Previous [ 1] [ 2] [ 3] [ 4] [ 5] [ 6] [ 7] [ 8] [ 9] [ 10] [ 11] [ 12] [ 13] [ 14] [ 15] [ 16] [ 17] [ 18] [ 19] [ 20] [ 21]

@

Journal of Information Science and Engineering, Vol. 23 No. 4, pp. 1023-1039 (July 2007)

SmartBone: An Energy-Efficient Smart Backbone Construction in Wireless Sensor Networks

Shun-Yu Chuang and Chien Chen
Department of Computer Science
National Chiao Tung University
Hsinchu, 300 Taiwan

Wireless sensor network is a rapidly growing discipline with new technologies emerging, and new applications under development. The nodes in a wireless network generally communicate with each other along the same wireless channel. Unfortunately, sharing among wireless channels decreases network performance due to radio interference, and also raises energy consumption due to packet retransmission when interference occurs. Many topology control algorithms have been proposed to solve these problems. One widely used strategy is the backbone method. Backbone algorithms aim to reduce the backbone size. However, poor performance may be explored if only few backbone nodes are selected. Therefore, several heuristic algorithms such as SBC have been proposed. However, these algorithms cannot efficiently eliminate redundant nodes, and dramatically decrease performance, especially in relatively sparse networks. This study proposes a novel heuristic-based backbone algorithm called SmartBone to choose proper backbone nodes from a network. SmartBone includes two major mechanisms. Flow- Bottleneck preprocessing is adopted to find critical nodes, which act as backbone nodes to improve connectivity. Dynamic Density Cutback is adopted to reduce the number of redundant nodes depending on local area node density of network. SmartBone simultaneously considers the balance of network performance and energy savings. Significantly, the proposed algorithm has a 50% smaller backbone size than SBC, and improves the energy saving ratio from 25% using SBC to 40% using SmartBone. Moreover, Smart- Bone improves the packet delivery ratio from 40% to 90% when the density of sensor networks becomes relatively sparser.

Keywords: backbone, topology control, distributed DFS, flow-bottleneck preprocessing, critical nodes, density cutback, energy saving

Full Text () Retrieve PDF document (200707_05.pdf)

Received September 15, 2006; accepted February 6, 2007.
Communicated by Ten H. Lai, Chung-Ta King and Jehn-Ruey Jiang.