DRACo: Distributed, Robust an Asynchronous Coverage in Wireless Sensor Networks

Coverage and lifetime are both important performance criteria in wireless sensor networks. Unfortunately, improving one of these metrics comes at the expense of the other metric. In this paper, we investigate the design of algorithms which provide the maximum possible coverage subject to a lifetime guarantee. We assume that nodes are randomly scattered in a sensor field and the goal is to partition these nodes into K sets. At any given time, nodes belonging to only one of these sets actively sense the field. A key challenge is to achieve this partition in a distributed manner with purely local information and yet provide near optimal coverage. In this paper, we propose DRACo for this purpose. We formally prove that DRACo converges when executed in a synchronous manner. We prove that our algorithm and the optimal solution are both Nash equilibrium for an appropriately defined game. Via extensive simulations, we show that DRACo achieves near optimal coverage performance. Moreover, we show that DRACo is robust to network dynamics and can converge even when executed asynchronously. Our simulations indicate that the convergence speed of DRACo is almost constant with the number of nodes N and K.