A Machine-Learning-Based Channel Assignment Algorithm for IoT

Abstract

Multi-channel technique benefits IoT network by support parallel transmission and reduce interference. However, the extra overhead posed by the multi-channel usage coordination dramatically challenges the resource constrained IoT devices. In this paper, a machine-learning-based channel assignment algorithm utilizing Tug-Of-War (TOW) dynamics is proposed to cognitively select channels for communication in massive IoT. Furthermore, the proposed TOW-dynamics-based channel assignment algorithm has simple learning procedure which only needs to receive Acknowledge frame for learning procedure, meanwhile, only needs minimal memory and computation capability, i.e., addition and subtraction procedure. Thus, the proposed TOW-dynamics-based algorithm is possible to run on resource constrained IoT devices. We prototype the proposed algorithm on extremely resource constrained Single-board Computer, which is called cognitive IoT device hereafter. Moreover, the evaluation experiments that densely deployed cognitive IoT devices in the frequently changed radio environment are conducted. The evaluation results show that cognitive IoT device quickly make decision to selects channel when the real environment frequently changed, meanwhile keep fairness among IoT devices.