Gossip-based density estimation in dynamic heterogeneous sensor networks

The density estimation of diverse sensor types in a heterogeneous sensor network is a useful and challenging service that can be applied in clustering schemes, node redeployment, and sleep mode scheduling. Energy efficiency is one of the main requirements for any wireless sensor network service. Besides, the service has to provide a fresh version of the estimation to each node. Network dynamics, especially node mobility, introduce new challenges. Moreover, churn makes the problem even more complicated. In this paper we introduce a gossip-based approach for the density estimation of sensor diversity in clustered dynamic networks. The devised method supports node mobility and churn, as well as redeployment of new nodes. It is fully distributed and adaptive to network dynamics. We analyze the effect of mobility as well as scalability in the number of clusters and the quantity of nodes. Our algorithm has a fast convergence speed and provides more accurate estimation compared to similar approaches.