A new method for multi-objective TDMA scheduling in wireless sensor networks using pareto-based PSO and fuzzy comprehensive judgement

Authors:
Tao Wang;Zhiming Wu;Jianlin Mao
Affiliations:
School of Electrical and Computer Engineering, Georgia Institute of Technology and Department of Automation, Shanghai Jiao Tong University, Shanghai, China;Department of Automation, Shanghai Jiao Tong University, Shanghai, China;Department of Automation, Shanghai Jiao Tong University, Shanghai, China
Venue:
HPCC'07 Proceedings of the Third international conference on High Performance Computing and Communications
Year:
2007

Citing 3
Cited 0

GPSR: greedy perimeter stateless routing for wireless networks

MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
An energy-efficient, scalable and collision-free MAC layer protocol for wireless sensor networks: Research Articles

Wireless Communications & Mobile Computing
MOPSO: a proposal for multiple objective particle swarm optimization

CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02

Quantified Score

Hi-index	0.00

Visualization

Abstract

In wireless sensor networks with many-to-one transmission mode, a multi-objective TDMA (Time Division Multiple Access) scheduling model is presented, which concerns about the packet delay and the energy consumed on node state transition. To realize the scheme, a mapping between the problem and evolutionary algorithm is reasonably set up. A multi-objective particle swarm optimization based on Pareto optimality (PAPSO) is then proposed to solve such multi-objective optimization problem and find a better tradeoff between time delay and energy consumption. The simulation results validate the effectivity of PAPSO algorithm and also show that PAPSO outperforms other techniques in the literature.