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Abstract

Energy-efficient computing is the thrust area of research in an energy-constrained Wireless Sensor Network (WSN). Comb Needle Model (CNM) exists in literature for energy efficient data aggregation in regular (grid-based) WSNs. Clustering concept is added to CNM to reduce the energy consumption further. Besides, basic CNM is extended for randomly deployed WSNs. In this paper, the extended CNM for random WSNs is augmented with clustering mechanism. When clustering is added to the Extended CNM, it will aggregate the data at Cluster Head and minimizes the number of data transmissions, and thereby extends the network life span. The CNM uses the push-pull data distribution approach. It may overload certain sensor nodes, and lead to hotspots, which causes excess amount of energy loss. We extend the CNM with clustering in random network to overcome these issues and perform energy efficient processing. This paper makes the simulation based comparative analysis of the Extended CNM with clustering with that of without clustering. The performance metrics considered are energy consumption, communication cost, delay, packet loss, packet delivery ratio, and throughput. It is empirically observed that the network life span is improved substantially.