A clustering method for wireless sensor networks based on k-means

Abstract

The invention proposes a k-means-based wireless sensor network clustering method. Step 1, calculate the coordinates of the central point of each sensor in the network, and calculate the coordinates of the clustering points according to the coordinates of the central position; Step 2, calculate the distance from each sensor in the network to each clustering point, and select the closest clustering point for each sensor Join in the same cluster; Among the sensors added to the same cluster, select the sensor that is closest to the corresponding cluster point and whose energy is higher than the average energy of the sensors in the cluster as the cluster head of the cluster; Step 3, for For each cluster, calculate the position coordinates of the center point of the sensors in the cluster, and use the position coordinates of the center point as the coordinates of a new clustering point; repeat steps 2 and 3 until the sensors in the cluster no longer change. The invention reduces the energy consumption of each sensor and prolongs the overall survival time of the wireless sensor network.

Description

technical field [0001] The invention belongs to the technical field of wireless sensor networks, and in particular relates to a k-means-based wireless sensor network node clustering method. Background technique [0002] A wireless sensor network is a low-cost, highly adaptive wireless network composed of a large number of tiny sensor nodes in a self-organizing manner. It can monitor and perceive information of various environments and monitoring objects in real time, and transmit the data to the required users. With the development of the Internet of Things, wireless sensor networks have very broad prospects, and more and more large-scale wireless sensor networks are put into use. In the wireless sensor network, the energy source of the node is mainly the battery. Due to the huge node scale and the limitation of the environment, it is very difficult to charge the node. When the energy of the node is exhausted, the monitoring range of the network will be empty, and the full ...

AI Technical Summary

Problems solved by technology

However, this method of randomly selecting cluster head nodes often leads to poor clustering results, which makes the transmission distance of some nodes longer, which speeds up energy consumption and greatly shortens the survival time.

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