Biblio

Monitoring some sites using a wireless sensor network (WSN) may be hampered by the difficulty of recharging or renewing the batteries of the sensing devices. Mechanisms aiming at improving the energy usage at any moment while fulfilling the application requirements are thus key for maximizing the lifetime of such networks. Among the different methods for achieving such a goal, we focus on energy management methods based on duty-cycling allowing the sensors to switch between two modes: a high-energy mode (active) and a low-energy mode (sleep). In this paper we propose two new scheduling heuristics for addressing the problem of maximizing the lifetime of a WSN under the constraint of coverage of a subset of fixed targets. The first one is a stochastic greedy algorithm and the second one is based on applying Simulated Annealing (SA). Both heuristics use a specific knowledge about the problem. Experimental results show that while both algorithms perform well, greedy algorithm is preferable for small and medium sizes networks, and SA algorithm has competitive advantages for larger networks.