This paper proposes a profit maximization model for a Decentralized Electric Vehicle Supply Equipment (D-EVSE) equipped with a renewable energy sources system such as a solar energy system. We acknowledge a power connection to the central power grid when necessary. We allow EV to offer its surplus power as discharging power processes. We design a Decentralized Profit Maximization Algorithm (DPMA) to help D-EVSEs take profit from the electricity price variation during the day when selling or buying electricity respectively to EVs or from the grid or EVs as discharging processes. Finally, numerical simulations with MATLAB are conducted to prove the effectiveness of our proposed solution.

In this paper, we address a decentralized power production and management system based on Game Theory (GT) for Electric Vehicles’ (EVs’) interplay with a Decentralized Electric Vehicle Supply Equipment (D-EVSE) located at the public supply station. Renewable energy production such as solar energy (PV) is considered as the main power source for our D-EVSE and we consider the connection to the grid when the solar renewable energy system is failing to respond to the demand. We propose a decentralized GT (D-GT) scheme aiming to optimize the EVs’ interaction with the D-EVSE considering both EVs’ satisfaction as well as the D-EVSEs’ stability. Also, the D-GT model is used to choose the optimal available solution for EV charging or discharging services that fulfill predefined constraints. A realistic scenario is considered as a testbed for our D-GT optimization model. Simulation results indicate that the proposed model can manage and control the interaction between EVs and D-EVSEs efficiently.

In this paper, we consider the decentralization of power generation and management problems in electric vehicle supply equipment (D-EVSE). We propose two algorithms to manage the interaction between Electric Vehicles (EVs) and D-EVSEs while maximizing EV driversb’ satisfaction in terms of reducing the waiting time for charging or discharging services and minimizing the stress level for D-EVSEs. The first algorithm is used to schedule charging or discharging service for EVs. The second algorithm is used to manage unscheduled EV needs at D-EVSEs. Simulation results using realistic scenarios are conducted to validate the proposed schemes and demonstrate their efficiency and effectiveness while satisfying the defined constraints. Furthermore, we apply both D-EVSE scheduled and unscheduled schemes to Ontario highways 174, 416, and 417 in the city of Ottawa as a case study.

In this paper, we address a decentralized management system based on noncooperative Game Theory (GT) for Electric Vehicles’ (EVs’) interplay with a Decentralized Electric Vehicle Supply Equipment (D-EVSE) located at the public supply station. Renewable energy production, such as photovoltaic energy (PV), is considered as the only power source for our D-EVSE. We propose a decentralized GT (D-GT) model aiming to optimize the EVs’ interaction with the D-EVSE considering both EVs’ satisfaction as well as the D-EVSEs’ stability. Also, the D-GT model is used to choose the optimal available solution for EV charging or discharging processes that fulfill predefined constraints. Simulation results indicate that the proposed model can manage and control the interaction between EVs and D-EVSEs efficiently and effectively.

In this paper, we consider the Electric Vehicles (EVs) interaction with a decentralized energy storage system (DESS) located at the public supply station. To manage the EV charging and discharging process, we propose a scheduling algorithm aiming to maximize the EV drivers' satisfaction and minimize the DESSs' stress level. Simulation results using realistic scenarios are conducted to validate the proposed approach and demonstrate its efficiency and effectiveness while satisfying the defined constraints.

Automated mobile microscopy in Internet of Things (IoT) networks is essential for scaling malaria screening in resource-constrained environments. Deploying standard convolutional architectures here introduces severe adversarial vulnerabilities. Post-Training Quantization (PTQ) mitigates hardware constraints by converting floating-point models to 8-bit integers (INT8); however, its impact on clinical safety and security remains unexplored. This study presents an adversarial audit of quantized Vision Transformers for medical edge deployment. We evaluated a Swin-Tiny transformer against ViT-Tiny and MobileNetV3 baselines using a 27,558-image malaria dataset and an out-of-distribution (OOD) White Blood Cell dataset. Our findings redefine the “Quantization Shield” hypothesis. PTQ compresses the Swin model by 3.9× (to 27.89 MB) with a negligible 0.11% accuracy drop, maintaining statistical reliability on OOD tests. However, the hypothesized architectural resilience shatters under white-box Projected Gradient Descent (PGD) attacks. Despite robustness against single-step attacks, both MobileNetV3 and the INT8 Swin-Tiny collapse to 0.00% accuracy under iterative PGD. Conversely, the quantized Swin-Tiny resists black-box transfer attacks from a surrogate, maintaining 81.00% accuracy. We conclude that while quantized Vision Transformers meet mobile sensor constraints, integer quantization provides zero innate defense against targeted iterative perturbations, exposing a critical vulnerability in diagnostic IoT networks.