Correlation-aware machine selection for M2M data gathering in cellular networks

In machine-to-machine communications, several machines located close to one another can form a cluster to leverage spatial reuse gains and energy savings. We investigate a machine selection algorithm where only a machine in each cluster transfers its data to the cellular user via device-to-device communication and then the cellular user forwards it to the base station. Unlike the previous works that focus on data rate or channel condition as the selection metrics, this paper proposes a correlation-aware selection algorithm which maximizes the joint entropy extracted from the data delivered from the selected machines. Our evaluation results show the proposed selection scheme reaps a significant gain and is near-optimal.