Abstract
We develop a methodology for Multi-Channel Joint Forecasting-Scheduling (MC-JFS) targeted at solving the Medium Access Control (MAC) layer Massive Access Problem of Machine-to-Machine (M2M) communication in the presence of multiple channels, as found in Orthogonal Frequency Division Multiple Access (OFDMA) systems. In contrast with the existing schemes that merely react to current traffic demand, Joint Forecasting-Scheduling (JFS) forecasts the traffic generation pattern of each Internet of Things (IoT) device in the coverage area of an IoT Gateway and schedules the uplink transmissions of the IoT devices over multiple channels in advance, thus obviating contention, collision and handshaking, which are found in reactive protocols. In this paper, we present the general form of a deterministic scheduling optimization program for MC-JFS that maximizes the total number of bits that are delivered over multiple channels by the delay deadlines of the IoT applications. In order to enable real-time operation of the MC-JFS system, first, we design a heuristic, called Multi-Channel Look Ahead Priority based on Average Load (MC-LAPAL), that solves the general form of the scheduling problem. Second, for the special case of identical channels, we develop a reduction technique by virtue of which an optimal solution of the scheduling problem is computed in real time. We compare the network performance of our MC-JFS scheme against Multi-Channel Reservation-based Access Barring (MC-RAB) and Multi-Channel Enhanced Reservation-based Access Barring (MC-ERAB), both of which serve as benchmark reactive protocols. Our results show that MC-JFS outperforms both MC-RAB and MC-ERAB with respect to uplink cross-layer throughput and transmit energy consumption, and that MC-LAPAL provides high performance as an MC-JFS heuristic. Furthermore, we show that the computation time of MC-LAPAL scales approximately linearly with the number of IoT devices. This work serves as a foundation for building scalable JFS schemes at IoT Gateways in the near future.
Highlights
It is expected that the majority of the connections on the Internet in the near future will be between machines that operate without human intervention [1]
In Multi-Channel Joint Forecasting-Scheduling (MC-JFS), an Internet of Things (IoT) Gateway forecasts the future traffic generation patterns of individual IoT devices in its coverage area and schedules the future traffic of these devices in advance on multiple channels over a scheduling window. This approach to the solution of the massive access problem of IoT stands in sharp contrast with reactive protocols in which the IoT Gateway merely reacts to the current traffic demand
For the special case in which the uplink channel capacities seen by an individual IoT device are identical, we developed a reduction of this problem by virtue of which an optimal scheduling solution can be obtained in real time
Summary
It is expected that the majority of the connections on the Internet in the near future will be between machines that operate without human intervention [1]. This new paradigm, which is referred to as Machine-to-Machine (M2M) communication, typically originates at an Internet of Things (IoT) device that reports data and ends at an Artificial Intelligence (AI). While Fourth Generation (4G) cellular systems were designed to support Human-to-Human (H2H), Machine-to-Human (M2H) and Human-to-Machine (H2M) traffic [2], Fifth Generation (5G) and future wireless systems must effectively address the new challenges [3] brought by M2M communication.
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