A wireless sensor network node location method based on the quantum tiger shark mechanism

Abstract

The invention discloses a wireless sensor network node positioning method based on the quantum tiger shark mechanism, including establishing a distance estimation model based on jump distance correction; establishing a node positioning model based on jump distance correction; initializing the unknown node number to be positioned as 1, Locate the unknown nodes to be positioned one by one; initialize the quantum tiger shark group and set parameters; define and calculate the distance between the quantum tiger shark and the prey, and determine the optimal quantum position of the quantum tiger shark group; the quantum tiger shark executes the prey tracking mode and Swimming mode, and use the simulated quantum revolving door to evolve the quantum position of the quantum tiger shark during the execution process; update the quantum position of the quantum tiger shark and the optimal quantum position of the quantum tiger shark group; judge the evolution termination, realize positioning; determine the termination of positioning, Output all unknown node location results. The estimated distance from the unknown node to the anchor node of the whole network in the present invention is closer to the real distance, has better robustness, and realizes the positioning of the unknown node in the wireless sensor network.

Description

technical field [0001] The invention relates to a wireless sensor network node positioning method based on hop distance correction and quantum tiger shark mechanism, and belongs to the technical field of wireless communication. Background technique [0002] As a multi-hop self-organizing network formed by wireless communication, wireless sensor network (WSN) mainly includes three elements: sensor, sensing object and observer, and integrates various technologies to realize the collection and processing of sensing object information in the network coverage area. It has broad application prospects in the fields of intrusion detection, environmental monitoring, indoor monitoring and traffic analysis. Location information is an indispensable part in the process of sensor network nodes collecting data. It is one of the most basic functions of sensor networks to determine the location of events or the location of nodes that collect data. Therefore, network node location technology...

AI Technical Summary

Problems solved by technology

[0004] Through the retrieval of existing technical literature, it was found that "Improved DV-Hop Positioning Algorithm Based on Hop Distance Optimization" published by Jing Lulu et al. The multi-communication radius strategy is introduced to refine the number of hops between anchor nodes, and then the hop distance of each anchor node is obtained. The weighted average strategy is introduced to use the hop distance information of the anchor nodes in the whole network, and then the hop distance of each unknown node is obtained. This positioning method enables all unknown nodes to obtain a theoretically optimal hop distance to the anchor nodes of the entire network, which is suitable for isotropic networks, but does not conform to the characteristics of anisotropic networks in practical applications, and multi-communication Transmitting signals within a radius will increase network energy consumption; Shi Qinqin and others published "Improvement of DV-Hop Positioning Based on Distance Correction and Gray Wolf Optimization Algorithm" in "Journal of Sensing Technology" (2019, 32(10): 1549-1555) "Introduce the maximum similarity path strategy to obtain the hop distance of each unknown node, and introduce the gray wolf optimization mechanism to realize the location calculation of unknown nodes. The network topology and other aspects have high requirements, and the problem of positioning deviation caused by the gray wolf optimization mechanism is easy to fall into local extreme values ​​cannot be effectively avoided; Fang Wangsheng et al. in "Sensors and Microsystems" (2020,39(07):127- 129+133) published "Node Density Weighted and Distance Corrected Particle Swarm Optimal Positioning Algorithm" introduced the density weighted strategy and particle swarm optimization mechanism to realize unknown node positioning, but there are still shortcomings in the application level

[0005] The search results of the existing literature show that there are several deficiencies in the existing non-ranging-based wireless sensor network node positioning methods: In the distance estimation link, the accuracy of distance estimation depends largely on the quality of the network topology , if factors such as network node density, number of anchor nodes, or network architecture are not ideal, it will lead to excessive distance estimation deviation and increased network energy consumption, which in turn will lead to excessive positioning deviation of network nodes and reduced sensor life; in the position calculation link, positioning The algorithm still has problems such as slow convergence speed, poor convergence accuracy and easy to fall into local extremum.

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