Optimal coverage method based on multiobjective evolutionary algorithms in wireless sensor networks

Abstract

The invention discloses an optimal coverage method based on multiobjective evolutionary algorithms in wireless sensor networks. The method comprises the following steps: firstly creating a mathematical model and a target function of the wireless sensor networks, randomly generating a population and employing the multiobjective evolutionary algorithms based on nondominated sorting and dimension bothway search, wherein a main process of the multiobjective evolutionary algorithms is as follows: maintaining one population whose size is N, and guiding the algorithms to approach a Pareto optimal front via continuous iterations. In each iteration process, firstly giving one population Pt, and introducing a bothway directional local search strategy based on an improved differential operation to generate the better population Pt'; and then, sorting the merged population PtUPt' by employing a fast nondominated sorting algorithm and generating a partially ordered boundary, and introducing a new distribution degree maintenance strategy to combine with the fast nondominated sorting algorithm so as to select the new population to enter next evolution. And therefore, a population scheme which enables the total operating power of all nodes of the wireless sensor networks to be small and simultaneously guarantees a coverage rate to be maximized is obtained.

Description

technical field [0001] The invention relates to the technical field of wireless sensors, in particular to an optimal coverage method based on a multi-objective evolutionary algorithm in a wireless sensor network. Background technique [0002] Wireless Sensor Networks (WSN for short) is a wireless ad hoc network composed of a large number of cheap micro sensor nodes. When the sensor nodes are placed in the target area, the sensory signals of the nodes will be affected by factors such as environment and distance, and the sensory probability of the sensor nodes presents a strong uncertainty. The closer the Euclidean distance between the sensor nodes and the monitoring pixels, the more pixels are perceived The greater the probability of , the higher the reliability of the data, and vice versa, the lower the reliability of the data. Most of the existing coverage algorithms study the service life of wireless sensor networks, and it is an important research in wireless sensor tech...

AI Technical Summary

Problems solved by technology

When the sensor nodes are placed in the target area, the sensory signals of the nodes will be affected by factors such as the environment and distance, and the sensory probability of the sensor nodes presents a strong uncertainty. The closer the Euclidean distance between the sensor node and the monitoring pixel, the pixel is perceived The greater the probability, the higher the reliability of the data, and vice versa, the lower the reliability of the data

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