Dynamic Coverage and Clustering: A Maximum Entropy Approach

摘要: We present a computational framework we have recently developed for solving large class of dynamic coverage and clustering problems, ranging from those that arise in the deployment mobile sensor networks to identification ensemble spike trains neuroscience applications. This provides natural clusters an underlying dataset, while addressing inherent tradeoffs such as between cluster resolution cost.More specifically, define problem minimizing instantaneous metric combinatorial optimization Maximum Entropy Principle framework, which formulate specifically setting. Locating tracking centers is cast control design ensures algorithm achieves progressively better with time.