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. I T has been envisioned that a huge amount of machines will be installed and inter-connected in the near future to facilitate better living experiences for human beings through various M2M (machine-to-machine) applications such as home automation, neighbourhood surveillance, intelligent transportation, and smart energy. Different from conventional wireless sensor networks (WSNs), in many of these M2M applications machines are not necessarily limited in the form factor, processing capability, and energy supply as most sensors are. To effectively support long-range, large-scale M2M communications, therefore, several standardization bodies for next-generation communication systems have actively investigated mechanisms for machine-type communications (MTC).