Cluster Algorithm with Dual-Cluster Heads Based on Multi-Criteria Decision-Making in Heterogeneous Cognitive Sensor Network

Cognitive sensor networks can not only alleviate the congestion of unlicensed spectrum, but also improve the utilization of licensed spectrum, which has become a recent research hotspot. However, it faces the dual challenges of high cost and high energy consumption. The clustering routing algorithm has the advantages of high energy utilization and less data redundancy, and has become an effective solution to high energy consumption. At present, the cluster unit constructed by the existing cluster routing algorithm has only one cluster head, which results in a serious imbalance of energy consumption between the cluster head and the member nodes. In addition, the existing cluster routing algorithms do not consider the reliability of transmitted data. Based on the above problems, this paper proposes a cluster routing algorithm with dual-cluster heads based on multi-criteria decision-making for heterogeneous cognitive sensor networks. Firstly, in the process of the cognitive node electing the main cluster head, the multi-criteria decision-making method is used to determine the main cluster head. Secondly, a selection scheme of the slave cluster head and an adjustable primary cluster head competition radius adapted to the random distribution of nodes are proposed. Finally, in the cluster head transmission stage, a routing algorithm based on spectrum detection is proposed to improve the data transmission rate. The simulation results show that the proposed algorithm can not only effectively reduce the energy consumption of the cognitive sensor network, but also improve the channel detection probability of the network, thereby improving the reliability of data transmission.