Wireless sensor network clustering method based on K-means

Abstract

The invention proposes a wireless sensor network clustering method based on K-means. The method comprises the steps of (1) calculating center point position coordinates of each sensor in a network, and calculating coordinates of clustering points according to the center point position coordinates, (2) calculating a distance from each sensor to each clustering point in the network, and adding each sensor to a same cluster through selecting a clustering point with a nearest distance, wherein in the sensors added into the same cluster, a sensor with the smallest distance to the corresponding clustering point and energy higher than average energy of the sensors in the cluster is selected as the cluster head of the cluster, and (3) calculating the center point position coordinates of the sensors in the cluster for each cluster, taking the center point position coordinates as new cluster point coordinates, repeating the step (2) and the step (3) until the sensors in the cluster are not changed. According to the method, the power consumption of each sensor is reduced, and the overall survival time of a wireless sensor network is prolonged.

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 ...

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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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