Improved DV-Hop localization method based on hop count threshold optimal average hop distance

Abstract

The present invention provides an improved DV-Hop positioning method based on the hop count threshold to optimize the average hop distance. Firstly, the hop count threshold is calculated, and considering the influence of other anchor nodes other than the nearest anchor node in the local and global ranges, the DV-Hop location is selected according to the threshold value. The optimal corrected average hop distance is used to estimate the distance, and after combined optimization of the anchor nodes within the single-hop communication radius participating in the positioning, the centroid algorithm is used to obtain an estimated coordinate, and the weighted least square method is used to obtain another estimated coordinate, and finally The arithmetic mean of the two estimated coordinates is used as the positioning coordinates of the unknown node. Simulation experiments show that the present invention can more effectively reduce the average node positioning error and improve the positioning accuracy.

Description

technical field [0001] The invention relates to an improved DV-Hop positioning method for optimizing an average hop distance based on a hop count threshold, and belongs to the technical field of wireless sensor networks. Background technique [0002] The DV-Hop algorithm is a non-ranging distributed, hop-by-hop positioning algorithm. The realization of this algorithm mainly includes three steps. [0003] Step 1: The unknown node obtains the coordinate information of the anchor node and the minimum number of hops to the anchor node [0004] Anchor nodes broadcast their own coordinate information through flooding, where the initial hop count is 0, and unknown nodes receive and record the minimum hop count to each anchor node, while ignoring other paths with larger hop counts to the same anchor node , and then add 1 to the hop value and forward it to the neighbor node. Through this mechanism, all nodes in the network can obtain the minimum number of hops from each anchor node...

AI Technical Summary

Problems solved by technology

The error caused by this deviation is not considered in the DV-Hop algorithm

[0021] (2) When an anchor node uses the formula (1) to calculate the average hop distance, it uses all anchor nodes except itself, so the average hop distance obtained can reflect the average density of nodes in the global range, but it cannot reflect the anchor node The distribution of network density in a local area

The estimated distance of the average hop distance multiplied by the number of hops is only an estimate of the real distance, and there will inevitably be errors. The more hops between the anchor node and the unknown node, the greater the error in the estimated distance. Using a farther anchor node The estimated distance information of the node participates in the position calculation, which may reduce the accuracy of the positioning result

[0023] (4) In the positioning stage of unknown nodes, the iterative positioning of anchor nodes will cause errors to accumulate

Because the next round may introduce new errors based on the previous round of positioning errors and accumulate larger errors. When the network scale is large, the cumulative errors will lead to extremely large positioning errors.

[0024] Through the above analysis of the DV-Hop algorithm, we can know that the error of the algorithm is mainly caused by the inaccuracy of the average hop distance of the unknown node and the large distance error to the anchor node participating in the calculation. The positioning error for the DV-Hop algorithm is relatively large. An improved DV-Hop positioning method based on optimizing the average hop distance based on the hop count threshold is proposed

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