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

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, 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 outer cluster formation sub-problems and propose algorithms for solving the problems. While power control can be optimally solved for any given cluster structure by the proposed algorithm, cluster formation is an NP-hard problem. Hence, we propose an anytime, guided, stochastic search algorithm to find a reasonably good cluster structure without incurring prohibitive computation complexity. Compared with baseline approaches, our evaluation results show that data-centric clustering can achieve noticeable performance gain by selecting only important machines and forming a cluster structure that can balance the radio resource usage of the two tiers. We therefore motivate data-centric clustering as a promising communication model for resource-constrained M2M networks.