Wireless sensor network node positioning method of ecological niche grey wolf optimization DV-Hop algorithm

A wireless sensor and network node technology, applied in network topology, wireless communication, service based on location information, etc., can solve problems such as low accuracy and local optimum

Inactive Publication Date: 2019-07-26
SHANGHAI MARITIME UNIVERSITY
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AI Technical Summary

Problems solved by technology

However, the existing DV-Hop algorithm with gray wolf optimization algorithm calculates the optimal solution according to the position information changes of gray wolf individuals with the top three

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  • Wireless sensor network node positioning method of ecological niche grey wolf optimization DV-Hop algorithm
  • Wireless sensor network node positioning method of ecological niche grey wolf optimization DV-Hop algorithm
  • Wireless sensor network node positioning method of ecological niche grey wolf optimization DV-Hop algorithm

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Embodiment Construction

[0062] The present invention will be further elaborated below by describing a preferred specific embodiment in detail in conjunction with the accompanying drawings.

[0063] Such as Figure 1-3 As shown, a wireless sensor network node positioning method based on the niche gray wolf optimization DV-Hop algorithm includes the following steps:

[0064] S1. Using the classic distance-vector-based routing protocol, the beacon node broadcasts the data packet, and after the data packet is propagated through the flooding method, each beacon node in the network can obtain the minimum hop count h of other beacon nodes ij and position coordinate information X.

[0065] Described step S1 specifically comprises:

[0066] S11. The beacon node broadcasts a beacon to the network. The beacon contains the location coordinate information X of the beacon node and a parameter h indicating the number of hops with an initial value of 1. The beacon is flooded in the network The way to spread out, ...

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Abstract

The invention discloses a wireless sensor network node positioning method of an ecological niche grey wolf optimization DV-Hop algorithm. The method comprises: a beacon node broadcasting a beacon to anetwork, the beacon comprising position information and hop count information of the beacon node, and the beacon being spread out in a flooding mode in the network; after obtaining the position information and the hop count, each node estimating the average distance of each hop between the nodes, and then estimating the distance between the beacon nodes; the beacon nodes refining the average distance per hop by using a ecological niche grey wolf optimization algorithm, and changing the fitness value of the beacon nodes by comparing the distances between the beacon nodes with the ecological niche radius; calculating the position of the unknown node through the beacon node, recording the coordinate of the unknown node, and converting the calculated position information of the unknown sensorbeacon node into the position information of the known sensor beacon node. The method has the advantages that the global search capability can be improved, the positioning error is reduced, and the positioning precision is improved.

Description

technical field [0001] The invention relates to a positioning method of a wireless sensor network node, in particular to a positioning method of a wireless sensor network node based on a niche gray wolf optimization DV-Hop algorithm. Background technique [0002] Wireless sensor network is one of the current research hotspots in the information field. It integrates various technologies such as wireless communication, computer, microelectronics, and sensors, and can be used for data collection, processing, transmission, and monitoring in some special environments, such as: environment Monitoring, military survey, industrial and agricultural production, traffic monitoring, etc. In wireless sensor networks, the accuracy and security of node positioning are crucial to the normal operation of wireless sensor networks. However, wireless sensor networks are composed of cheap energy-limited perceptrons, and only a small number of perceptron nodes know their own Location. Therefore...

Claims

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Application Information

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IPC IPC(8): H04W4/02H04W4/06H04W40/20H04W64/00H04W84/18
CPCH04W4/023H04W4/06H04W40/20H04W64/003H04W84/18
Inventor 周书丽韩德志
Owner SHANGHAI MARITIME UNIVERSITY
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