The proliferation of electric vehicles (EVs), owing to their advantages over internal combustion engine vehicles, has introduced many challenges, due to the lack of charging infrastructure that can handle such a large surge of EVs. Therefore, alternative feasible charging solutions, such as EV-to-Grid (V2G) and EV-to-EV (V2V) charging have gained prominence thanks to the bidirectional charger. However, there are several challenges hindering the adoption of V2V energy sharing solutions. The existing frameworks that detail the main aspects in energy management protocols emphasize solely on the EV integration with the grid, with the assumption of the grid availability and capability of supporting V2V energy sharing. In this article, a novel holistic energy management framework for an efficient V2V energy sharing is proposed. The proposed framework offers a complete overview of the different stages in the V2V charging problem and introduces possible solutions for each stage and its integration with other stages to form a comprehensive V2V solution, that is not only cost-effective but also maximizes user satisfaction and social welfare, while simultaneously fulfills the highest number of energy demands.

Artificial intelligence (AI) has the potential to revolutionize healthcare by automating the detection and classification of events and anomalies. In the scope of this work, events and anomalies are abnormalities in the patient’s data, where the former are due to a medical condition, such as a seizure or a fall, and the latter are erroneous data due to faults or malicious attacks. AI-based event and anomaly detection (EAD) and their classification can improve patient outcomes by identifying problems earlier, enabling more timely interventions while minimizing false alarms caused by anomalies. Moreover, the advancement of Medical Internet of Things (MIoT), or wearable devices, and their high processing capabilities facilitated the gathering, AI-based processing, and transmission of data, which enabled remote patient monitoring, and personalized and predictive healthcare. However, it is fundamental in healthcare to ensure the explainability of AI systems, meaning that they can provide understandable and transparent reasoning for their decisions. This article proposes an online EAD approach using a lightweight autoencoder (AE) on the MIoT. The detected abnormality is explained using KernelSHAP, an explainable AI (XAI) technique, where the explanation of the abnormality is used, by an artificial neural network (ANN), to classify it into an event or anomaly. Intensive simulations are conducted using the Medical Information Mart for Intensive Care (MIMIC) data set for various physiological data. Results showed the robustness of the proposed approach in the detection and classification of events, regardless of the percentage of the present anomalies.

Electric Vehicles (EVs) are being widely adopted to completely replace vehicles with internal combustion engines. However, the development of charging infrastructure to support the growing number of EVs has been lacking primarily due to the high cost of installation. Additionally, the immature and non-uniform deployment of charging stations has led to the absence of charging infrastructure in areas such as highways and rural areas. As a result, emerging concepts such as, EV-to-Home (V2H), EV-to-Grid (V2G) and EV-to-EV (V2V) charging have gained prominence. In this paper, the V2V energy sharing concept is exploited, where an intelligent and comprehensive framework to manage and allocate energy between EVs is proposed. This work presents a realistic modeling of the V2V energy sharing problem and proposes a two-layer matching approach that can efficiently match the EVs. The proposed approach not only optimizes the cost, but also the time, system energy efficiency, user satisfaction, and social welfare; hence making the approach more realistic and inclusive. Gale-Shapley is utilized to produce stable matchings, in the first layer, whereas a user-satisfaction model is devised to ensure realistic matchings, in the second layer. Additionally, a real-life dataset is developed from commercially available EVs and the performance of the system is evaluated using this dataset, in addition to realistic parameters derived from real-life data. The proposed approach is compared with a benchmark, where the results show that efficient, realistic, and effective V2V matches are achievable, paving the way for the adoption of such framework.

In large-scale mobile crowd sensing systems, multi-task-oriented worker selection has shown an increased efficiency in workers’ allocation. However, existing solutions for multi-task selection mainly focus on meeting the requirements of the available tasks and fall short in considering workers’ preferences. Assigning workers to their preferred tasks should substantially improve the possibility that they will perform the tasks assigned to them, which will improve the quality of the sensing outcome. In this paper, we propose to use the Gale–Shapley matching game selection to allocate multiple workers to multiple tasks based on the preferences of both the tasks and workers. It aims at maximizing the level of satisfaction for the workers, by assigning them to their most preferred tasks, as well as maximizing the Quality of Service (QoS) and the completion confidence of the tasks. Simulations based on the real-life dataset show that the proposed approach outperforms other multi-task allocation benchmark in terms of the completion confidence of the tasks, the QoS of the sensing outcome, and the workers’ satisfaction level without compromising on their traveling distance.

Due to its wide band gap, 4H-SiC is potentially capable of sustained operation at temperatures well above 600 °C, but current devices are limited to lower temperatures by the stability of the metallization and passivation layers. SiC bipolar transistors are capable of operation at temperatures above 300 °C, as they do not have an oxide layer under high electric field and hence do not suffer from oxide reliability issues. In this paper, we describe bipolar digital integrated circuits on semi-insulating 4H-SiC that operate over a wide range of supply voltage and temperature, demonstrating the potential of SiC for high-temperature small-scale integrated-circuit applications.