The global market share of electric vehicles (EVs) is on the rise, resulting in a rapid increase in their charging demand in both spatial and temporal domains. A remedy to shift the extra charging loads at peak hours to off-peak hours, caused by charging EVs at public charging stations, is an online pricing strategy. This paper presents a novel combinatorial online pricing strategy that has been established upon a reward-based model to prevent network instability and power outages. In the proposed solution, the utility provides incentives to the charging stations for their contributions in the EVs charging load shifting. Then, a constraint optimization problem is developed to minimize the total charging demand of the EVs during peak hours. To control the EVs charging demands in supporting utility's stability and increasing the total revenue of the charging stations, treated as a multi-agent framework, an online reinforcement learning model is developed which is based on the combination of an adaptive heuristic critic and recursive least square algorithm. The effective performance of the proposed model is validated through extensive simulation studies such as qualitative, numerical, and robustness performance assessment tests. The simulation results indicate significant improvement in the robustness and effectiveness of the proposed solution in terms of utility's power saving and charging stations' profit. INDEX TERMS Electric vehicles, charging stations, pricing strategy, reinforcement learning. Nomenclature Abbreviations ACO Ant colony optimization. AHC Adaptive heuristic critic. BSS Battery storage system. CDP Coordinated dynamic pricing. CS Charging station. EV Electric vehicle. M DP Markov decision process. RL Reinforcement learning. RLS Recursive least square. Parameters α EVs load index. ∆t Time slot.

This paper proposes a novel graph-based approach with automorphic grouping for the modelling, synthesis, and analysis of electric vehicle (EV) networks with charging stations (CSs) that considers the impacts of traffic. The EV charge demands are modeled by a graph where nodes are positioned at potential locations for CSs, and edges represent traffic flow between the nodes. A synchronization protocol is assumed for the network where the system states correspond to the waiting time at each node. These models are then utilized for the placement and sizing of CSs in order to limit vehicle waiting times at all stations below a desirable threshold level. The main idea is to reformulate the CS placement and sizing problems in a control framework. Moreover, a strategy for the deployment of portable charging stations (PCSs) in selected areas is introduced to further improve the quality of solutions by reducing the overshooting of waiting times during peak traffic hours. Further, the inherent symmetry of the graph, described by graph automorphisms, are leveraged to investigate the number and positions of CSs. Detailed simulations are performed for the EV network of Perth Metropolitan in Western Australia to verify the effectiveness of the proposed approach.

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Recent studies have shown how motion-based biometrics can be used as a form of user authentication and identification without requiring any human cooperation. This category of behavioural biometrics deals with the features we learn in our life as a result of our interaction with the environment and nature. This modality is related to changes in human behaviour over time. The developments in these methods aim to amplify continuous authentication such as biometrics to protect their privacy on user devices. Various Continuous Authentication (CA) systems have been proposed in the literature. They represent a new generation of security mechanisms that continuously monitor user behaviour and use this as the basis to re-authenticate them periodically throughout a login session. However, these methods usually constitute a single classification model which is used to identify or verify a user. This work proposes an algorithm to blend behavioural biometrics with multi-factor authentication (MFA) by introducing a two-step user verification algorithm that verifies the user’s identity using motion-based biometrics and complements the multi-factor authentication, thus making it more secure and flexible. This two-step user verification algorithm is also immune to adversarial attacks, based on our experimental results that show how the rate of misclassification drops while using this model with adversarial data.