Simultaneous Sensor Placement and Scheduling for Fusion-Based Detection in RF-Powered Sensor Networks

When deploying radio frequency (RF)-powered sensor networks for mission-critical applications such as security surveillance, it is often required to maximize or guarantee the quality of surveillance. Both placing and scheduling the charging/working modes of sensors are of key importance in order to continuously ensure a satisfying quality of surveillance. Traditionally, sensor placement and scheduling have been considered separately. The first decision regards where to place the sensors, and then when to activate them. In this paper, we study simultaneous sensor placement and charging/working scheduling problem for fusion-based detection in RF-powered sensor networks. The problem is formulated as a constrained optimization problem and proved to be NP-complete. Two greedy heuristic algorithms, joint optimization greedy algorithm with fixed fusion radius (JOGA-FFR) and joint optimization greedy algorithm with dynamic fusion radius (JOGA-DFR) based on fixed and dynamic fusion radiuses, respectively, are presented to solve the problem. We validate our approaches through extensive numerical simulations as well as simulations based on real data traces collected from a vehicle detection experiment. The results show that, our proposed algorithms always outperform two-stage greedy algorithm (TSGA), an algorithm that optimizes sensor placement and scheduling separately, in all the simulation scenarios, and are near optimal in small-scale networks. Besides, JOGA-DFR outperforms JOGA-FFR under certain specific sensing model settings, but more often has a comparable performance with JOGA-FFR. JOGA-FFR is thus more recommended for its lower complexity.