A Distributed Cluster Head Election Method Based on Wireless Ad Hoc Networks

Abstract

The invention discloses a distributed cluster head election method based on a wireless self-organizing network. The election method is based on the highest node degree algorithm and is used when each node is unknown to other node information when the network is initialized. In dense scenarios, the network topology is too complex to complete clustering at one time through a single index. Therefore, the invention divides the cluster head election process into three stages. A certain number of cluster heads are selected in each stage, and the nodes that pass three elections become the final cluster. head. After the simulation test, the clustering speed of this election method is fast, and the network connectivity rate reaches 100%. The clustering result provides the necessary preparation for the optimization of subsequent clustering, network slicing and communication resource allocation.

Description

technical field [0001] The invention relates to the technical field of wireless ad hoc networks, in particular to a distributed cluster head election method based on wireless ad hoc networks. Background technique [0002] The topological types of wireless ad hoc networks can be divided into two types: one is planar structure, such as figure 1 As shown, this kind of network structure is simple, in which all nodes are equal and share the network load. There may be more than one data transmission path between each pair of source nodes and destination nodes. The disadvantage of this structure is that it has poor scalability. If As the scale of the network increases and the mobility of each node increases, maintaining dynamic routing requires a large amount of control information, so this structure is only suitable for small-scale wireless ad hoc networks. [0003] The other is a layered structure, which mainly changes the network form from the physical structure. In the hierar...

AI Technical Summary

Problems solved by technology

Typical clustering algorithms include: the lowest ID algorithm (LOWID), the highest node degree algorithm (HIGHD); weight-based clustering algorithm (WCA); comprehensively consider factors such as connectivity, node moving speed, and energy consumption rate to carry out weighting. Clustering; BIMAC, a bionic clustering algorithm based on mobile perception, can effectively improve link connectivity and cluster head survival time, but cannot guarantee data transmission between clusters; distributed clustering algorithm MOBIC, according to the movement of nodes To select the cluster head, but it is only applicable to the scene where the moving speed and direction of the nodes are the same; the clustering algorithm CEMCA based on connectivity, node energy and mobility

[0005] In the application scenario, the communication distance of network nodes is 200km, which is relatively large compared to the activity range of network nodes (500km*500km), which makes the network topology too dense (such as image 3 shown), so the above-mentioned algorithms such as the highest node degree cannot be directly applied

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