A target tracking method and system for self-adaptive node selection based on a Vino diagram

Abstract

The invention belongs to the field of wireless sensor network and target tracking technology application, and discloses a target tracking method and system for self-adaptive node selection based on aVino diagram, and the method comprises the steps: taking a Voronoi graph as a network model, carrying out the clustering of nodes in the network, and enabling an activated node, a dormant node, and aunique cluster head node to exist in a cluster; Partially activating the sensor nodes in the Voronoi graph area according to a proposed node selection algorithm, and performing extended Kalman filtering for target tracking. By implementing the method, the algorithm can have lower energy consumption and higher tracking precision, the energy consumption of the sensor node can be reduced, and the life cycle of the network can be prolonged; The problem of energy balance is greatly improved, and the number of activated nodes is effectively reduced through setting of a greedy algorithm and a threshold value by nodes in the cluster.

Description

technical field [0001] The invention belongs to the application field of wireless sensor network and target tracking technology, and in particular relates to a target tracking method and system based on Voronoi diagram-based self-adaptive node selection. Background technique [0002] Currently, the closest prior art: [0003] With the rapid development of science and technology and computer field, wireless sensor network (Wireless Sensor Networks, WSN) has also been developed rapidly, and has a wide range of applications in various fields such as wildlife monitoring, industrial production, military environment, environmental monitoring, health care, etc. . WSN consists of a large number of low-power and cheap sensors with sensing, data processing and wireless communication components and base station (BS), each sensor node in WSN can calculate, communicate and process data. Sensor nodes rely on batteries for power supply, have no power storage function, and cannot replace ...

AI Technical Summary

Problems solved by technology

[0006] (1) In wireless sensor networks, the energy of sensor nodes is often exhausted, resulting in inefficient operation of the entire network, and even node holes

Moreover, the existing technology cannot reduce the occurrence of node holes and inefficiency

[0007] (2) In the wireless sensor network, the cluster head nodes are randomly selected, and the cluster head nodes may be unevenly distributed or the energy of the cluster head nodes cannot support the network to track the target

[0008] (3) In the wireless sensor network, too many nodes tracking the target will cause energy waste and node redundancy. After excessive energy waste, the sensor nodes in the network cannot perform tracking work, or there are fewer nodes remaining, and the target tracking accuracy is reduced. , and even node holes and target loss appear in the network

[0012] Wireless sensor networks are dense networks. A large number of sensors need to be deployed in the network. Generally, relatively cheap sensor nodes are used for deployment. If the sensor nodes have small-scale failures or insufficient energy, it will directly affect the accuracy of target tracking. The existing The energy of sensor nodes is powered by batteries and cannot be stored. When the energy of the nodes is exhausted, the nodes cannot continue to work. Therefore, how to reduce the energy consumption of sensor nodes is a very critical part of wireless sensor networks.

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