QoS-Aware Resource Allocation for Green Sensor-as-a-Service Provisioning in Vehicular Multi-Sensor-Cloud

Abstract

This work addresses the problem of optimal resource allocation for provisioning green Sensor-as-a-Service (Se-aaS) in the presence of multiple sensor-cloud service providers (SCSPs) and vehicle owners (VOs) having heterogeneous sensors. To ensure high service availability, low carbon footprint, and optimal profit distribution, optimal usage of the available resources while maintaining optimal contribution of VOs in the multi-sensor-cloud (MSC) market is necessary. Additionally, a single SCSP may lose revenue due to the absence of compatible sensor-equipped vehicles (SVs) to serve user requests. To address these issues, we propose FRAME, a game-theoretic resource allocation scheme, for vehicular MSC. Initially, we use a single-leader-multiple-followers Stackelberg game to decide the optimal set of SVs for serving a request while ensuring high QoS, low energy consumption, and optimal profit distribution among the SCSPs and VOs. Further, to address the inadequacy of resources, we propose a collaboration scheme using the expected utility theory for multiple SCSPs and choose the optimal SCSP to serve the requests. We evaluate the performance of FRAME theoretically and experimentally and compare it with benchmark schemes. Simulation results depict a 4.14-27.3% increase in served requests, a 14.61-22.83% decrease in energy consumption, and optimal profit distribution among the SCSP and VOs.