Wireless sensor network node positioning method based on quantum tiger shark mechanism

A wireless sensor and quantum tiger shark technology, applied in wireless communication, network topology, electrical components, etc., can solve the problems of large positioning deviation of network nodes, inability to effectively avoid the positioning deviation problem of gray wolf optimization mechanism, and increased network energy consumption. , to achieve good robustness

Active Publication Date: 2021-08-31
HARBIN ENG UNIV
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  • Abstract
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  • Application Information

AI Technical Summary

Benefits of technology

This technology helps correct for errors caused by distances between nodes on different networks that are far apart or have many other things happening around them. It uses an algorithm called Quantum Shark Optimization (QS) which works well even with noisy data. By estimating these differences accurately it makes sure there will always work correctly without any issues like incorrect location detection.

Problems solved by technology

Technological Problem addressed in this patented paper describes different techniques used in wireless sensor networks (WSN). These techniques involve multiple components like radios, cameras, receivers, and transmitters, among others. They also aim to improve efficiency and reduce power usage during operation while maintaining accurate measurements over large areas with varying topography conditions. Additionally, they focus on improving the reliability of these systems through advanced algorithms called Neural Network Thickness Time Protocol (NNT), Fast Gradient Signature Locator (FGSLSIG) Navigation Message Structure Interval Analysis(SFMA)) Scheme, GISP Antenna Interface Definition Guide System (GNAS)-Windows Platform Support Vector Quantification (WSPQC)).

Method used

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  • Wireless sensor network node positioning method based on quantum tiger shark mechanism
  • Wireless sensor network node positioning method based on quantum tiger shark mechanism
  • Wireless sensor network node positioning method based on quantum tiger shark mechanism

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Embodiment Construction

[0043] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0044] combine figure 1 , the present invention comprises the following steps:

[0045] Step 1, establishing a distance estimation model based on jump distance correction.

[0046] The anchor node broadcasts a beacon to the wireless sensor network. The beacon contains the location information of the anchor node and a parameter indicating the number of hops with an initial value of 1. The beacon is propagated in the form of flooding in the network. The beacon can only be received by the node within the communication radius of a certain node, and the hop count increases by 1 each time the beacon is forwarded. The receiving node only saves the beacon with the smallest hop value among all the beacons it receives about the same anchor node, and discards the beacon with the larger hop value. Through this distance vector routing mechanism, all nodes i

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Abstract

The invention discloses a wireless sensor network node positioning method based on a quantum tiger shark mechanism. The method comprises the following steps: establishing a distance estimation model based on hop distance correction; establishing a node positioning model based on hop distance correction; initializing unknown nodes to be positioned as 1, and positioning the unknown nodes to be positioned one by one; initializing a quantum tiger shark group and setting parameters; defining and calculating the distance between the quantum tiger shark and the prey, and determining the optimal quantum position of the quantum tiger shark group, wherein the quantum tiger sharks execute a prey tracking mode and a swimming mode respectively, and the quantum positions of the quantum tiger sharks are evolved by using a simulated quantum revolving door in the execution process; updating the quantum position of the quantum tiger shark and the optimal quantum position of the quantum tiger shark group; judging evolution termination, and realizing positioning; and performing positioning termination judgment, and outputting positioning results of all unknown nodes. According to the method, the estimated distance from the unknown node to the anchor nodes of the whole network is closer to the real distance, better robustness is achieved, and unknown node positioning in the wireless sensor network is achieved.

Description

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Claims

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Application Information

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Owner HARBIN ENG UNIV
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