Clustering algorithm in delay tolerant mobile networks for pervasive information gathering

With computing devices becoming progressively small and powerful, the trend of computing tries to move beyond the realm of the personal computer to the tiny devices deeply embedded in people's daily life. The idea of pervasive computing was envisioned in a system that “activates the world, makes computer so embedded, so fitting, so natural, that we use it without even thinking about it. The work focuses on the DelayTolerant Mobile Networks for pervasive information gathering, with unique characteristics of DFT-MSN, such as sensor mobility, loose connectivity, fault tolerability, delay tolerability, and buffer limit, designing an efficient data delivery scheme is challenging. In this paper we propose an energy efficient clustering algorithm for dense mobile sensor networks scenario. In the initial cluster formation phase, our proposed scheme features a simple execution process with polynomial time complexity, by introducing some GPS-capable mobile nodes to act as cluster heads. Later is the maintenance of clusters which is asynchronous and event driven so as to thoroughly eliminate the “ripple effect” brought by node mobility. As a result local changes in a cluster is not visible but is updated by the entire network, thus communication overheads is reduced enormously and suitable for the high mobility environment. Experimental results reveal that our proposed algorithm successfully achieves much less clustering overheads as well as maintaining much more stable cluster structure, as compared to High Connectivity Clustering algorithm.