Research Article
PSO-based Hybrid Algorithm for Multi-objective TDMA Scheduling in Wireless Sensor Networks
@INPROCEEDINGS{10.1109/CHINACOM.2007.4469517, author={Tao Wang and Zhiming Wu and Jianlin Mao}, title={PSO-based Hybrid Algorithm for Multi-objective TDMA Scheduling in Wireless Sensor Networks}, proceedings={2nd International ICST Conference on Communications and Networking in China}, proceedings_a={CHINACOM}, year={2008}, month={3}, keywords={Computational complexity Computational modeling Delay effects Particle swarm optimization Processor scheduling Scheduling algorithm Simulated annealing Sleep Time division multiple access Wireless sensor networks}, doi={10.1109/CHINACOM.2007.4469517} }- Tao Wang
Zhiming Wu
Jianlin Mao
Year: 2008
PSO-based Hybrid Algorithm for Multi-objective TDMA Scheduling in Wireless Sensor Networks
CHINACOM
IEEE
DOI: 10.1109/CHINACOM.2007.4469517
Abstract
In wireless sensor networks, time division multiple access (TDMA)-based MAC can eliminate collisions, hence save energy and guarantee a bounded delay. However, the slot scheduling problem in TDMA is an NP problem. To minimized the total slots needed by a set of data collection tasks and saving the energy consumed on switching between the active and sleep states, a multi-objective TDMA scheduling scheme needs to be achieved. Nevertheless, owing to the high computational complexity, it is quite difficult to achieve an optimal solution. In this paper, a new hybrid algorithm(HPSO), particle swarm optimization (PSO) embedded with simulated annealing (SA), is proposed against such TDMA scheduling. It combines the high search efficiency and strong global search ability of PSO with good local search ability of SA, thus greatly improving time slot allocation in wireless sensor networks. Simulation results validate that HPSO outperforms three other algorithms in the literature.