Energy-efficient spatially-adaptive clustering and routing in wireless sensor networks

Wireless sensor networks hold the potential to open new domains to distributed data acquisition. However, low-cost battery-powered nodes are often used to implement such networks, resulting in tight energy and communication bandwidth constraints. Cluster-based data compression and aggregation helps to reduce communication energy consumption. However, neglecting to adapt cluster sizes to local network conditions has limited the efficiency of previous clustering schemes. We have found that sensor node distances and densities are key factors in clustering. To the best of our knowledge, this is the first work taking these factors into consideration when adaptively forming data aggregation clusters. Compared with previous uniform-size clustering techniques, the proposed algorithm achieves up to 24% communication energy savings in uniform density networks and 36% savings in non-uniform density networks.