Mixed Iterative Adaptive Dynamic Programming Based Sensor Scheduling for Target Tracking in Energy Harvesting Wireless Sensor Networks

Abstract

Target tracking is a typical application of wireless sensor networks (WSNs), in which improving the tracking accuracy with the limited network resources is remaining as a challenging problem. Hence target tracking often relies on sensor scheduling approaches to optimize the resource utilization. With the development of energy acquisition technologies, the building of WSNs based on energy harvesting has become possible to help weaken the limitation of battery energy in WSNs, where theoretically the lifetime of the network could be extended to infinite. Hence, the development of energy harvesting technologies provides a new challenge of infinite-horizon sensor scheduling with the finite energy harvesting capability for high performance target tracking. This paper proposes an adaptive multi-step sensor scheduling approach based on the mixed iterative adaptive dynamic programming (MIADP) to minimize the global performance composed of tracking performance and energy consumption. MIADP consists of two iterations: P-iteration to update the iterative value function and V-iteration to obtain the iterative control law sequence. The simulation results demonstrate that the proposed scheme has advantages in the global trade-off between tracking performance and energy consumption compared with adaptive dynamic programming (ADP) based single-step sensor scheduling.