A Cluster Based Weighted Rendezvous Planning for Efficient Mobile-Sink Path Selection in WSN

Data collection and Aggregation is most important task in the large –density applications of wireless sensor network. In the existing system, a mobile-sink node visits Rendezvous Points (RPs), as opposed to all nodes for data collection. Due to processing overhead of Rendezvous point is not appropriate for large scale applications. To overcome this problem, the proposed method is a Clustering Based Weighted Rendezvous Planning (CBWRP) algorithm. The sensor nodes are organized into clusters and each Cluster Head (CH) has a responsibility for collecting data from each sensor node and transmits data to nearest Rendezvous Point. High Energy First (HEF) clustering algorithm is used for selecting node as CH which has more residual energy. The travelling path of Mobile Sink (MS) to visit all Rendezvous point which can be obtained by Weighted Rendezvous Planning (WRP) which can be calculating weight for each sensor node which can be computed by hop distance and number of packets forwarded. An NS2 simulation demonstrates CBWRP reduces energy consumption and increases network lifetime as compared with existing algorithms.