An Entropy-Based Weighted Clustering Algorithm and Its Optimization for Ad Hoc Networks
Yuxuan Wang, Forrest Sheng Bao · 2007
As a newly-proposed weighing-based clustering algorithm, WCA has improved performance compared with other previous clustering algorithms. But the high mobility of nodes will lead to high frequency of re-affiliation which will increase the network overhead. To solve this problem, we propose an entropy- based WCA (EWCA) which can enhance the stability of the network. Meanwhile, in order better to facilitate the optimal operation of the MAC protocol and to further stabilize the network structure, this paper applies tabu search onto EWCA to choose a near optimal dominant set. Consequently, less clusterheads are required to manage the network. Simulation study indicates that the revised algorithm (EWCA-TS) has improved performance with respect to the original WCA, especially on the number of clusters and the re-affiliation frequency.