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.
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Wang, T., Wu, Z., Mao, J. (2007). A New Method for Multi-objective TDMA Scheduling in Wireless Sensor Networks Using Pareto-Based PSO and Fuzzy Comprehensive Judgement. In: Perrott, R., Chapman, B.M., Subhlok, J., de Mello, R.F., Yang, L.T. (eds) High Performance Computing and Communications. HPCC 2007. Lecture Notes in Computer Science, vol 4782. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75444-2_19
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DOI: https://doi.org/10.1007/978-3-540-75444-2_19
Publisher Name: Springer, Berlin, Heidelberg
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