To overhear or not to overhear: On correlated data gathering in M2M networks with limited radio resources

In this paper, we consider the problem of correlated data gathering in M2M (machine-to-machine) wireless networks with a large number of machines. Since machines communicate directly with the aggregator, the limited radio resources at the aggregator become the bottleneck for supporting all machines. Unlike related work that employs distributed source coding for minimizing resource usage, we assume that machines only perform local source coding. However, machines can leverage data overheard from transmissions of other machines for removing redundancy based on “dependent” source coding. To explore the performance tradeoffs of overhearing, we formulate a joint optimization problem involving node selection, resource allocation, and transmission scheduling, and then solve the problem based on the cross entropy method. Evaluation results show that without incurring the complexity of distributed source coding, dependent source coding via overhearing can achieve noticeable performance gain compared to independent source coding - even if the overhearing range and time are limited due to energy consideration. The results thus motivate further investigation for leveraging overhearing opportunities in M2M networks with limited radio resources.