Sensor Node Clustering Algorithm with Respect to Node Density in Wireless Sensor Networks

In clustering algorithms for wireless sensor networks, cluster heads close to the sink node usually encounter much more relay traffic and therefore lose energy rapidly. To address this problem in wireless sensor networks, distance-aware clustering approaches such as EEUC that adjust the cluster size according to the distance between the sink node and each cluster head have been proposed. However, the network lifetime of such approaches is highly dependent on the distribution of the sensor nodes because in randomly distributed sensor networks the approaches do not guarantee that the cluster energy consumption is commensurate with the cluster size. It might be necessary, for example, for sensors to be randomly distributed over the surveillance region (e.g., via aircraft). To solve this problem in wireless sensor networks, a new method called distribution based clustering algorithm (DBCA) was proposed in the present research which is not only aware of the distance but also the density of the sensor nodes. In DBCA, clusters have limited sensor nodes that are determined by the distance between the sink node and the cluster head. The simulation results show that DBCA is 25% to 45% more efficient than previous algorithms in terms of power consumption under different operating conditions.