RACE: QoI-Aware Strategic Resource Allocation for Provisioning Se-aaS

Abstract

In this work, the problem of ensuring profitability for multiple sensor-owners in sensor-cloud, while satisfying the service-requirements of end-users, is studied. In traditional sensor-cloud, Sensor-Cloud-Service-Provider (SCSP) solely dictates the service-provisioning process. However, the SCSP cannot always ensure high profits for sensor-owners, who incur significant maintenance costs for their sensor-nodes. Contrarily, it is highly essential to meet the Quality-of-Information (QoI) requirements of end-users to ensure their service-satisfaction. In the existing literature, researchers proposed few node-allocation schemes which neither consider the cost incurred by sensor-owners nor the QoI of sensed-data in sensor-cloud. To address this problem, a strategic resource-allocation scheme, named RACE, is proposed, which introduces the participation of sensor-owners in the node-allocation process. Firstly, utility theory is used to calculate the optimum number-of-nodes to be allocated for a service. Thereafter, single-leader-multiple-followers Stackelberg game is formulated to decide the number-of-nodes to be contributed by each sensor-owner and the price to be charged. Through simulations, we observed that, using RACE, the profits of the sensor-owners and those of the SCSP increase by 86.11-89.26% and 41.95-80.82%, respectively, as compared to existing benchmark schemes, while considering that each sensor-node is capable of serving multiple applications simultaneously. Moreover, service-availability in sensor-cloud increases by 31.70-96.96% using RACE.