Search Results (331)

Wireless sensor networks provide a solution for structural health monitoring of aviation pipelines. In the installation environment of aviation pipelines, widespread vibrations can be utilized to extract energy through vibration energy harvesting technology to achieve self-powering of sensors. This study analyzed the vibration characteristics of aviation pipeline structures. The vibration characteristics and influencing factors of typical aviation pipeline structures were obtained through simulations and experiments. An N-shaped symmetric vibration energy harvester was designed considering the limited space in aviation pipeline structures. To improve the efficiency of electrical energy extraction from the vibration energy harvester, expand its operating frequency band, and achieve efficient vibration energy harvesting, this study first analyzed its natural frequency characteristics through theoretical analysis. Finite element simulation software was then used to analyze the effects of the external excitation acceleration direction, mass and combination of counterweights, piezoelectric sheet length, and piezoelectric material placement on the output power of the energy harvester. The structural parameters of the vibration energy harvester were optimized, and the optimal working conditions were determined. The experimental results indicate that the N-shaped symmetric vibration energy harvester designed and optimized in this study improves the efficiency of vibration energy harvesting and can be arranged in the limited space of aviation pipeline structures. It achieves efficient energy harvesting under multi-modal conditions, different excitation directions, and a wide operating frequency band, thus meeting the practical application requirement and engineering feasibility of aircraft design. Full article

Terahertz (THz) communications and simultaneous wireless information and power transfer (SWIPT) hold the potential to energize battery-less Internet-of-Things (IoT) devices while enabling multi-gigabit data transmission. However, severe path loss, blockages, and rectifier nonlinearity significantly hinder both throughput and harvested energy. Additionally, high-power THz beams pose safety concerns by potentially exceeding specific absorption rate (SAR) limits. We propose a sensing-adaptive power-focusing (APF) framework in which a reconfigurable intelligent surface (RIS) embeds low-rate THz sensors. Real-time backscatter measurements construct a spatial map used for the joint optimisation of (i) RIS phase configurations, (ii) multi-tone SWIPT waveforms, and (iii) nonlinear power-splitting ratios. A weighted MMSE inner loop maximizes the data rate, while an outer alternating optimisation applies semidefinite relaxation to enforce passive-element constraints and SAR compliance. Full-stack simulations at 0.3 THz with 20 GHz bandwidth and up to 256 RIS elements show that APF (i) improves the rate–energy Pareto frontier by 30–75% over recent adaptive baselines; (ii) achieves a 150% gain in harvested energy and a 440 Mbps peak per-user rate; (iii) reduces energy-efficiency variance by half while maintaining a Jain fairness index of 0.999;; and (iv) caps SAR at 1.6 W/kg, which is 20% below the IEEE C95.1 safety threshold. The algorithm converges in seven iterations and executes within <3 ms on a Cortex-A78 processor, ensuring compliance with real-time 6G control budgets. The proposed architecture supports sustainable THz-powered networks for smart factories, digital-twin logistics, wire-free extended reality (XR), and low-maintenance structural health monitors, combining high-capacity communication, safe wireless power transfer, and carbon-aware operation for future 6G cyber–physical systems. Full article

The increasing demand for sustainable and renewable energy solutions has made radio frequency energy harvesting (RFEH) a promising technique for powering low-power electronic devices. RFEH captures ambient RF signals from wireless communication systems, such as mobile networks, Wi-Fi, and broadcasting stations, and converts them into usable electrical energy. This approach offers a viable alternative for battery-dependent and hard-to-recharge applications, including streetlights, outdoor night/security lighting, wireless sensor networks, and biomedical body sensor networks. This article provides a comprehensive review of the RFEH techniques, including state-of-the-art rectenna designs, energy conversion efficiency improvements, and multi-band harvesting systems. We present a detailed analysis of recent advancements in RFEH circuits, impedance matching techniques, and integration with emerging technologies such as the Internet of Things (IoT), 5G, and wireless power transfer (WPT). Additionally, this review identifies existing challenges, including low conversion efficiency, unpredictable energy availability, and design limitations for small-scale and embedded systems. A critical assessment of current research gaps is provided, highlighting areas where further development is required to enhance performance and scalability. Finally, constructive recommendations for future opportunities in RFEH are discussed, focusing on advanced materials, AI-driven adaptive harvesting systems, hybrid energy-harvesting techniques, and novel antenna–rectifier architectures. The insights from this study will serve as a valuable resource for researchers and engineers working towards the realization of self-sustaining, battery-free electronic systems. Full article

Smart agriculture is transforming traditional farming by integrating advanced sensor-based systems, intelligent control technologies, and sustainable energy solutions to meet the growing global demand for food while reducing environmental impact. This review presents a comprehensive analysis of recent innovations in smart agriculture, focusing on the deployment of IoT-based sensors, wireless communication protocols, energy-harvesting methods, and automated irrigation and fertilization systems. Furthermore, the paper explores the role of artificial intelligence (AI), machine learning (ML), computer vision, and big data analytics in monitoring and managing key agricultural parameters such as crop health, pest and disease detection, soil conditions, and water usage. Special attention is given to decision-support systems, precision agriculture techniques, and the application of remote and proximal sensing technologies like hyperspectral imaging, thermal imaging, and NDVI-based indices. By evaluating the benefits, limitations, and emerging trends of these technologies, this review aims to provide insights into how smart agriculture can enhance productivity, resource efficiency, and sustainability in modern farming systems. The findings serve as a valuable reference for researchers, practitioners, and policymakers working towards sustainable agricultural innovation. Full article

The integration of multi-source energy harvesting (EH) systems into silicon presents a promising avenue for powering autonomous, low-power devices, particularly in applications such as the Internet of Things (IoT), biomedical implants, and wireless sensor networks, where power efficiency and small-size solutions are crucial. This review provides a detailed technical assessment of energy harvesting schemes—including photovoltaic, mechanical, thermoelectric, and radio frequency energy harvesting—and the integration of their associated electronic circuits into silicon integrated solutions. The EH systems are critically analyzed based on their architectures, the number and type of input sources, and key performance metrics such as energy conversion efficiency, output power delivered to loads, silicon area footprint, and degree of integration (e.g., reliance on external components). By examining current advancements and practical implementations, crucial design parameters are assessed for state-of-the-art integrated silicon energy harvesting systems. Furthermore, based on current trends, future research directions are outlined to enhance EH efficiency, reliability, and scalability, paving the way for fully integrated silicon-based EH systems for the next-generation self-powered electronic devices. Full article

Wireless Sensor Networks (WSNs) are widely used for environmental data collection; however, their reliance on battery power significantly limits network longevity. While energy harvesting technologies provide a sustainable power solution, conventional approaches often fail to efficiently utilize surplus energy, leading to performance constraints. This paper proposes an energy-efficient dual-mode data collection scheme that integrates Long Range Wide Area Network (LoRaWAN) and Bluetooth Low Energy (BLE) in an energy-harvesting WSN environment. The proposed method dynamically adjusts sensing intervals based on harvested energy predictions and reserves energy for urgent data transmissions. Urgent messages are transmitted via BLE using multi-hop routing with redundant paths to ensure reliability, while periodic environmental data is transmitted over LoRaWAN in a single hop to optimize energy efficiency. Simulation results demonstrate that the proposed scheme significantly enhances data collection efficiency and improves urgent message delivery reliability compared to existing approaches. Future work will focus on optimizing energy consumption for redundant urgent transmissions and integrating error correction mechanisms to further enhance transmission reliability. Full article

Consumer interest in medicinal and aromatic herbs is on the rise, with buyers increasingly concerned about the microbiological quality of nutraceutical and aromatic plants. The use of Unmanned Aerial Vehicles (UAVs) and sensor technology allows for high-resolution crop monitoring, particularly in the production of rosemary and sage in Grotte (Italy), Agrigento District. The aim of this study is to evaluate the efficacy of UAV-based time series remote sensing data and multimodal data fusion using RGB and multispectral sensors in rosemary and sage harvesting time individuation and the microbiological quality of these nutraceutical and aromatic plants before and after an innovative and sustainable drying process. The multispectral data were acquired with a DJI multispectral camera mounted on a Phantom 4 UAV. The use of drones in the aromatic plant crops can lead to improved efficiency, productivity, and profitability for farmers and businesses. Italian producers follow strict hygiene regulations to reduce bacterial contamination, particularly during the crucial drying process. A rapid drying method at low temperature using a dryer powered by a photovoltaic renewable energy source (RES) helps preserve the quality of the plants. Real-time monitoring of the drying process is enabled through a system based on wireless sensor networks (WSN), providing valuable data on moisture content, drying rates, and microbial stability. Overall, the innovative use of drones, sensor technology, and renewable energy sources in the production of aromatic herbs like rosemary and sage holds great potential for enhancing crop quality, shelf life, and overall sustainability in the chain food industry. Full article

The increasing demand for self-powered sensors and wireless sensor networks, particularly for environmental and structural health monitoring applications, is driving the need for energy harvesting from natural sources. To fill a gap in the scientific literature, this study quantitatively investigates the advantages of hybrid energy harvesters, which utilize multiple energy sources, compared to single-source harvesters. The analysis leverages a real-world dataset collected from a meteorological station in Cervinara, Southern Italy. The measured data are processed to estimate the energy that can be recovered from solar, wind, and rain sources using energy harvesters designed to supply low-power electronic devices. The available energy serves as the basis for optimizing the sizing of a hybrid energy harvester that effectively integrates the aforementioned energy sources. The system sizing, carried out under the constraint of ensuring a continuous and uninterrupted power supply to the load, quantifies the benefits of using a hybrid harvester over a single-source harvester. The results show that one of the main advantages of the hybrid solution is the reduction in the size of the storage device, enabling the replacement of rechargeable batteries with supercapacitors, which offer both environmental and reliability benefits. Full article

The rapid proliferation of Internet of Things (IoT) devices and applications has led to an increasing demand for energy-efficient and secure communication in wireless sensor networks. In this article, we firstly propose an intelligent approach to maximize the energy efficiency of the UAV in a secure sensor network with wireless power transfer (WPT). All sensors harvest energy via downlink signal and use it to transmit uplink information to the UAV. To ensure secure data transmission, the UAV needs to optimize the transmission parameters to decode received information under malicious interference from an attacker. Code Division Multiple Access (CDMA) is adopted to improve uplink communication robustness. To maximize the UAV’s energy efficiency in data collection tasks, we formulate a constrained optimization problem that jointly optimizes charging power, charging duration, and data transmission duration. Applying Deep Deterministic Policy Gradient (DDPG) algorithm, we train an action policy to dynamically determine near-optimal transmission parameters in real time. Numerical results validate the superiority of proposed intelligent approach over exhaustive search and gradient ascent techniques. This work provides some important guidelines for the design of green secure wireless-powered sensor networks. Full article

Energy harvesting wireless sensor networks (EH-WSNs) appear as the fundamental backbone of research that attempts to expand the lifespan and efficiency of sensor networks positioned in resource-constrained environments. This review paper provides an in-depth examination of latest developments in this area, highlighting the important components comprising routing protocols, energy management plans, cognitive radio applications, physical layer security (PLS), and EH approaches. Across a well-ordered investigation of these features, this article clarifies the notable developments in technology, highlights recent barriers, and inquires avenues for future revolution. This article starts by furnishing a detailed analysis of different energy harvesting methodologies, incorporating solar, thermal, kinetic, and radio frequency (RF) energy, and their respective efficacy in non-identical operational circumstances. It also inspects state-of-the-art energy management techniques aimed at optimizing energy consumption and storage to guarantee network operability. Moreover, the integration of cognitive radio into EH-WSNs is acutely assessed, highlighting its capacity to improve spectrum efficiency and tackle associated technological problems. The present work investigates ground-breaking methodologies in PLS that uses energy-harvesting measures to improve the data security. In this review article, these techniques are explored with respect to classical encryption and discussed from network security points of view as well.The assessment furthers criticizes traditional routing protocols and their significance in EH-WSNs as well as the balance that has long been sought between energy efficiency and security in this space. This paper closes with the importance of continuous research to tackle existing challenges and to leverage newly available means as highlighted in this document. In order to adequately serve the increasingly changing requirements of EH-WSNs, future research will and should be geared towards incorporating AI techniques with some advanced energy storage solutions. This paper discusses the integration of novel methodologies and interdisciplinary advancements for better performance, security, and sustainability for WSNs. Full article

The future Internet of Things (IoT) will consist of energy harvesting devices and Unmanned Aerial Vehicles (UAVs) to support applications in remote areas. However, as UAVs communicate with IoT devices using broadcast channels, information leakage emerges as a critical security threat. This paper considers the problem of maximizing the minimum secrecy rate in an energy harvesting IoT network supported by two UAVs, where one acts as a server to collect data from devices, and the other is an eavesdropper to intercept data transmission. It presents a novel Mixed-Integer Nonlinear Program (MINLP), which we then linearize into a Mixed-Integer Linear Program (MILP) problem. It also proposes a heuristic solution called Fly Nearest Location (FNL). Both solutions determine (i) the UAV server’s flight routing, flight time, and computation time, as well as (ii) the energy usage and operation mode of IoT devices. Our results show that FNL achieves on average 78.15% of MILP’s performance. Full article

This paper investigates the feasibility of harvesting Radio Frequency (RF) energy from the Wi-Fi frequency band to power low-power Internet-of-Things (IoT) devices. With the increasing prevalence of IoT applications and wireless sensor networks (WSNs), there is a critical need for sustainable energy sources that can extend the operational lifespan of these devices, particularly in remote locations, where access to reliable power supplies is limited. The paper describes the design, simulation, and fabrication of a dual-band antenna capable of operating at 2.4 GHz and 5 GHz, the frequencies used by Wi-Fi. The simulation and experimental results show that the proposed design is efficient based on the reflection coefficient. Using a high-frequency simulator, we developed two C-shaped and an F-shaped microstrip antenna design, optimized for impedance matching and efficient RF–DC conversion.The captured RF energy is converted into usable electrical power that can be directly utilized by low-power IoT devices or stored in batteries for later use. The paper introduces an efficient design for dual-band antennas to maximize the reception of Wi-Fi signals. It also explains the construction of an impedance-matching network to reduce signal reflection and improve power transfer efficiency. The results indicate that the proposed antennas can effectively harvest Wi-Fi energy, providing a sustainable power source for IoT devices. The practical implementation of this system offers a promising solution to the energy supply challenges faced by remote and low-power IoT applications, paving the way for more efficient and longer-lasting wireless sensor networks. Full article

Micro-scale kinetic energy harvesters are in large demand to function as sustainable power sources for wireless sensor networks and the Internet of Things. However, one of the challenges associated with them is their inability to easily tune the frequency during the manufacturing process, requiring devices to be custom-made for each application. Previous attempts have either used active tuning, which consumes power, or passive devices that increase their energy footprint, thus decreasing power density. This study involved developing a novel passive method that does not alter the device footprint or power density. It involved creating a proof mass with an array of chambers or cavities that can be individually filled with liquid to alter the overall proof mass as well as center of gravity. The resonant frequency of a rectangular cantilever can then be altered by changing the location, density, and volume of the liquid-filled mass. The resolution can be enhanced by increasing the number of chambers, whereas the frequency tuning range can be increased by increasing the amount of liquid or density of the liquids used to fill the cavities. A piezoelectric cantilever with a 340 Hz initial resonant frequency was used as the testing device. Liquids with varying density (silicone oil, liquid sodium polytungstate, and Galinstan) were investigated. The resonant frequencies were measured experimentally by filling various cavities with these liquids to determine the tuning frequency range and resolution. The tuning ranges of the first resonant frequency mode for the device were 142–217 Hz, 108–217 Hz, and 78.4–217 Hz for silicone oil, liquid sodium polytungstate, and Galinstan, respectively, with a sub Hz resolution. Full article

Agricultural sustainability is becoming more and more important for human health. Wireless sensing technology could provide smart monitoring in real time for different parameters in planting, breeding, and the food supply chain with advanced sensors such as flexible sensors; wireless communication networks such as third-, fourth-, or fifth-generation (3G, 4G, or 5G) mobile communication technology networks; and artificial intelligence (AI) models. Many sustainable, natural, renewable, and recycled facility energies such as light, wind, water, heat, acoustic, radio frequency (RF), and microbe energies that exist in actual agricultural systems could be harvested by advanced self-powered technologies and devices using solar cells, electromagnetic generators (EMGs), thermoelectric generators (TEGs), piezoelectric generators (PZGs), triboelectric nanogenerators (TENGs), or microbial full cells (MFCs). Sustainable energy harvesting to the maximum extent possible could lead to the creation of sustainable self-powered wireless sensing devices, reduce carbon emissions, and result in the implementation of precision smart monitoring, management, and decision making for agricultural production. Therefore, this article suggests that proposing and developing a self-powered wireless sensing system for sustainable agriculture (SAS) would be an effective way to improve smart agriculture production efficiency while achieving green and sustainable agriculture and, finally, ensuring food quality and safety and human health. Full article

Wireless rechargeable sensor networks (WRSNs) have emerged as a critical infrastructure for monitoring and collecting data in large-scale and dynamic environments. The energy autonomy of sensor nodes is crucial for the sustained operation of WRSNs. This paper presents a comprehensive survey on the state-of-the-art approaches and technologies in on-demand energy provisioning in large-scale WRSNs. We explore various energy harvesting techniques, storage solutions, and energy management strategies tailored to the unique challenges posed by the dynamic and resource-constrained nature of WRSNs. This survey categorizes existing literature based on energy harvesting sources, including solar, kinetic, and ambient energy, and discusses advancements in energy storage technologies such as supercapacitors and rechargeable batteries. Furthermore, we investigate energy management techniques that adaptively balance energy consumption and harvesting, optimizing the overall network performance. In addition to providing a thorough overview of existing solutions, this paper identifies opportunities and challenges in the field of on-demand energy provisioning for large-scale WRSNs. By synthesizing current research efforts, this survey aims to provide insight to researchers and policymakers in understanding the landscape of on-demand energy provisioning in large-scale WRSNs. The insights gained from this study pave the way for future innovations and contribute to the development of sustainable and self-sufficient wireless sensor networks, critical for the advancement of applications such as environmental monitoring, precision agriculture, and smart cities. Full article