WSN three-dimensional coverage enhancement method based on improved grey wolf optimizer

Abstract

Disclosed is a WSN three-dimensional coverage enhancement method based on an improved grey wolf optimizer. The contribution degree of an alpha wolf with the most abundant experience is enhanced by theweight calculation formula in the step 3, so that the hunting process of the wolf group is more accurate; to solve the defect that the early global search performance of a classic grey wolf algorithmis poor, the dynamic convergence factor in the step 4 is defined, so that the global optimization capability of the algorithm is enhanced while the complexity of the algorithm is not sacrificed; fora coverage rate threshold of a classic virtual force algorithm, the dynamic stepping formula in the step 4 is defined, so that the algorithm is higher in early optimization speed and more accurate inlater convergence position. To solve the defect that the earlier-stage optimization speed of a classic grey wolf algorithm is relatively low, a virtual force factor is introduced into the wolf pack position updating formula in the step 4, so that the optimal guidance quality of the global search process of the algorithm is enhanced, and the convergence speed of the algorithm is increased.

Description

technical field [0001] The invention relates to the field of improving the gray wolf optimizer, in particular to a WSN three-dimensional coverage enhancement method based on the improved gray wolf optimizer. Background technique [0002] Wireless sensor network is a multi-node, multi-task wireless self-organizing network composed of distributed sensors. The goal of optimal coverage of wireless sensor networks is to achieve complete coverage without gaps in the designated area. However, in the application process, most of the sensor nodes are deployed randomly. Due to the random deployment position, it is difficult to effectively cover the area to be monitored, which affects the quality of the network. monitoring capabilities. [0003] Therefore, it is necessary to adaptively adjust the deployment of sensor nodes, and complete the secondary movement of each sensor node by implementing the coverage optimization algorithm, so as to improve network coverage and reduce network c...

AI Technical Summary

Problems solved by technology

[0004] At present, the research on the coverage optimization of wireless sensor networks generally focuses on the two-dimensional monitoring area, but the monitoring of the two-dimensional area is not enough to meet the needs of many practical fields, such as deploying radar in the ocean to monitor the activities of fish schools, Detecting the impact of migratory bird migration and environmental changes on human production in the atmosphere, so the application research of wireless sensors in three-dimensional environment is also of great value

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