Name
Abstract | ||
|---|---|---|
Energy constraint is an important issue in wireless sensor networks. This paper proposes a distributed energy optimization method for target tracking applications. Sensor nodes are clustered by maximum entropy clustering. Then, the sensing field is divided for parallel sensor deployment optimization. For each cluster, the coverage and energy metrics are calculated by grid exclusion algorithm and Dijkstra's algorithm, respectively. Cluster heads perform parallel particle swarm optimization to maximize the coverage metric and minimize the energy metric. Particle filter is improved by combining the radial basis function network, which constructs the process model. Thus, the target position is predicted by the improved particle filter. Dynamic awakening and optimal sensing scheme are then discussed in dynamic energy management mechanism. A group of sensor nodes which are located in the vicinity of the target will be awakened up and have the opportunity to report their data. The selection of sensor node is optimized considering sensing accuracy and energy consumption. Experimental results verify that energy efficiency of wireless sensor network is enhanced by parallel particle swarm optimization, dynamic awakening approach, and sensor node selection. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TMC.2009.99 | IEEE Trans. Mob. Comput. |
Keywords | Field | DocType |
maximum entropy clustering,energy optimization method,energy optimization,power management,sensor nodes,radial basis function networks,energy metrics,grid exclusion algorithm,energy constraint,target tracking,wireless sensor network,particle swarm optimisation,cluster heads,particle filter,dijkstra's algorithm,radial basis function network,energy consumption,sensing field,optimization.,wireless sensor networks,energy metric,sensor node,collaborative sensing,distributed energy optimization,dynamic energy management mechanism,energy efficiency,parallel particle swarm optimization,particle swarm optimization,maximum entropy,energy management,entropy,energy efficient,particle filters,process model,dijkstra s algorithm,clustering algorithms,optimization | Particle swarm optimization,Sensor node,Key distribution in wireless sensor networks,Mathematical optimization,Computer science,Sensor array,Brooks–Iyengar algorithm,Real-time computing,Multi-swarm optimization,Mobile wireless sensor network,Wireless sensor network,Distributed computing | Journal |
Volume | Issue | ISSN |
9 | 1 | 1536-1233 |
Citations | PageRank | References |
37 | 1.33 | 23 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xue Wang | 1 | 271 | 32.46 |
| Junjie Ma | 2 | 148 | 15.24 |
| Sheng Wang | 3 | 79 | 4.08 |
| Daowei Bi | 4 | 115 | 6.67 |