A Scalable MAC Framework for Internet of Things Assisted by Machine Learning

The vision of the Internet-of-Things (IoT) networks calls for a large number of power constrained devices communicating with the gateway. To achieve the channel coordination in IoT, IEEE 802.15.4 standard has been considered as one of the most competitive technologies. However, the length of the Contention Access Period (CAP) of the superframe can hardly adapt to the variation of network traffic, so the performance of IoT is restricted. To resolve the problem, we propose a scalable MAC framework assisted by Machine Learning, called MML. With the implementation of machine learning algorithms such as Neural Network Predictor (NNP), the gateway can detect the number and type of devices from the overlapped signals, as demonstrated in our Universal Software Radio Peripheral (USRP2) testbed. Therefore, MML can dynamically adjust the CAP length based on the knowledge of the number of active devices and a stable throughput can be achieved. Moreover, the throughput of MML is analyzed, which is verified by conducting simulations using network simulator (ns-2.35). The analytical and simulation results demonstrate the superiority of the proposed MML.