Not every bit counts: A resource allocation problem for data gathering in machine-to-machine communications

Many applications involving machine-to-machine (M2M) communications are characterized by the large amount of data to transport. To address the “big data” problem introduced by these M2M applications, we argue in this paper that instead of focusing on serving individual machines with better quality, one should focus on solutions that can better serve the data itself. To substantiate this concept, we consider the scenario of data gathering in a wide area by machines that are connected to a central aggregator through direct wireless links. The aggregator has limited radio resources to allocate to machines for uplink transmission of collected data, and hence the problem arises as to how the resources can be effectively utilized for supporting such an M2M application. In contrast to conventional approaches on maximizing the number of machines that can access the radio resources, we investigate an approach that takes into consideration “useful” information content that individual machines can provide for prioritization of resource allocation. Numerical results based on the proposed algorithms show that although the number of machines that can be supported is not maximized, the data so collected at the aggregator does exhibit significant quality gain for the target M2M scenario, thus motivating further investigation along this direction.