Dynamic self-adaptive multicast in wireless networks

Data multicast technology has received much attention and many achievements have been attained for it. However, most existing multicast techniques rely heavily on client input for data dissemination. In this paper, we investigated an efficient way of automatically identifying the changes of clients´ preferences. Our scheme utilizes clustering technique to realize the group of similar clients, and furthermore, dynamically adjusts these clusters to cope with the changes of clients´ demands. The rationale underneath is that clients within the same cluster possess similar preferences. The goal of our method is to dynamically present information tailored to specific groups of clients and ensure that the multicast information clients received can satisfy their interests. To conclude this paper, we experimented the recommended method on three different modes of clients´ preferences. The simulation results prove it an effective scheme to reduce the amount of superfluous data clients received for certain preference distributions.