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This paper discusses a point-distribution index, l, which measures the normalized minimum distance between sensors. Maximizing l of a set of points causes the Delaunay triangulation graph of these points to be a net of equilateral triangles. Such a structure indicates the lowest redundancy of coverage if each point represents the center of a disc. Thus l can serve as a promising measure for solving a critical problem in field coverage: How to group a set of sensor nodes into disjoint subsets so that each subset can cover the entire field? Based on the l index, we develop an effective algorithm, MAXINE (MAXimizing-l Node-redundancy Exploiting), for the sensor- grouping problem. We evaluate the performance of MAXINE through extensive simulations and compare it with existing algorithms. The results demonstrate the effectiveness of MAXINE and verify the superiority of employing i for the sensor-grouping problem.