D-TDMA: An Approach of Dynamic TDMA Scheduling for Target Tracking in Wireless Sensor Networks
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
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ACM Transactions on Sensor Networks (TOSN)
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ACM Transactions on Design Automation of Electronic Systems (TODAES)
On optimisation of cluster-based sensor network tracking system
International Journal of Ad Hoc and Ubiquitous Computing
Hierarchical role-based data dissemination in wireless sensor networks
The Journal of Supercomputing
Genetic Algorithm-based Adaptive Optimization for Target Tracking in Wireless Sensor Networks
Journal of Signal Processing Systems
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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.