Near-Optimal Node Clustering in Wireless Sensor Networks for Environment Monitoring

Wireless sensor networks (WSNs) for environment monitoring consist of a large number of low-cost battery-powered sensors nodes, densely deployed throughout a remote or inaccessible physical space. "Energy conservation" is identified as the key challenge in the design and operation of these networks. In our earlier work, we prove that WSN clustering schemes capable of positioning their resultant clusters within the isoclusters of the monitored phenomenon have the potential to reduced the nodes\´ energy consumption and, thereby, prolong the network lifetime. However, a careful analysis of the existing WSN clustering algorithms shows that these algorithms do not consider the similarity of sensed data as a clustering criterion, and therefore cannot provide optimal performance in terms of energy conservation. In this paper, a novel clustering algorithm, local negotiated clustering algorithm (LNCA), which employs the similarity of nodes\´ readings as an important criterion in cluster formation, is presented. LNCA greatly reduces the data-reporting related traffic with reasonable clustering cost. Simulations show that LNCA achieves considerable improvements over the most popular WSN clustering algorithm-low-energy adaptive clustering hierarchy (LEACH)