Data aggregation algorithm based on clustering for wireless sensor networks

In wireless sensor networks, due to uneven load distribution of sensor nodes and high energy consumption, cluster heads die prematurely and the phenomenon of energy hole appears. To solve these problems, a data aggregation method based on game theory is proposed. In the novel algorithm, the clusters are adjusted following the game theory after the initial clustering based on the virtual cellular structure. The numbers of nodes in each cluster are optimized to be as balanced as possible to maximize network life. The data collection schemes are designed according to the different functions of nodes in different positions in the cluster. In order to improve the accuracy of cluster head fusion data, the member nodes in the cluster are divided into reliable nodes, redundant nodes, and supplementary nodes, and the judgment and selection method of redundant nodes is proposed. The simulation results show that the algorithm can effectively eliminate data redundancy, and reduce the communication burden of the cluster head. The proposed algorithm is an effective measure to fully extend the network life.

Automated summary

In this study, a data aggregation method based on game theory is proposed to address the problems of uneven load distribution and high energy consumption in wireless sensor networks. The algorithm adjusts the clusters and optimizes the number of nodes in each cluster to maximize network life. Different data collection schemes are designed based on the functions of nodes in different positions in the cluster. Moreover, a method for judging and selecting redundant nodes is proposed to improve the accuracy of cluster head fusion data.