Quality of Information (QoI)-aware cooperative sensing in vehicular sensor networks

Abstract

Recently, the vehicular sensor network (VSN) is emerging as an efficient solution for executing different sensing tasks in urban environments. However, due to the heterogeneity of vehicles in sensing capability and uncontrollable movement trajectory, it is a challenge to best provide the required quality of information (QoI) of the sensing task in VSNs. In this paper, we introduce a VSN architecture, in which multiple vehicles cooperatively sense a particular urban area of interest, and process the sensed data to achieve the QoI requirements while considering incentives for environment sensing, data processing and communication. Furthermore, we formulate and solve an optimization problem for determining the optimal sampling rates for vehicles with the objective of minimizing the total incentive under the constraints related to QoI requirements. Various numerical results based on realistic vehicular traces are presented to justify the effectiveness of proposed approach in the vehicles' QoI-aware cooperative sensing operations.