Unequal Distributed Spatial Correlation-based Tree Clustering for Approximate Data Collection
Maiying Shen, Shuo Chen · Advances in intelligent systems research/Advances in Intelligent Systems Research · 2014
Dividing the networks into several unequal sizes of clusters and the nodes with similar readings and neighboring geographical location are in the same cluster is an effective way to prolong the WSN lifetime.Most applications in WSNs can tolerate certain accuracy loss of the sensor readings and we can exploit the tradeoff between data accuracy and energy consumption.In this paper, we present an improved protocol called UDSCTC (Unequal Distributed Spatial Correlationbased Tree Clustering for Approximate Data Collection).We modify the radius of node competition depending on the distance of the nodes to the sink node to make close to the sink node of the cluster radius decreases, and realize the compromise of energy-consumption between intra and inter clusters.At the same time, we make the clusters' data forwarded to the sink node by multi-hops like tree architecture in traditional network.The method can enlarge the network area.Simulation result shows that UDSCTC has some improvement in the number of the cluster, the energy consumption, etc.