Wireless Visual Sensor Networks (WVSNs) are commonly using information technologies of modern networking and computing platforms. Today, visual network computing applications are faced with high demand of powerful network functionalities and performances. This paper proposes specific WVSN nodes, which are able to sense surrounding signals coming from the patient in the rehabilitation room and perform as local computations wirelessly communicated within the considered WVSNs. Assuming the WVSN platform for the rehabilitation supervision of patients, this paper discusses the specifications of the concept and the development of the WVSN nodes relying on KINECT and Raspberry Pi 3 (RPi 3) boards. Several technologies, such as RPi 3, Kinect, and ID sensor, are utilized in the proposed platform to realize sensors, image-capturing units, and processing cores. This collaborative platform consists of three nodes that are designed in three processing cores using the entire workflow to support the development steps. The communication of the elements between one to another and between the components takes place via Wi-Fi. The implementation of a preprocessing stage for component integration and the development of software development kit tools are necessary. In its final part, the paper summarizes the outlines of the proposed platform in different scenarios which run on nodes; it also provides evaluation results that prove the feasibility of the approach.

The transition from Internal Combustion Engine vehicles to Electric Vehicles presents challenges for fleet managers in terms of adapting operational processes and systems. One prominent challenge is the Electric Vehicle Routing Problem, which requires careful consideration of battery monitoring, efficient route planning, charging infrastructure availability, and vehicle performance management. The goal is to alleviate range anxiety and ensure effective fleet management. In this context, we propose a Reinforcement Learning approach in conjunction with graph-based modeling to solve Electric Vehicle Routing Problem with Time Window. This paper provides a novel approach addressing the need for efficient and sustainable electric vehicle fleet management. Our aim is to minimize the distance traveled while serving customers within their time windows using the combination of Structure2vect and Double Deep Q-Network. Real-world data from a public utility fleet company in Tunisia is utilized to evaluate the proposed model, and comparisons are made with conventional benchmarking strategies and other Reinforcement Learning approaches. The results highlight the effectiveness of the proposed model achieving a reduction up to 50% in traveled distance while demonstrating enhanced computational efficiency through reduced complexity and optimized runtime. Moreover, the obtained model can be directly applied to treat the large-scale adoption of electric vehicles in fleets.

The rapid expansion of Electric Vehicle (EV) charging capacities, driven by the surge in transportation electrification, has a direct impact on the electric power system, particularly the Low Voltage (LV) grid. Consequently, there is a pressing need to redesign energy management systems, placing a greater emphasis on mitigating the effects of EV charging. In this article, we crafted an online grid-level Model Predictive Control (MPC) incorporating Machine Learning (ML) prediction enhanced with Singular Spectrum Analysis (SSA). ML models predicts the future EV charging demands, which are the key factors for the MPC for managing efficiently a large-scale charging infrastructure, aiming to mitigate EV charging impacts, specifically phase unbalance and overloading. The MPC results, especially on phase unbalance metrics, mirrored in a predictive manner those of offline/no forecast optimization from our previous work. A week-long MPC simulation comparison with uncontrolled case showcased a 63% reduction in peak demand duration, a 68% decrease in Phasing Unbalance Index (PUI), and a 45% reduction in Voltage Unbalance Factor (VUF). The minimum voltage unbalance improved from 0.897 pu to 0.913 pu, reducing out-of-standard drops duration by 21%. Lastly, we observed a 20% cut in lines’ losses, equating to a 50 kgCO ₂ reduction within a week.

With the growth of electric vehicles’ (EVs) deployment as a substitute for internal combustion engine vehicles, the impact of this kind of load on the distribution grid cannot be neglected. An in-depth study needs to be performed on a regional basis to investigate the impacts of electric vehicle (EV) charging on the grid for each country’s grid configuration and specifications, in order to be able to reduce them. In this work, we built a case study of a charging infrastructure of a Tunisian workplace parking lot, by combining different measured data and simulations using OpenDSS and Matlab. The first objective was to analyze the integration impacts on the Tunisian low-voltage (LV) grid including phase unbalance, voltage drop, harmonics, and power losses. We found that 10 metric tons of carbon dioxide (${\mathrm{MtCO}}_2$) in yearly emissions were caused by power losses, and 50% of these emissions came from harmonic losses, which can be avoided by active and passive filtering. The second objective was to decrease phase unbalance by formulating an optimization problem and solving it by combining a genetic algorithm (GA) and a pattern search (PS) in the Matlab environment. The GA returned interesting results by balancing the phases, and the addition of PS as a hybrid function reduced the convergence speed by 38%. Moreover, the optimization led to a reduction of 83% in the neutral current maximum value, a reduction of 67% in the violation period of the voltage drop, a minimum voltage drop of 0.94 pu. and kept the total current consumption within a fixed limit. The developed model can be adapted to any similar workplace parking facility in Tunisia that is equipped with an EV charging infrastructure.