Ubiquitous sensor network has a history of applications varying from monitoring troop movement during battles in WWII to measuring traffic flows on modern highways. In particular, there lies a computational challenge in how these data can be efficiently processed for real-time intelligence. Given the data collected from ubiquitous sensor networks that have different densities distributed over a large geographical area, one can see how separate groups could be formed over them in order to maximize the total coverage by these groups. The applications could be either destructive or constructive in nature; for example, a jet fighter pilot needs to make a real-time critical decision at a split of second to locate several separate targets to hit (assuming limited weapon payloads) in order to cause maximum damage, when it flies over an enemy terrain; a town planner is considering where to station certain resources (sites for schools, hospitals, security patrol route planning, airborne food ration drops for humanitarian aid, etc.) for maximum effect, given a vast area of different densities for benevolent purposes. This paper explores this problem via optimal "spatial groups" clustering. Simulation experiments by using clustering algorithms and linear programming are to be conducted, for evaluating their effectiveness comparatively.