Sensor network skeleton extraction method

Abstract

The invention discloses a method for extracting a sensor network skeleton, which comprises the steps of: 1, finding angular points on the boundary, and dividing the boundary into limited boundary branches; 2, identifying interconnected skeleton nodes; 3, searching two skeleton nodes with largest distance in the interconnected skeleton nodes, and connecting the two skeleton nodes to generate a skeleton chord; 4, connecting adjacent skeleton chords, and then connecting the angular points with nearest skeleton chords to generate a rough skeleton drawing; and 5, adopting a pruning method to optimize the rough skeleton drawing to obtain a final skeleton. The method utilizes different boundary branches to determine the skeleton nodes; and compared with the conventional algorithm, the method cannot be affected by boundary perturbation, so a more approximate network topology structure can be obtained, thereby better reconstructing a network.

Description

technical field [0001] The invention relates to the technical field of wireless sensor networks, in particular to a network skeleton extraction method based on connection information. Background technique [0002] Many sensor network devices are closely related to the collective environment in which the sensor nodes are located. Skeleton extraction about the sensor network topology greatly improves the performance of services such as positioning and routing in sensor networks. In the field of computer vision research, research on topology extraction mainly focuses on continuous space and cannot be directly applied to discrete sensor networks. In the existing literature on topology discovery, the main focus is on boundary recognition techniques, and on this basis to find out the skeleton lines. The representative one is the MAP algorithm proposed by JehoshuaBruck et al. The specific method is: first identify the boundary of the sensor network, and then use the maximum inscri...

AI Technical Summary

Problems solved by technology

Since the sensor network nodes are discretely distributed, this method is often susceptible to boundary disturbances, resulting in unreal central axis nodes, so that the central axis cannot well represent the real topology of the sensor network.

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