Wireless network coverage optimization method based on multivariate universe optimization algorithm

Abstract

The invention relates to the field of wireless sensing technology and intelligent technology, and discloses a wireless network coverage optimization method based on a multivariate universe optimization algorithm, which comprises the following steps: discretizing a monitoring area into a plurality of pixel points by using a grid method; utilizing a sensor sensing model to calculate the joint probability that the sensor node at each pixel point is sensed by the sensor node set; calculating the coverage rate of the wireless sensor network according to the joint probability, and obtaining the coverage optimization objective function of the wireless sensor network in combination with the energy consumption of the sensor nodes; dividing sensor nodes in the wireless sensor network into a plurality of single universe groups, and performing fitness calculation on each single universe group by utilizing a fitness function of the target function; performing universe chaotic evolution on the single universe group with relatively low fitness; and setting the nodes meeting the coverage rate requirement to be in a dormant state based on a node dormancy strategy, and completing coverage optimization of the wireless sensor network. According to the invention, the coverage optimization of the wireless sensor network is realized.

Description

technical field [0001] The invention relates to the technical field of wireless sensing technology and intelligence, in particular to a wireless network coverage optimization method based on a multiverse optimization algorithm. Background technique [0002] With the development of embedded systems, micro-electromechanical systems and wireless communication technologies, wireless sensor networks enable humans to realize information perception and physical information exchange in the target environment through information transmission technology, which has attracted great attention from various research institutions. The wireless sensor network is composed of a large number of sensor nodes deployed in the detection area, and collects the node information in the detection area based on wireless communication. In order to meet the requirements of deployment time cost, reliability and energy efficiency that network applications can bear, it is necessary to study the deployment me...

AI Technical Summary

Problems solved by technology

[0004] In order to achieve higher network coverage, the traditional method is to deploy large-scale sensor nodes, but too many sensor nodes are likely to cause communication conflicts

Some scholars have proposed some optimization algorithm strategies for the wireless network optimization problem, such as using the artificial firefly algorithm to optimize the wireless sensor network nodes, so that the wireless sensor nodes are evenly distributed and have high coverage, but there are many boundary blind areas; the chaotic fish swarm algorithm The optimization stability of the wireless sensor network is improved, and a high network coverage rate is obtained, but there are many blind areas at the border, and the center redundancy is high; the particle swarm optimization algorithm has a strong global convergence ability, and can quickly find the network with a low coverage rate. The distribution of sensor nodes is high, but the local search ability is weak after the algorithm is optimized; although the search ability of the ant colony algorithm is relatively strong, there are disadvantages such as complicated solution process, slow convergence speed, and no consideration of energy loss of sensor nodes; genetic algorithm can Obtain the optimal coverage set in the wireless sensor network, so as to cover all the targets in the monitoring area with the least number of nodes and node power consumption. However, this algorithm is easy to fall into local optimum and cannot guarantee the quality of the coverage set.

Images