Dynamic Entropy Based Combination Weighted Clustering Approach for High-Speed Ad hoc Network

Weight based clustering has become the mainstream clustering algorithm in low-speed Ad hoc networks because of its excellent cluster stability. However, due to the dynamic topology changing in high-speed Ad hoc network, the cluster stability (network stability) decreased and the cluster maintenance costs increased sharply. To solve the problem, we propose a dynamic entropy based combination weighted clustering approach (DECW). First, according to the history messages of an evaluation node in the network, the upper bound and the lower bound value of each clustering index will be recorded, so the information entropy deviation of the indexes and dynamic entropy weight of each node can be obtained. After, the linear combination weights set of evaluation nodes is modeled as the second-order norm game , and the weight vector deviation is minimized as the optimization goal to get the multi-node dynamic entropy weights. In the cluster maintenance, a new Monte Carlo optimization is proposed to avoid the frequent clusterheads (CHs) replacement induced of high node mobility of. Simulation results reveal that the proposed approach has the better adaptability in high-speed mobile environment.