WSN routing protocol method based on cuckoo algorithm and K-means algorithm

Abstract

The invention discloses a WSN routing protocol method based on a cuckoo algorithm and a K-means algorithm. The method is operated by taking a round as a unit, and each round comprises the following steps: 1, carrying out a clustering stage: determining an optimal cluster head number k, and clustering by adopting the K-means algorithm optimized based on the cuckoo algorithm to obtain k clusters and nodes in each cluster; 2, obtaining election values of nodes in each cluster according to an election function, and selecting a cluster head of each cluster from k clusters according to the election values; and 3, carrying out a data transmission stage: receiving and fusing, by the cluster head in each cluster, node data in the cluster, then sending the data to the base station in a single-hop or multi-hop mode according to the distance between the cluster head and the base station, and when the cluster head adopts multiple hops, planning a route for the cluster head by using a cuckoo algorithm. The initial clustering center of the K-means algorithm is optimized by adopting the cuckoo algorithm, so that the network clustering is more uniform, the network energy consumption is balanced, the selected cluster head is more reasonable, and the energy consumed by the cluster head due to long-distance data transmission is reduced by planning the cluster head route.

Description

technical field [0001] The invention relates to the field of network routing protocols, in particular to a WSN routing protocol method based on a cuckoo algorithm and a K-means algorithm. Background technique [0002] Wireless sensor network (Wireless Sensor Network, WSN) is composed of a large number of cheap, low power consumption miniature sensor nodes. Sensor nodes form a self-organizing network through wireless communication, which can perceive monitoring information, collect and process data, and send the information to the aggregation center or base station. It is mostly used in military, industrial, environmental monitoring, aviation and other fields. However, due to the limited and non-rechargeable energy of network nodes, how to extend the life cycle of the network is a current focus of attention. Aiming at how to improve the network life cycle, many hierarchical network routing protocols based on cluster structure have been proposed, which have been proved to be ...

AI Technical Summary

Problems solved by technology

[0003] The LEACH protocol randomly selects cluster heads in a circular manner, so that each node has the opportunity to become a cluster head, and then performs network clustering according to the nearest principle, and the nodes in the cluster directly send data to the cluster head, and the cluster head directly sends the fused data to However, because the LEACH algorithm does not consider the remaining energy of the cluster head and its geographical location, it is easy to cause extremely small and extremely large clusters. Clusters of different sizes will increase the energy consumption of the cluster heads, especially the cluster heads in larger clusters. , because the cluster head in a very large cluster needs to receive a large amount of data fusion, it will cause the cluster head of the cluster to die prematurely, which is not conducive to the balance of network energy consumption

In addition, when a node with low energy is selected as the cluster head, the cluster head of the cluster may be interrupted due to energy overdraft after a period of data transmission, resulting in unstable data transmission

The LEACH-improve protocol integrates the remaining energy factor, spacing factor, and density factor into the traditional LEACH threshold calculation formula, which makes the elected cluster head more reasonable than the LEACH protocol and prolongs the network life cycle. Although the protocol considers The remaining energy of the cluster heads and geographical location factors make the elected cluster heads more reasonable than the LEACH algorithm, but because the selection of the cluster heads does not consider the uniformity of the clusters, uneven clustering will still occur, which will aggravate individual maximum The energy consumption of the cluster head and the data transmission energy consumption of the nodes in the cluster far away from the cluster head are not conducive to balancing the energy consumption of nodes

In addition, the cluster head sends data to the base station in the form of a single hop. When the cluster head is far away from the base station, the cluster head load will be increased.

The DTK-means algorithm first uses the K-means algorithm to cluster the network, and then uses the remaining energy of nodes, distance and other factors as dynamic trust to select dual cluster heads, which prolongs the network life cycle. However, this algorithm ignores the impact of the K-means algorithm on the initial Sensitivity of the clustering center can easily cause the K-means algorithm to fall into a local optimum and form uneven clustering

In addition, the algorithm does not perform routing planning for remote cluster heads, and long-distance data transmission will increase the energy consumption of cluster heads.

In the clustering stage, the BeeSwarm algorithm uses the artificial bee colony algorithm to find the optimal cluster head and clusters in combination with node energy and distance factors. In the data transmission stage, the artificial bee colony algorithm is used to plan the path from the nodes in the cluster to the cluster head. The cluster head directly When communicating with the base station, although the BeeSwarm algorithm has obtained a reasonable cluster head and planned routes for the nodes in the cluster, it does not consider the remaining energy of the relay nodes, which may easily lead to the selection of nodes with low energy as relay nodes, increasing the number of relay nodes. Increases the possibility of premature aging and affects the stability of data transmission. In addition, the algorithm does not plan routes for cluster heads, which is not conducive to balancing the load of remote cluster heads.

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