Efficient sensor data fusion is one of the more critical and challenging tasks in building practical sensor networks. It is widely understood that transmitting raw sensor data to a central location for processing is severely hampered by scaling, in terms of energy consumption and latency costs, in large scale wireless networks. However, many detection, classification, estimation, and phenomena modeling algorithms rely heavily on the individual data from each sensor and thus require raw data collection, if not from the entire network, then at least among localized node clusters of varying sizes. In order to make the data collection as efficient as possible, various compression and fusion techniques have been proposed and are currently being investigated. In addition to the compression and fusion algorithms, the topology of the aggregation, e.g. the clusters and routes used, can play a significant role in the achievable compression rates. In this paper, we investigate the problem of cluster formation for data fusion by focusing on two aspects of the problem: (i) how does one estimate the number of clusters needed to efficiently utilize data correlation of sensors for a general sensor network, and (ii), given the number of clusters, how does one pick the cluster-heads (sinks of information) to cover the sensor network more efficiently. We start by first analytically deriving and analyzing the number of required cluster heads. We then propose an algorithm for the head selection. Simulation results are used to investigate the performance of the algorithm compared to exhaustively found optimal solutions which show that significant improvements in energy efficiency of the fusion algorithms can be obtained through minimal efforts spent on optimizing the cluster head-selection process
['Hao Chen', 'Seapahn Megerian']
Cluster sizing and head selection for efficient data aggregation and routing in sensor networks