Optimal recruitment of smart vehicles for reputation-aware public sensing

Public sensing services utilizing the abundant on-vehicle resources are gaining high interest nowadays. One of the challenges facing such ubiquitous utilization is the recruitment and selection of the participating vehicles. In this paper, we present an optimal reputation-aware, trajectory-based framework that handles recruitment of vehicles for public sensing. The framework considers the spatiotemporal availability of participants along with their reputation to select vehicles that achieve desired coverage of an area of interest within a budget limit. In addition, we present a reputation assessment scheme and a pricing model for computing a reputation score and a recruitment cost for each candidate participant. The framework is formulated as an integer linear programming optimization problem and hence provides a benchmark and upper bound on achievable potential. We present analysis for two different practical recruitment objectives and show results under various scenarios.