A point-distribution index and its application to sensor-grouping in wireless sensor networks
Yangfan Zhou, Haixuan Yang, Michael Rung-Tsong Lyu, Edith C.‐H. Ngai · 2006
Abstract — We propose ι, a novel index for evaluation of pointdistribution. ι is the minimum distance between each pair of points normalized by the average distance between each pair of points. We find that a set of points that achieve a maximum value of ι result in a honeycomb structure. We propose that ι can serve as a good index to evaluate the distribution of the points, which can be employed in coverage-related problems in wireless sensor networks (WSNs). To validate this idea, we formulate a general sensor-grouping problem for WSNs and provide a general sensing model. We show that locally maximizing ι at sensor nodes is a good approach to solve this problem with an algorithm called Maximizing-ι Node-Deduction (MIND). Simulation results verify that MIND outperforms a greedy algorithm that exploits sensorredundancy we design. This demonstrates a good application of employing ι in coverage-related problems for WSNs. I.