Fast Adaptive S-ALOHA Scheme for Event-Driven Machine-to-Machine Communications

Abstract

Abstract-Machine-to-Machine (M2M) communication is now playing a market-changing role in a wide range of business world. However, in event-driven M2M communications, a large number of devices activate within a short period of time, which in turn causes high radio congestions and severe access delay. To address this issue, we propose a Fast Adaptive S-ALOHA (FASA) scheme for M2M communication systems with bursty traffic. The statistics of consecutive idle and collision slots, rather than the observation in a single slot, are used in FASA to accelerate the tracking process of network status. Furthermore, the fast convergence property of FASA is guaranteed by using drift analysis. Simulation results demonstrate that the proposed FASA scheme achieves near-optimal performance in reducing access delay, which outperforms that of traditional additive schemes such as PB-ALOHA. Moreover, compared to multiplicative schemes, FASA shows its robustness even under heavy traffic load in addition to better delay performance.

Online Supervised Learning for Traffic Load Prediction in Framed-ALOHA Networks

“…where the N max is an upper bound on the backlog. Unless one makes simplistic assumptions as in [4][5][6], problem (1) remains generally intractable even in the presence of realistic traffic models, further justifying the data-driven solutions developed in prior art and in this letter. We emphasize that we focus solely on the problem of prediction and that we leave the problem of investigating the interplay between overload control via, e.g., frame size selection and access barring, and traffic prediction to future work.…”

“…Previous classical works have proposed Method of Moment (MOM)-based estimators that aim at matching the average number of such RAOs to the current measurements [3]. More recent works proposed to predict bursty traffic for event-driven applications, i.e., for massive devices being activated by an external event to request transmissions within a short period, using drift analysis [4], MOM [5], or Maximum-Likelihood (ML) estimation [6]. All these prior works estimate the current backlog only based on the latest observations of idle, collided, or successful RAOs, while ignoring historical data from prior frames.…”

Reconfigurable and Traffic-Aware MAC Design for Virtualized Wireless Networks via Reinforcement Learning

“…However, the results are derived for a single application in which all devices have the same traffic parameters. Similarly, [12] considers an M2M network where all devices are reporting the same event. In this work, the authors proposed a pure random-based channel access for devices to report the event, in which the optimal transmission probability is dependent on the number of active devices.…”

“…In this work, to reduce the congestion in the large scale network, the access clear bearing (ACB) method is proposed. Similar to [11], [12], considering a network running a single application might be unrealistic for the future networks as different applications may share the same infrastructure.…”

Reduction of Network Congestion in M2m Communication

“…If N1 is bigger than 31, then they should retry after 512 seconds. Otherwise, RACH procedure is initiated [6] .…”