LAUSR

Internet of Things (IoT) imbued with several heterogeneous services and applications is integral to the ultimate realization of connected living. However, massive connectivity would pose huge challenges not only due to immense scalability but also due to diverse characteristics of periodicity, delay-criticality, and quality-of-service (QoS) requirement. One important trend of this diversity is that the connected devices would manifest different priorities. Random access procedure (RAP) is the first step by which most devices establish connection to the base station. A major transformation is urgently needed in RAP for cellular IoT as the volume of connected devices would far exceeds human oriented connections of the legacy networks. To prioritize the RAP for IoT devices, we propose novel online control algorithm that enables the most likely successful preamble transmission for the devices that manifest higher priority requirements. In the proposed algorithm, the number of active devices in each priority is recursively estimated based on Bayesian rule and then the preambles to each priority are accordingly allocated. We extend our proposal by adopting access class baring (ACB) to optimize the algorithm and subsequently, enhance it further by incorporating access delay requirements. Extensive simulations show the effectiveness of proposed algorithms over multiple priorities and confirm that the proposed algorithms are able to resolve congestions for massive activation of IoT devices.