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

TL;DR

This paper introduces a Fast Adaptive S-ALOHA scheme for event-driven M2M communications that significantly reduces access delay and improves robustness under bursty traffic conditions.

Contribution

The paper presents a novel FASA scheme that uses statistics of consecutive idle and collision slots for faster network status tracking and guarantees quick convergence through drift analysis.

Findings

FASA achieves near-optimal delay performance.

FASA outperforms traditional additive schemes like PB-ALOHA.

FASA maintains robustness under heavy traffic loads.

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.