Energy Efficient Mobile Sink Path Selection Using a Cluster Based Approach in WSNs

Abstract

Data collection and Aggregation is most important task in the wireless sensor network energy saving can be achieved in wireless sensor networks by using mobile Sink (MS) capable of carrying data in unconstrained path. In large –density applications, the sensor node should be transmit the collected data to mobile sink within time constraint. In the existing system, to form a hybrid moving pattern in which a mobile-sink node only visits rendezvous points (RPs), as opposed to all nodes for data collection. Due to Processing overhead of Rendezvous point is not appropriate for large and delay sensitive applications. To overcome this problem, the proposed method is a Clustering Based Weighted Rendezvous Planning (CBWRP) algorithm for sensor networks. The sensor nodes are ordered into clusters. Inside a cluster, nodes transfer data to cluster head (CH) through routing protocol. Cluster Head is elected for each cluster based on the High energy first (HEF) clustering algorithm. This algorithm is used for selecting cluster head with high ranking of residual energy of each sensor node. This algorithm is used for minimizes the energy depletion throughout sensor network. CH has a responsibility for collecting data from each sensor node and transmits data to nearest Rendezvous point. The travelling path of Mobile Sink (MS) to visit all Rendezvous point which can be considered as a NP Hard problem. This problem taken as delay-aware energy efficient path (DEETP) and solved as Weighted Rendezvous Planning (WRP) algorithm.WRP preferentially designates sensor nodes with the highest weight as a RP with distance of RP should be less than of maximum allowed tour length. WRP algorithm calculating weight for each sensor node which can be computed by hop distance and number of packets forwarded.

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Cite this paper

@inproceedings{Brindha2015EnergyEM, title={Energy Efficient Mobile Sink Path Selection Using a Cluster Based Approach in WSNs}, author={L . Brindha and U . Muthaiah}, year={2015} }