A Sleep Scheduling Mechanism with PSO Collaborative Evolution for Wireless Sensor Networks

Abstract

To extend the lifetime of a wireless sensor network and improve the energy efficiency of its nodes, it is necessary to use node collaborative sleep algorithm to reduce the number of redundant nodes in the network. This paper proposes a particle swarm optimization sleep scheduling mechanism for use in wireless sensor networks based on sleep scheduling algorithm. The mechanism adopts the approach of density control and finds the redundant nodes based on the computation results of the network coverage. Experimental results show that the proposed algorithm can ensure adequate coverage under the premise of the ability to close off the redundant nodes, while reducing the total energy consumption of the network.

Efficient approaches to optimize energy consumption in 3D wireless video sensor network under the coverage and connectivity constraints

“…But such a hierarchical structure is unsuitable in the post-disaster scenario where the random deployment of sensor nodes is the only possibility. A PSO based sleep scheduling algorithm is proposed in [25]. The method used in [25] is able to bring down the number of active WSNs and energy consumption ensuring an adequate percentage of coverage.…”

“…A PSO based sleep scheduling algorithm is proposed in [25]. The method used in [25] is able to bring down the number of active WSNs and energy consumption ensuring an adequate percentage of coverage. An improved immune fuzzy genetic algorithm (IIFGA) is suggested in [26] to remove redundancy among WSNs and to select a set of working WSNs without lowering the quality of the coverage much.…”

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“…Zeng [12] and Li proposed a new interval type-2 fuzzy entropy, and discusses its relationship with the interval type-2 fuzzy similarity. Li [13] made the interval type-2 fuzzy entropy integral expression. Zhang discussed the distance based interval type-2 fuzzy entropy and interval type-2 fuzzy similarity relationship.…”