Leveraging overhearing for correlated data gathering in M2M communications with limited radio resources

We consider the problem of data gathering from a set of machines deployed arbitrarily in a source field. Machines are assumed to communicate directly with the aggregator (sink) using the allocated radio resource. Since the aggregator has limited radio resources to allocate for machine communications, only a subset of machines can be selected for uplink transmissions. Missing data from unselected machines is reconstructed by the aggregator through exploiting correlation of gathered data. To further reduce resource usage, each selected machine performs local source coding before the data is transmitted to remove redundant information that it overhears from other machines. In such a scenario, the aggregator needs to optimally select the subset of machines for resource allocation and then schedule their orders of transmissions to minimize the overall data distortion. We first formulate a joint optimization problem for the target scenario and then propose an algorithm based on the cross entropy method to solve the problem. Evaluation results show that dependent source coding based on overhearing can achieve significant performance gain compared to independent source coding without overhearing. The gain is still noticeable even if the overhearing time is limited to due energy consideration.