A Multiscale Algorithm for Joint Forecasting-Scheduling to Solve the Massive Access Problem of IoT

Abstract

The massive access problem of the Internet of Things (IoT) is the problem of enabling the wireless access of a massive number of IoT devices to the wired infrastructure. In this article we describe a multiscale algorithm (MSA) for joint forecasting-scheduling at a dedicated IoT gateway to solve the massive access problem at the medium access control (MAC) layer. Our algorithm operates at multiple time scales that are determined by the delay constraints of IoT applications as well as the minimum traffic generation periods of IoT devices. In contrast with the current approaches to the massive access problem that assume random arrivals for IoT data our algorithm forecasts the upcoming traffic of IoT devices using a multilayer perceptron architecture and preallocates the uplink wireless channel based on these forecasts. The multiscale nature of our algorithm ensures scalable time and space complexity to support up to 6650 IoT devices in our simulations. We compare the throughput and energy consumption of MSA with those of reservation-based access barring (RAB) priority based on average load (PAL) and enhanced predictive version burst-oriented (E-PRV-BO) protocols and show that MSA significantly outperforms these beyond 3000 devices. Furthermore we show that the percentage control overhead of MSA remains less than 1.5%. Our results pave the way to building scalable joint forecasting-scheduling engines to handle a massive number of IoT devices at IoT gateways. © 2020 Elsevier B.V. All rights reserved.