Network clustering method of mobile sensor based on partition

Abstract

The invention discloses a network clustering method of a mobile sensor based on partition, which belongs to the network field of the mobile sensor in the wireless communication. The method not only combines loading conditions of clusters, but also takes the mobility and the rest energy of nodes into consideration. By comparing the numbers of nodes in the clusters, the loading conditions of each cluster can be obtained. The partition of the clusters can be realized by dividing the clusters with large load into a proper amount of subdomains and degrading and fusing the clusters with undersized load. With the adoption of the network clustering method of the mobile sensor, the network connectivity and the stability of the cluster structure can be efficiently enhanced, and the service life of the network is prolonged.

Description

technical field [0001] The invention takes the mobile sensor network as the research object, and designs a clustering method based on the partition mechanism, which not only considers the mobility but also takes into account the remaining energy of the nodes, can effectively improve the network connectivity and prolong the life cycle of the network, and belongs to the field of wireless communication. field of mobile sensor networks. Background technique [0002] Mobile sensor networks are mostly composed of a large number of tiny, cheap, low-power, mobile nodes with wireless transceivers deployed in the observation environment. Each node in the network not only has the capabilities of sensing, computing and communication , and has a certain mobility. Usually, in order to extend the life time of nodes, only a small number of nodes in the network are responsible for sending query messages and receiving data uploaded by other nodes. [0003] Due to the movement of nodes, mobi...

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Problems solved by technology

Chih-Yu Wen et al. proposed Self-Adaptive Mobile Clustering Algorithm (SAMCA), which further improves the connectivity of the network. This algorithm takes mobility and node degree as the factors of cluster head election, and selects the node whose speed value tends to be average as the cluster head. Candidate nodes, but due to the iterative method for calculation, the algorithm is more complicated, and the energy consumption of nodes is not considered

S.Deng et al. proposed a mobility-based clustering (MBC) clustering method. This algorithm comprehensively considers the remaining energy and mobility of nodes, and uses a method similar to LEACH to randomly select cluster heads. The algorithm is simple and easy to deploy on a large scale. However, The energy balance of the network is not considered, which will affect the life cycle of the network

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