Minimizing Radio Resource Usage for Machine-to-Machine Communications through Data-Centric Clustering

Abstract— While clustered communication has been considered as one key technology for wireless sensor networks, existing work on cluster formation predominantly takes a pure graph-theoretic approach with the goal of optimizing the performance of individual machines. Since the radio resource available for M2M communications is typically limited yet the amount of data to transport is large, Radio Resource such “resource-agnostic” and “data-agnostic” clustering techniques could lead to sub-optimal performance. To address this problem, we propose “data-centric” clustering in a resource-constrained M2M network by prioritizing the quality of overall data over the
performance of individual machines. We first formulate an optimization problem to minimize the amount of radio resource needed for supporting two-tier clustered communications. We then partition the formulated problem into the inner power control and outercluster formation sub-problems and propose algorithms for solving the problems. < final year projects >