An energy minimization algorithm based on synergy choice for resource-constrained wireless senor network

• Published in: 2009 ISECS International Colloquium on Computing, Communication, Control, and Management Vol. 4; pp. 129 - 132
• Main Authors: Long Cheng-zhi, Chen Hui, Li Li-hong, Wu Wei-ling, Xiang Man-tian
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2009
• Subjects: Computer networks; cooperative diversity; Energy consumption; Energy efficiency; Minimization methods; Potential energy; Power engineering and energy; Resource management; resource-constrained wireless sensor networks; synergy choice; Transmitting antennas; Wireless application protocol; Wireless sensor networks
• ISBN: 9781424442478; 1424442478
• ISSN: 2154-9613
• DOI: 10.1109/CCCM.2009.5267806

Cooperative diversity can be applied to energy-constrained resource-constrained wireless sensor network s to significantly reduce node energy consumption. However, cooperation synergy must be carefully selected and coordinated to practically exploit this energy saving potential. In this paper we investigate synergy choice for energy minimization algorithm in a resource-constrained wireless sensor network. We formulate novel and computationally efficient synergy choice heuristics for sensor nodes based on either global or local knowledge of average path loss values in the network. We present extensive simulation results of cooperation in a resource-constrained wireless sensor network to show that the proposed heuristics achieve near-optimally energy efficient synergy selection. Therefore, our simple synergy choice heuristics form the basis of an effective distributed cooperation protocol for improving the energy efficiency of a resource-constrained wireless sensor network. Very importantly from the point of view of practical implementation, we show that our synergy choice heuristic based on local information is the most effective cooperation strategy for resource-constrained resource-constrained wireless sensor networks, as it yields superior energy conservation results while enabling fully distributed and scalable cooperation.