Simulation-based optimization for reconfiguration of mobile wireless sensor network

When on-board equipments in connected vehicles transmit data to road-side sensors integrated with a traffic signal controller, green phases are redistributed to directly and indirectly reduce network delays. In urban areas, propagation of information and telecommunications may have an obstacles in some locations and produce packet loss and latency of communication. Despite the potential benefits of sensor technologies, the challenges associated with identifying optimal sensor locations for multiple time stages throughout a day with uncertain demand patterns has received little attention. In this paper, we focus on proactively reducing the network delay by controlling traffic signals through an optimized sensor deployment. The framework is based upon portable sensors that may be repositioned within-day and day-to-day to new locations such that delay savings over multiple time stages will be maximized. To tackle this multi-period stochastic problem, dynamic models are proposed, considering the future sensor locations given budget constraints on the sensor costs and relocation costs, and the effect of control is tested on various demand profiles and penetration rates of an urban transportation network.