Eficient Energy Management to Prolong Lifetime of Wireless Sensor Network

Since the batteries in a wireless sensor network are either hard to charged or replaced, how to efficiently utilize limited energy in a wireless sensor network has become an important issue. Those operations for a sensor to consume energy are target detection, data transmission and reception, data processing, etc. Among others data transmission consumes most of the energy, and it heavily depends on the transmission distance and the transmitted data amount. In the literature those methods have been devoted to energy saving problems can be categorized into shortening transmission distance (Heinzelman et al., 2000), reducing transmitted data amount (Klein, 1993), scheduling radio transceivers (Busse et al., 2006), scheduling sensing components (Huang & Tseng, 2003), adjusting transmission range (Wang, 2004), and adjusting detection range (Cardei et al., 2006). Our approach focuses on adjusting the detection range of each sensor in order to reduce the overlaps among detection ranges while keep the detection ability above a predefined threshold. If we can largely reduce the overlaps among detection ranges and effectively decrease the amount of duplicate data then we will be able to save energy more efficiently. Meguerdichian et al. (2001) exploited the coverage problem in wireless ad-hoc sensor networks in terms of Voronoi diagram and Delaunay triangulation. In this paper we propose a Voronoi dEtection Range Adjustment (VERA) method that utilizes distributed Voronoi diagram to delimit the responsible detection range of each sensor. Then we use Genetic Algorithm to optimize the most suitable detection range of each sensor. Simulations show that VERA outperforms Maximum Detection Range, K-covered (Huang & Tseng, 2003), and Greedy (Cardei et al., 2006) methods in reducing the overlaps among detection ranges, minimizing energy consumption, and prolonging network lifetime. This paper is organized as follows. Section 2 has a detailed survey on the related work. Section 3 introduces a five-step framework of our proposed methodology, which includes position determination, detection range partition, grid structure establishment, detection power minimization, and detection power adjustment. Section 4 presents system simulations and results. Finally, section 5 offers brief concluding remarks. 3