This paper addresses coverage loss and rapid energy depletion issues for wireless livestock sensor networks by proposing a UAV-based energy-efficient reconfigurable routing (UBER) scheme for smart wireless livestock sensor networking applications. This routing scheme relies on a dynamic residual energy thresholding strategy, robust cluster-to-UAV link formation, and UAV-assisted network coverage and recovery mechanism. The performance of UBER was evaluated using low, normal and high UAV altitude scenarios. Performance metrics employed for this analysis are network stability (NST), load balancing ratio (LBR), and topology fluctuation effect ratio (TFER). Obtained results demonstrated that operating with a UAV altitude of 230 m yields gains of 31.58%, 61.67%, and 75.57% for NST, LBR, and TFER, respectively. A comparative performance evaluation of UBER was carried out with respect to hybrid heterogeneous routing (HYBRID) and mobile sink using directional virtual coordinate routing (MS-DVCR). The performance indicators employed for this comparative analysis are energy consumption (ENC), network coverage (COV), received packets (RPK), SN failures detected (SNFD), route failures detected (RFD), routing overhead (ROH), and end-to-end delay (ETE). With regard to the best-obtained results, UBER recorded performance gains of 46.48%, 47.33%, 15.68%, 19.78%, 46.44%, 29.38%, and 58.56% over HYBRID and MS-DVCR in terms of ENC, COV, RPK, SNFD, RFD, ROH, and ETE, respectively. The results obtained demonstrated that the UBER scheme is highly efficient with competitive performance against the benchmarked CBR schemes.

Adopting Wireless Power Transfer (WPT) technology to an Unmanned Aerial Vehicle (UAV) involves adding extra components, which may impact the drone’s overall weight and performance. This paper aims to enhance UAV performance by designing a lightweight WPT system through a parametric design approach. This method explores novel perspectives by identifying the most suitable combination of parameters in terms of efficiency, weight, and feasibility. Various parameters such as the compensation topology, number of turns of coils, and frequency were studied. The system was analyzed through a coupled simulation approach, where electromagnetic modeling of the coupler using the finite element method (FEM) was combined with electrical circuit simulations, providing a more accurate assessment of the overall system efficiency and behavior considering variations in the coupling factor due to misalignment. A prototype of the resulting configuration was designed and tested experimentally versus misalignment at reduced power using a specific test bench. The results show a 70% efficiency level with SP compensation that was improved to 80% with SS compensation.

An efficient diagnosis method dedicated to embedded wiring network based on reflectometry technique is developed in this study. The proposed methodology is based on the two complementary steps. In the first step, the time‐domain reflectometry (TDR) method is simulated, by RLCG ( R : resistance, L : inductance, C : capacitance and G : conductance) circuit model and the numerical finite‐difference time‐domain method, and at the same time the datasets are created. In the second step, the support vector machine (SVM) algorithm is combined with a principal component analysis to identify the faults on wiring network from the TDR response. Two types of SVM models have been used in the diagnosis procedure: SVM classifiers and SVM regression models. In order to illustrate the performances and the feasibility of the proposed approach, numerical and experimental results are presented.

The identification and resolution of faults, along with the proactive maintenance of wiring networks, are essential for ensuring the reliable, safe, and energy-efficient operation of industrial systems. Research in this domain advances fault detection and prevention, thereby enhancing overall safety, reliability, efficiency, and cost-effectiveness. Time-domain reflectometry (TDR) responses are extensively utilized for this purpose; however, their inherent nonlinearity and complexity pose significant challenges in interpretation. We propose an innovative solution to this problem that is aimed at diagnosing the state of the wiring network: integrating TDR responses with twin support vector machines (TWSVMs) by utilizing kernel functions. The effectiveness and feasibility of the TDR and TWSVM-based fault diagnosis methodology are substantiated through its application to two prevalent wiring network configurations, demonstrating superior performance compared to other fault diagnosis techniques.

This paper introduces a novel end-to-end fault diagnosis framework that integrates Bidirectional Long Short-Term Memory (BiLSTM) networks with Time-Domain Reflectometry (TDR) for the detection, characterization, and localization of wiring faults. The method is designed to operate directly on TDR signals, requiring no manual feature extraction or preprocessing. A forward model is used to simulate TDR responses across various fault scenarios and topologies, serving as the basis for supervised learning. The proposed BiLSTM-based model is trained and validated on common wiring network topologies, demonstrating high diagnostic performance. Experimental results show a diagnostic accuracy of 98.97% and a macro-average sensitivity exceeding 98%, outperforming conventional machine learning techniques. In addition to technical performance, the proposed approach supports sustainable and predictive maintenance strategies by reducing manual inspection efforts and enabling real-time automated diagnostics.