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

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. © Springer-Verlag Berlin Heidelberg 2007.