Wireless sensor network node optimal deployment method based on improved wolf pack algorithm

Abstract

The invention discloses a wireless sensor network node optimal deployment method based on an improved wolf pack algorithm, which is applied to node optimal deployment of a wireless sensor network, improves the effective coverage rate of wireless sensor nodes, and uses a nonlinear convergence factor balance algorithm to perform earlier global search and later local search capability. An elitist strategy is added to accelerate the convergence speed of the algorithm. A dynamic weight strategy is provided, so that position updating of individuals with poor positions is more reasonable. Meanwhile,a dynamic position crossing processing strategy is provided, and the possibility of searching a global optimal solution in an area is increased. A dynamic variation strategy is introduced to increasethe diversity of wolf groups and effectively expand the search range of the algorithm. The method has the advantages that the problem that the GWO algorithm is prone to falling into local optimum later is solved. The IGWO algorithm improves the coverage performance of the nodes of the wireless sensor network, the higher coverage rate can be achieved with fewer nodes, coverage holes are reduced, and the deployment cost of the network is reduced.

Description

technical field [0001] The invention relates to a method for optimal deployment of wireless sensor network nodes based on an improved wolf pack algorithm, in particular to a method for improving the effective coverage rate of wireless sensor nodes. Background technique [0002] Coverage optimization is a fundamental problem in wireless sensor networks (WSN). In WSN, sensing nodes are generally scattered randomly, resulting in extremely low coverage, and even problems such as network disconnection. Therefore, many researchers have conducted research on it and achieved rich results. Overall, there are two types of network coverage optimization methods. The first is coverage optimization based on geometry, such as the coverage control strategy based on Thiessen's blind zone polygon centroid (blind-zonecentroid-based scheme, BCBS), which uses the geometric relationship between nodes for optimization. Although the BCBS strategy has fast coverage and high robustness, it still c...

AI Technical Summary

Problems solved by technology

The existing technology has problems of falling into local optimum, slow convergence speed, and "premature" situation

And does not consider the existence of obstacles and static nodes in the actual deployment

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