Parallel energy-efficient coverage optimization with maximum entropy clustering in wireless sensor networks

Authors:
Xue Wang;Junjie Ma;Sheng Wang
Affiliations:
State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments, Tsinghua University, Beijing 100084, PR China;State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments, Tsinghua University, Beijing 100084, PR China;State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments, Tsinghua University, Beijing 100084, PR China
Venue:
Journal of Parallel and Distributed Computing
Year:
2009

Citing 6
Cited 1

Entropy-based fuzzy clustering and fuzzy modeling

Fuzzy Sets and Systems
Algebra and algorithms for QoS path computation and hop-by-hop routing in the internet

IEEE/ACM Transactions on Networking (TON)
Energy-Efficient Communication Protocol for Wireless Microsensor Networks

HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Optimized Scheduling for Data Aggregation in Wireless Sensor Networks

ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II - Volume 02
Towards radar-enabled sensor networks

Proceedings of the 5th international conference on Information processing in sensor networks
Mobile agent based wireless sensor network for intelligent maintenance

ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part II

A distributed energy-efficient clustering algorithm with improved coverage in wireless sensor networks

Future Generation Computer Systems

Quantified Score

Hi-index	0.01

Visualization

Abstract

Energy constraint is an important issue in wireless sensor networks. This paper proposes a parallel energy-efficient coverage optimization mechanism to optimize the positions of mobile sensor nodes based on maximum entropy clustering in large-scale wireless sensor networks. According to the models of coverage and energy, stationary nodes are partitioned into clusters by maximum entropy clustering. After identifying the boundary node of each cluster, the sensing area is divided for parallel optimization. A numerical algorithm is adopted to calculate the coverage metric of each cluster, while the lowest cost paths of the inner cluster are used to define the energy metric in which Dijkstra's algorithm is utilized. Then cluster heads are assigned to perform parallel particle swarm optimization to maximize the coverage metric and minimize the energy metric where a weight coefficient between the two metrics is employed to achieve a tradeoff between coverage area and energy efficiency. Simulations of the optimization mechanism and a target tracking application verify that coverage performance can be guaranteed by choosing a proper weight coefficient for each cluster and energy efficiency is enhanced by parallel energy-efficient optimization.