Clustering with dynamic route adjustment and fuzzy based update cycle in wireless sensor networks with sink mobility

Abstract

Dynamic clustering is always regarded as a useful strategy in wireless sensor networks (WSN) to balance the energy dissipation of network nodes. However, the sensor nodes are burdened and lose power as a result of the message exchange required for recurrent clustering in order to change the role of the cluster head. In this paper, a clustering scheme for WSN with mobile sink is presented that minimizes message exchanges and clustering cost. In this paper we consider a virtual grid structure in the network area or sensor field. The field is divided into a number of cells that behave as clusters of uniform size. Cluster heads create a virtual backbone network and mobile sink moves at a relatively optimal predefined path inside the network. Dynamic routes re-adjustment cost incurred by sink mobility is minimized by virtual backbone network. Reduced re-adjustment cost save the energy of the cluster heads. This scheme effectively schedules the clustering task by calculating the update cycle with the help of fuzzy inference system. Next Update cycle is decided with the help of residual energy of nodes, average sensed data rate and the distance of cluster head from the mobile sink. Fuzzy inference system selects optimal length of update cycle and save the energy used for recurrent dynamic clustering and cluster head selection. The proposed scheme improves the lifetime of sensor network.