  Supporting massive device transmission is challenging in Machine-to-Machine
(M2M) communications. Particularly, 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 random access control of M2M
communication systems with bursty traffic. Instead of the observation in a
single slot, the statistics of consecutive idle and collision slots are used in
FASA to accelerate the tracking process of network status which is critical for
optimizing S-ALOHA systems. Using drift analysis, we design the FASA scheme
such that the estimate of the backlogged devices converges fast to the true
value. Furthermore, by examining the $T$-slot drifts, we prove that the
proposed FASA scheme is stable as long as the average arrival rate is smaller
than $e^{-1}$, in the sense that the Markov Chain derived from the scheme is
geometrically ergodic. Simulation results demonstrate that the proposed FASA
scheme outperforms traditional additive schemes such as PB-ALOHA and achieves
near-optimal performance in reducing access delay. Moreover, compared to
multiplicative schemes, FASA shows its robustness under heavy traffic load in
addition to better delay performance.
