Method for optimizing regional coverage of mobile sensor network

Abstract

The invention provides a method for optimizing regional coverage of a mobile sensor network, which comprises the following steps: establishing a mobile sensor network coverage model, discretizing a mobile sensor network detection area into a plurality of pixel points, and defining the ratio of the number of the pixel points covered by nodes to the total number of the pixel points as a coverage rate; calculating the coverage rate of the initial mobile node by taking improvement of the network coverage rate as an optimization purpose; an using improved longicorn beard search algorithm (IBAS) provided by the invention to take the coverage rate as a fitness function to carry out better search and update the position of the longicorn centroid; calculating fitness values of the left beard and the right beard of the long-horned beetle by improving the step length and selecting the random direction; calculating and reserving a better longicorn centroid position solution through greedy selection; and completing the coverage rate optimization of the wireless sensor network through a node matching algorithm. The longicorn beard search algorithm is improved, so that the coverage effect of themobile sensor network is more uniform, and the coverage rate is better.

Description

technical field [0001] The invention relates to the technical field of mobile sensor network and intelligent optimization, in particular to a method for area coverage optimization of mobile sensor network. Background technique [0002] Mobile Sensor Network (MSN for short) is a network system formed by self-organization of many sensor nodes with specific functions. It is widely used in many scientific fields such as national defense monitoring, environmental monitoring, smart home, medical treatment and transportation. With the development of science and technology and the popularization and application of sensor networks, mobile sensor networks are playing an increasingly important role in daily life, especially in natural disasters such as marine monitoring and forest fires. The timely acquisition of disaster information brings us Come to great convenience. [0003] Network coverage mainly studies the objective perception and data transmission of target objects through th...

AI Technical Summary

Problems solved by technology

[0004] In the field of MSN coverage optimization, the traditional method is often to increase the deployment density of nodes, but redundant nodes have overlapping sensing areas, which leads to increased network power consumption and reduces the overall performance of the network.

For the problem of mobile sensor network coverage optimization, many scholars have adopted some intelligent algorithms to deal with it, such as using particle swarm optimization algorithm to optimize mobile sensor nodes. Although the network coverage is effectively improved, the local search ability of the algorithm is weak; Although its improved dynamic accelerated particle swarm algorithm improves the local search to a certain extent, when there are many nodes, there will still be local blind spots; the artificial bee colony algorithm improves the uniformity of mobile node optimization and obtains a higher coverage rate. However, the solution process is more complicated, the convergence speed is slow, and the energy loss of nodes is faster; the chaotic ant colony algorithm uses the shrinkage factor to improve the search ergodicity of the algorithm, accelerate the convergence speed of the algorithm, and improve the stability of the overall network, but it is easy to fall into local Optimal, there are many boundary blind spots, and the uniformity of coverage cannot be guaranteed

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