Gossip-based density estimation in dynamic heterogeneous wireless sensor networks

The density estimation of diverse sensor types in a heterogeneous sensor network is an important service that can be used in clustering schemes, node redeployment and sleep scheduling strategies. Similar to any wireless sensor network service, energy effi ciency is one of the main requirements. The service has to provide an updated estimation at each node. Network dynamics, especially node mobility, introduce new challenges. Moreover, churn makes the problem even more complicated. In this paper we introduce a new approach called Gossip based Density Estimation (GDE) for heterogeneous dynamic networks. The devised method is able to cope with node mobility and churn, as well as redeployment of new nodes. It is fully distributed and adaptive to network dynamics. We analyse the effect of mobility as well as increased scale in the number of clusters and the quantity of nodes. The simulation results support the idea that our algorithm has a fast convergence speed and provides more accurate estimation compared to similar approaches.