  Massive connectivity for Internet of Things applications is expected to
challenge the way access reservation protocols are designed in 5G networks.
Since the number of devices and their density are envisioned to be orders of
magnitude larger, state-of-the-art access reservation, Random Access (RA)
procedure, might be a bottleneck for end-to-end delay. This would be especially
challenging for burst arrival scenarios: Semi-synchronous triggering of a large
number of devices due to a common event (blackout, emergency alarm, etc.). In
this article, to improve RA procedure scalability, we propose to combine Binary
Countdown Contention Resolution (BCCR) with the state-of-the-art Access Class
Barring (ACB). We present a joint analysis of ACB and BCCR and apply a
framework for treating RA as a bi-objective optimization, minimizing the
resource consumption and maximizing the throughput of the procedure in every
contention round. We use this framework to devise dynamic load-adaptive
algorithm and simulatively illustrate that the proposed algorithm reduces the
burst resolution delay while consuming less resources compared to the
state-of-the-art techniques.
