Machine-type-communications (MTC) in IoT is different from human-to-human (H2H) communications in terms of traffic patterns: (1) most MTC traffic is uplink, small in data size, but high in session frequency; (2) MTC devices are mostly inactive, and is active only when there is data to transmit. These will cause a significant problem in future network: the large amount of signal traffic generated by control plane. However, traditional radio management mechanisms, such as RRC and DRX, cannot fully support or efficiently manage the excessive access attempts generated by machine-type-devices.
In this work, we provide a session management methodology suitable for IoT traffic over LTE. Our analysis starts with a Markov Chain analysis of the impact of DRX parameters. This is followed by an optimal uplink scheduler design and an IoT-aware adaptive DRX algorithm at the client, both of which modulate the tradeoff among signal load, delay and power consumption. Scalability is also considered in this work by providing a high-priority clustering-based adaptive DRX algorithm at eNB. Simulation results show that for packets with 0:1s delay, our scheduler outperforms ‘Tx now’ (and ‘Wait Till Deadline’) by 50% (and 30%) in power saving, by 60% (and 15%) in signal saving. With knowledge of the traffic pattern, IoT-aware adaptive DRX can further reduce signal load by at least 25%, especially for delay-sensitive traffic.
X. Wang, M. Sheng, Y. Lou, Y. Shih, M. Chiang, “Internet of Things Session Management over LTE - Balancing Signal Load, Power and Delay”, IEEE Internet of Things Journal, 2015 [paper]