Massive Access Control Aided by Knowledge-Extraction for Co-Existing Periodic and Random Services over Wireless Clinical Networks

The prosperity of e-health is boosted by fast development of medical devices with wireless communications capability such as wearable devices, tiny sensors, monitoring equipments, etc., which are randomly distributed in clinic environments. The drastically-increasing population of such devices imposes new challenges on the limited wireless resources. To relieve this problem, key knowledge needs to be extracted from massive connection attempts dispersed in the air towards efficient access control. In this paper, a hybrid periodic-random massive access (HPRMA) scheme for wireless clinical networks employing ultra-narrow band (UNB) techniques is proposed. In particular, the proposed scheme towards accommodating a large population of devices include the following new features. On one hand, it can dynamically adjust the resource allocated for coexisting periodic and random services based on the traffic load learned from signal collision status. On the other hand, the resource allocation within periodic services is thoroughly designed to simultaneously align with the timing requests of differentiated services. Abundant simulation results are also presented to demonstrate the superiority of the proposed HPRMA scheme over baseline schemes including time-division multiple access (TDMA) and random access approach, in terms of channel utilization efficiency, packet drop ratio, etc., for the support of massive devices’ services.