is very important in many fields such as safety, environment, and agriculture, and is especially essential for industrial waste and air pollution monitoring. As the performance of mobile platforms equipped with sensors such as smartphones and drones and the technologies supporting them (wireless communication, battery performance, data processing technology, etc.) are spreading and improving, a lot of efforts are being made to perform these tasks by using portable systems such as smartphones or installing them on unmanned wireless platforms such as drones. For example, research is continuously being conducted on chemical sensors for field monitoring using smartphones and rapid monitoring of air pollution using unmanned aerial vehicles (UAVs). In this paper, we review the measurement results of various chemical sensors available on mobile platforms including drones and smartphones, and the analysis of detection results using machine learning. This topic covers a wide range of specialized fields such as materials engineering, aerospace engineering, physics, chemistry, environmental engineering, electrical engineering, and machine learning, and it is difficult for experts in one field to grasp the entire content. Therefore, we have explained various concepts with relatively simple pictures so that experts in various fields can comprehensively understand the overall topics. 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

Quadratic forms with random kernel matrices are ubiquitous in applications of multivariate statistics, ranging from signal processing to time series analysis, biomedical systems design, wireless communications performance analysis, and other fields. Their statistical characterization is crucial to both design guideline formulation and efficient computation of performance indices. To this end, random matrix theory can be successfully exploited. In particular, recent advancements in spectral characterization of finite-dimensional random matrices from the so-called polynomial ensembles allow for the analysis of several scenarios of interest in wireless communications and signal processing. In this work, we focus on the characterization of quadratic forms in unit-norm vectors, with unitarily invariant random kernel matrices, and we also provide some approximate but numerically accurate results concerning a non-unitarily invariant kernel matrix. Simulations are run with reference to a peculiar application scenario, the so-called spectrum sensing for wireless communications. Closed-form expressions for the moment generating function of the quadratic forms of interest are provided; this will pave the way to an analytical performance analysis of some spectrum sensing schemes, and will potentially assist in the rate analysis of some multi-antenna systems. Full article

Unmanned aerial vehicle (UAV)-based wireless sensor networks (WSNs) hold great promise for supporting ground-based sensors due to the mobility of UAVs and the ease of establishing line-of-sight links. UAV-based WSNs equipped with mobile edge computing (MEC) servers effectively mitigate challenges associated with long-distance transmission and the limited coverage of edge base stations (BSs), emerging as a powerful paradigm for both communication and computing services. Furthermore, incorporating simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) as passive relays significantly enhances the propagation environment and service quality of UAV-based WSNs. However, most existing studies place STAR-RISs in fixed positions, ignoring the flexibility of STAR-RISs. Some other studies equip UAVs with STAR-RISs, and UAVs act as flight carriers, ignoring the computing and caching capabilities of UAVs. To address these limitations, we propose an energy-efficient aerial STAR-RIS-aided computing offloading and content caching framework, where we formulate an energy consumption minimization problem to jointly optimize content caching decisions, computing offloading decisions, UAV hovering positions, and STAR-RIS passive beamforming. Given the non-convex nature of this problem, we decompose it into a content caching decision subproblem, a computing offloading decision subproblem, a hovering position subproblem, and a STAR-RIS resource allocation subproblem. We propose a deep reinforcement learning (DRL)–successive convex approximation (SCA) combined algorithm to iteratively achieve near-optimal solutions with low complexity. The numerical results demonstrate that the proposed framework effectively utilizes resources in UAV-based WSNs and significantly reduces overall system energy consumption. Full article

A communication network integrating multiple modes can effectively support the sustainable development of next-generation wireless communications. Integrated sensing, communication, and power transfer (ISCPT) represents an emerging technological paradigm that not only facilitates information transmission but also enables environmental sensing and wireless power transfer. To achieve optimal beamforming in transmission, it is crucial to satisfy multiple constraints, including quality of service (QoS), radar sensing accuracy, and power transfer efficiency, while ensuring fundamental system performance. The presence of multiple parametric constraints makes the problem a non-convex optimization challenge, underscoring the need for a solution that balances low computational complexity with high precision. Additionally, the accuracy of channel state information (CSI) is pivotal in determining the achievable rate, as imperfect or incomplete CSI can significantly degrade system performance and beamforming efficiency. Deep reinforcement learning (DRL), a machine learning technique where an agent learns by interacting with its environment, offers a promising approach that can dynamically optimize system performance through adaptive decision-making strategies. In this paper, we propose a DRL-based ISCPT framework, which effectively manages complex environmental states and continuously adjusts variables related to sensing, communication, and energy harvesting to enhance overall system efficiency and reliability. The achievable rate upper bound can be inferred through robust, learnable beamforming in the ISCPT system. Our results demonstrate that DRL-based algorithms significantly improve resource allocation, power management, and information transmission, particularly in dynamic and uncertain environments with imperfect CSI. Full article

Multiple-input–multiple-output (MIMO), which uses multiple antennas at the transmitter and receiver, is now an essential technique for increasing communication capacity without widening the occupied radio bandwidth. However, antenna arrays within a deep subwavelength dimension degrade MIMO performance due to mutual coupling between the antenna elements. In particular, very small devices such as smartwatches encounter this problem. To address this, we propose Augmented MIMO, mounting a larger antenna array on the human body, for small wearable devices. The experimental results demonstrate throughput improvement with the proposed scheme, even if the overall antenna gain decreases with external body-mounted antennas. This work contributes to the future development of yet another scheme to improve the communication performance of small wearable devices—using the human body as a spacious antenna fixture. Full article

The growing demand for high-speed data transfer and ultralow latency in wireless networks-on-chips (WiNoC) has spurred exploration into innovative communication paradigms. Recent advancements highlight the potential of the terahertz (THz) band, a largely untapped frequency range, for enabling ultrafast tera-bit-per-second links in chip multiprocessors. However, the ultrashort duration of THz pulses, often in the femtosecond range, makes synchronization a critical challenge, as even minor timing errors can cause significant data loss. This study introduces a preamble-aided noncoherent synchronization scheme for time-of-arrival (ToA) estimation in pulse-based WiNoC communication operating in the THz band (0.02–0.8 THz). The scheme transmits the preamble, a known sequence of THz pulses, at the beginning of each symbol, allowing the energy-detection receiver to collect and analyze the energy of the preamble across multiple integrators. The integrator with maximum energy output is then used to estimate the symbol’s ToA. A preamble design based on maximum pulse energy constraints is also presented. Performance evaluations demonstrate a synchronization probability exceeding $0.98$ for distances under 10 mm at a signal-to-noise ratio of 20 dB, with a normalized mean squared error below ${10}^{-2}$. This scheme enhances synchronization reliability, supporting energy-efficient, high-performance WiNoCs for future multicore systems. Full article

The advent of millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, coupled with reconfigurable intelligent surfaces (RISs), presents a significant opportunity for advancing wireless communication technologies. This integration enhances data transmission rates and broadens coverage areas, but challenges in channel estimation (CE) remain due to the limitations of the signal processing capabilities of RIS. To address this, we propose an adaptive channel estimation framework comprising two algorithms: log-sum normalized least mean squares (Log-Sum NLMS) and hybrid normalized least mean squares-normalized least mean fourth (Hybrid NLMS-NLMF). These algorithms leverage the sparse nature of mmWave channels to improve estimation accuracy. The Log-Sum NLMS algorithm incorporates a log-sum penalty in its cost function for faster convergence, while the Hybrid NLMS-NLMF employs a mixed error function for better performance across varying signal-to-noise ratio (SNR) conditions. Our analysis also reveals that both algorithms have lower computational complexity compared to existing methods. Extensive simulations validate our findings, with results illustrating the performance of the proposed algorithms under different parameters, demonstrating significant improvements in channel estimation accuracy and convergence speed over established methods, including NLMS, sparse exponential forgetting window least mean square (SEFWLMS), and sparse hybrid adaptive filtering algorithms (SHAFA). Full article

Embedded systems, particularly when integrated into the Internet of Things (IoT) landscape, are critical for projects requiring robust, energy-efficient interfaces to collect real-time data from the environment. As these systems become complex, the need for dynamic reconfiguration, improved availability, and stability becomes increasingly important. This paper presents the design of a framework architecture that supports dynamic reconfiguration and “on-the-fly” code execution in IoT-enabled embedded systems, including a virtual machine capable of hot reloads, ensuring system availability even during configuration updates. A “hardware-in-the-loop” workflow manages communication between the embedded components, while low-level coding constraints are accessible through an additional abstraction layer, with examples such as MicroPython or Lua. The study results demonstrate the VM’s ability to handle serialization and deserialization with minimal impact on system performance, even under high workloads, with serialization having a median time of 160 microseconds and deserialization having a median of 964 microseconds. Both processes were fast and resource-efficient under normal conditions, supporting real-time updates with occasional outliers, suggesting room for optimization and also highlighting the advantages of VM-based firmware update methods, which outperform traditional approaches like Serial and OTA (Over-the-Air, the ability to update or configure firmware, software, or devices via wireless connection) updates by achieving lower latency and greater consistency. With these promising results, however, challenges like occasional deserialization time outliers and the need for optimization in memory management and network protocols remain for future work. This study also provides a comparative analysis of currently available commercial solutions, highlighting their strengths and weaknesses. Full article

Fires and fire hazards in industrial environments pose a significant risk to safety, infrastructure, and the operational community. The need for real-time monitoring systems capable of detecting fires early and transmitting alerts promptly is crucial. This paper presents a fire monitoring system utilizing lightweight communication protocols, a multi-hop wireless network, and anomaly detection techniques. The system leverages Message Queue Telemetry Transport (MQTT) for efficient message exchange, the ESP-NOW for low-latency and reliable multi-hop wireless communications, and a radio environment map for optimal node placement, eliminating packet loss and ensuring robust data transmission. The proposed system addresses the limitations of traditional fire monitoring systems, providing flexibility, scalability, and robustness in detecting fire. Data collected by ESP32-CAM sensors, which are equipped with pre-trained YOLOv5-based fire detection modules, are processed and transmitted to a central monitoring server. Experimental results demonstrate a 100% success rate in fire detection transmissions, a significant reduction in latency to 150ms, and zero packet loss under REM-guided configuration. These findings validate the system’s suitability for real-time monitoring in high-risk industrial settings. Future work will focus on enhancing the anomaly detection model for greater accuracy, expanding scalability through additional communication protocols, like LoRaWAN, and incorporating adaptive algorithms for real-time network optimization. Full article

Recent advances in signal processing and AI-based inference enable the exploitation of wireless communication signals to collect information on devices, people, actions, and the environment in general, i.e., to perform Integrated Sensing And Communication (ISAC). This possibility offers exciting opportunities for Internet of Things (IoT) systems, but it also introduces unprecedented threats to the security and privacy of data, devices, and systems. In fact, ISAC operates in the wireless PHY and Medium Access Control (MAC) layers, where it is impossible to protect information with standard encryption techniques or with any other purely digital methodologies. The goals of this paper are threefold. First, it analyzes the threats to security and privacy posed by ISAC and how they intertwine in the wireless PHY layer within the framework of IoT and distributed pervasive communication systems in general. Secondly, it presents and discusses possible countermeasures to protect users’ security and privacy. Thirdly, it introduces an architectural proposal, discussing the available choices and tradeoffs to implement such countermeasures, as well as solutions and protocols to preserve the potential benefits of ISAC while ensuring data protection and users’ privacy. The outcome and contribution of the paper is a systematic argumentation on wireless PHY-layer privacy and security threats and their relation with ISAC, framing the boundaries that research and innovation in this area should respect to avoid jeopardizing people’s rights. Full article

Wireless body area networks (WBANs) have great potential to supply society with vital technical services, but the low power of network nodes severely hampers their development. To solve this problem, Energy-Efficient, a low-power cluster-based routing system intended for precise biological data gathering in WBANs, is presented in this study. This approach comprises three main stages: data aggregation, cluster head (CH) selection, and cluster creation. The suggested approach balances biosensor energy and optimizes energy usage by utilizing the modified snake swarm optimization algorithm (MSSOA) for routing and the adaptive binary bird swarm optimization algorithm (ABBSOA) for cluster formation and CH selection. The suggested technique outperforms the most recent WBAN routing protocols, including MT-MAC, ALOC, DHCO, and M-GWO, by using a power-balancing routing tree and considering biosensor distance and remaining energy. The experimental results demonstrate that the proposed ABBSOA-MSSOA model achieves a jitter protocol value of 0.3 ms at 100 nodes, a buffer occupancy ratio of 2.5%, a cluster lifetime of 600 s, a cluster building time of 12.2 s, an energy consumption of 42 mJ, a communication overhead of 8.3%, a packet delivery ratio of 98.2%, and an average end-to-end delay of 25 ms compared to other existing methods. Full article

Indoor Internet of Things (IoT) is considered as a crucial component of Industry 4.0, enabling devices and machine to communicate and share sensed data leading to increased efficiency, productivity, and automation. Increased energy efficiency is a significant focus within Industry 4.0, as it offers numerous benefits. To support this focus, we developed a hybrid switching mechanism to switch between energy harvesting techniques, ambient backscattering and Simultaneous Wireless Information and Power Transfer (SWIPT), which can be utilized within cooperative communications. To implement the proposed switching mechanism, we consider an indoor warehouse environment, where the moving sensor node transmits sensed data to the fixed relay located on the roof, which is then transmitted to an IoT gateway. The relay is equipped with the proposed switch to energize its communication capabilities while maintaining the expected quality of service at the IoT gateway. Simulation results illustrate the improved energy efficiency within the indoor communication setup while maintaining QoS at varying signal-to-noise (SNR) conditions. Full article

The Internet of Vehicles (IoV), a burgeoning technology, merges advancements in the internet, vehicle electronics, and wireless communications to foster intelligent vehicle interactions, thereby enhancing the efficiency and safety of transportation systems. Nonetheless, the continual and high-frequency communications among vehicles, coupled with regional limitations in system capacity, precipitate significant challenges in allocating wireless resources for vehicular networks. In addressing these challenges, this study formulates the resource allocation issue as a multi-agent deep reinforcement learning scenario and introduces a novel multi-agent actor-critic framework. This framework incorporates a prioritized experience replay mechanism focused on distributed execution, which facilitates decentralized computing by structuring the training processes and defining specific reward functions, thus optimizing resource allocation. Furthermore, the framework prioritizes empirical data during the training phase based on the temporal difference error (TD error), selectively updating the network with high-priority data at each sampling point. This strategy not only accelerates model convergence but also enhances the learning efficacy. The empirical validations confirm that our algorithm augments the total capacity of vehicle-to-infrastructure (V2I) links by 9.36% and the success rate of vehicle-to-vehicle (V2V) transmissions by 6.74% compared with a benchmark algorithm. Full article