Distributed social welfare maximization in vehicular participatory sensing systems

We consider the crucial problem of maximizing the social welfare of a vehicular participatory sensing system which consists of mobile source vehicles that collect sensing data on the move and relay vehicles that forward sensing data to the central sensing platform. Three issues should be taken into consideration, i.e., sensing rate control, flow control and transmission, incentives for vehicles to participate. It is highly challenging to achieve the optimal social welfare, given the arbitrary unknown contact events and the dynamic network formed by mobile vehicles. To tackle the challenges, we propose a fully distributed optimal control algorithm DBC to maximize the social welfare while keeping good network stability. Exploiting the stochastic Lyapunov optimization technique, it derives an optimal control strategy for sensing rate control and flow control. Rigorous theoretical analysis shows the gap between the DBC and the optimum is only O(1/V), where V is a tradeoff parameter between social welfare and network stability. In addition, we have conducted extensive simulations based on real taxi GPS traces, and the results show the efficacy of DBC.