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.

Managing resource allocation for optimum effectiveness at various levels of maintenance activities is always a challenging task. Optimizing maintenance resources enables an organization to set priorities towards achieving certain goals which are availability and reliability of the equipment for operational excellence. The purpose of this analysis is to determine the optimum resources allocation proportions among the failure modes and to identify the failure modes that have the greatest cumulative effect on the equipment's downtime. This paper presents a methodology using the Pareto analysis in conjunction with failure mode effect and criticality Analysis in maintenance resources optimization. The approach is based on ensuring all failure mode criticality number are considered to obtain the significant failures mode that you should focus on as a priority. The analysis shows that failure mode; FM5, FM 3, FM 2, FM 12, FM 7 and FM 13 are confirmation to the Pareto principle, identifying that most of the downtime of the Instrumentation Air Compressors originated from these failure modes.

Shopping at supermarkets has become a daily activity in urban areas of Enugu, Nigeria. However, there is always a huge rush in most mega supermarkets during times of discount offers, weekends and holidays resulting in long queues due to the barcode billing process. This research proposes a way of reducing the time spent at the billing counter using a radio frequency identification (RFID) smart-based shopping cart. To achieve this objective, an RFID tag, RFID reader, Arduino microcontroller and light-emitting diode (LED) display were used to develop a smart shopping trolley. RFID tag was placed on each of the eight products displayed for sale. RFID reader reads all the products that were placed on the cart and the details of the product such as the name, quantity, cost, and total cost was displayed on the LED. The smart shopping trolley system also incorporates an alarm system that triggers off when the RFID tag is removed from a product to avoid shoplifting and make the system secure for the owners of the supermarket. The result showed that the billing of the products was done directly from the smart shopping cart. The system was compared with the conventional barcode system and was found to overcome the limitations of time-consuming billing procedures.

The rapid adoption of electric vehicles (EVs) has intensified the demand for advanced energy storage technologies capable of delivering high energy density, high power density, enhanced safety, and extended service life. Although lithium-ion batteries remain the dominant energy storage technology for EVs, their limited power capability, thermal degradation, and accelerated aging under high transient loads constrain vehicle performance. Battery–supercapacitor hybrid energy storage systems (HESSs) have emerged as a promising solution by combining the high energy density of batteries with the high-power density and rapid charge–discharge capability of supercapacitors. However, the increasing complexity of HESS architecture requires intelligent energy management strategies to optimize power allocation, battery protection, thermal regulation, and overall system efficiency. Existing review papers primarily address individual aspects of HESS architecture, battery management, or artificial intelligence (AI)-based control, leaving a lack of a unified review integrating these topics. This paper addresses this gap by reviewing 181 publications published between 2020 and 2026, covering HESS architectures, conventional and AI-driven energy management strategies, machine learning, deep learning, reinforcement learning, battery state estimation, diagnostics, prognostics, thermal management, and fault diagnosis. The reviewed studies are critically analyzed to assess the impact of AI on battery lifetime, regenerative braking, charging performance, thermal behavior, and energy efficiency. The review further identifies emerging research directions, including explainable AI, digital twins, federated learning, edge intelligence, vehicle-to-grid integration, and cybersecurity-aware energy management. The findings indicate that AI-based approaches generally demonstrate greater adaptability, predictive capability, and battery protection than conventional methods under dynamic operating conditions, although challenges related to computational complexity, real-time implementation, data availability, explainability, cybersecurity, and standardization remain significant barriers to large-scale deployment.

Antennas and filters are two key components that make up the configuration of the radio frequency (RF) front end of any wireless communication system. While the antenna is responsible for transmitting and receiving signals in the system, the filter is tasked with selecting signals that fall within the system’s operating frequency band, while also rejecting spurious (out‐of‐band) signals from the system. Following the recent increasing trend toward device simplicity and miniaturization, it is desirable to integrate the antenna and the filter into a single component that achieves both the radiating (i.e., transmitting and receiving) and the filtering functions simultaneously. This integrated component is what is now known as a filtering antenna (or filtenna). The filtenna reduces a system’s prefiltering requirements and improves the noise performance of the system. It also finds application in overcoming the insufficiency of stand‐alone bandpass filters in suppressing interference in sidebands. This article covers the review of the current state of the art in the design and implementation of filtennas, the practical applications, recent developments, and challenges. Emerging research and future opportunities in filtering antenna design and implementation techniques are also investigated and reported.

Battery‐free radiofrequency (RF) and microwave systems have emerged as a promising solution for enabling long‐lifetime, maintenance‐free wireless sensing and communication platforms by eliminating conventional battery dependence. These systems operate by harvesting ambient or dedicated electromagnetic energy, converting it to usable electrical power, and utilizing ultralow‐power circuit and communication techniques to sustain sensing, processing, and wireless data transmission. This paper presents a comprehensive review of battery‐free energy‐efficient RF and microwave systems, covering the evolution of the field, energy harvesting and rectenna modeling techniques, power management strategies, ultralow‐power communication methods, and energy‐aware networking protocols. The review further examines key enabling circuit technologies, including subthreshold microwave integrated circuits, passive and backscatter‐based transmitters, and envelope‐detection receivers designed for nanowatt‐to‐microwatt operation. Recent experimental developments in battery‐free Internet of Things (IoT) nodes, wearable and implantable platforms, and smart‐environment monitoring systems are analyzed to highlight the transition from laboratory prototypes to real‐world deployments. Performance evaluation metrics such as RF‐to‐DC conversion efficiency, communication reliability, scalability, and system lifetime are discussed using representative experimental results from recent literature. The paper also identifies major open challenges, including energy intermittency, miniaturization constraints, and security considerations, and outlines future research directions such as integration with 5G/6G infrastructure, AI‐assisted energy management, and advanced materials for flexible and multiband harvesting structures. The review demonstrates that advances in rectenna codesign, ultralow‐power circuit architectures, adaptive communication techniques, and network‐level coordination are rapidly enabling practical battery‐free wireless systems, positioning RF and microwave energy harvesting as a key technology for sustainable massive IoT and long‐term autonomous sensing applications.