Energy Efficient Clustering Approach for Distributing Heavy Data Traffic in Wireless Sensor Networks

Energy Efficient Clustering Approach for Distributing Heavy Data Traffic in Wireless Sensor Networks

NkemnoleE. Bridget 

Department of ECE, Pondicherry Engineering College, Puducherry, India

Department of ECE, Pondicherry Engineering College, Puducherry, India

Corresponding Author Email:;
July 2017
30 October 2017
31 December 2020
| Citation



A wireless sensor network is made up of a large number of sensor nodes deployed on a wide field and it has limited battery lifetime which gets depleted at a faster rate, when heavy data traffic occurs. Most recent researches focused that, clustering the group of nodes is a better strategy for enhancing the lifetime of the sensors and also clustering organizes the network by balancing the traffic load of the sensor nodes. Inspired by the benefits of clustering approach, Event Based Routing Protocol (EBRP) was proposed. The proposed protocol involves three procedures. First procedure refers to a cluster head selection, which appoints cluster head based upon residual energy which is near to the sink node. The residual nodes in the network are to be designated as cluster head at the successive rounds. This process helps to balance the load evenly in the network. Second phase refers to an Event sensing procedure, which appoints a set of active nodes for close sensing an event and to provide coverage area near to the event. Third step refers to a node routing, to route the witnessed information based upon the shortest path. This proposed method uses residual energy for appointing a cluster head. This proposed protocol was implemented and the experimental results were shown through the network simulator. The proposed protocol outperforms the existing routing techniques in terms of alive nodes, packet delivery ratio, average remaining energy and end-to-end delay.


Clustering, Energy efficiency, Event sensing, Residual energy

1. Introduction
2. Related Works
3. Proposed System
4. Simulation Results
5. Conclusion

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