6533b85afe1ef96bd12b9ce6

RESEARCH PRODUCT

Allocation des ressources dans l’informatique en brouillard le calcul du brouillard véhiculaire pour une utilisation optimale des véhicules électriques

subject

description

Abstract: Technological advancements made it possible for Electric vehicles (EVs) to have onboard computation, communication, storage, and sensing capabilities. Nevertheless, most of the time these EVs spend their time in parking lots, which makes onboard devices cruelly underutilized. Thus, a better management and pooling these underutilized resources together would be strongly recommended. The new aggregated resources would be useful for traffic safety applications, comfort related applications or can be used as a distributed data center. Moreover, parked vehicles might also be used as a service delivery platform to serve users. Therefore, the use of aggregated abundant resources for the deployment of different local mobile applications leads to the development of a new architecture called Vehicular Fog Computing (VFC). Through VFC, abundant resources of vehicles in the parking area, on the mall or in the airport, can act as fog nodes. In another context, mobile applications have become more popular, complex and resource intensive. Some sophisticated embedded applications require intensive computation capabilities and high-energy consumption that transcend the limited capabilities of mobile devices. Throughout this work, we tackle the problem of achieving an effective deployment of a VFC system by aggregating unused resources of parked EVs, which would be eventually used as fog nodes to serve nearby mobile users’ computation demands. At first, we present a state of the art on EVs and resource allocation in VFC. In addition, we assess the potential of aggregated resources in EVs for serving local mobile users’ applications demands by considering the battery State of Health (SOH) and State of Charge (SOC). Here, the objective is to choose EVs with a good condition of SOH and SOC so that owners secure tolerable amount of energy for mobility. Then, we address the problem of resource allocation scheme with a new solution based on Markov Decision Process (MDP) that aims to optimize the use of EVs energy for both computing users’ demands and mobility. Hence, the novelty of this contribution is to take into consideration the amount of aggregated EVs resource for serving users’ demands. Finally, we propose a stochastic theoretical game approach to show the dynamics of both mobile users’ computation demands and the availability of EVs resources.