LAUSR

Resource allocation, or scheduling, is one of the main challenges that face supporting machine-to-machine (M2M) communications on long term evolution networks. M2M traffic has unique characteristics. It generally consists of a large number of small data packets, with specific deadlines, generated by a potentially massive number of devices contending over the scarce radio resources. In this paper, we introduce a novel M2M scheduling metric that we term the “statistical priority”. Statistical priority is a term that indicates the uniqueness of the information carried by certain data packets sent by machine-type communications devices (MTCDs). If an MTCD data unit is significantly dissimilar to the previously sent data, it is considered to carry non-redundant information. Consequently, it would be assigned higher statistical priority, and this MTCD should then be given higher priority in the scheduling process. Using this proposed metric in scheduling, the scarce radio resources would be used for transmitting statistically important information rather than repetitive data, which is a common situation in M2M communications. Simulation results show that our proposed statistical priority-based scheduler outperforms the other baseline schedulers in terms of having the least number of deadline misses (less than 4%) for critical data packets. In addition, our scheduler outperforms the other baseline schedulers in non-redundant data transmission as it achieves a success ratio of at least 70%.