A Novel Congestion Reduction Scheme for Massive Machine-to-Machine Communication

Machine-to-machine (M2M) communication is a system that allows information interaction between machines independently and automatically through a network without human intervention. However, when massive M2M devices access the network, they can quickly scramble preambles, and induce significant network congestion. Specially, when the massive M2M devices consist of delay tolerant devices (DTDs) and delay sensitive devices (DSDs), DSD success rate will decrease sharply. Therefore, this paper proposes a novel scheme of congestion reduction, Markov chain-based access class barring (M-ACB) to guarantee random access success for massive M2M devices that incorporate DTDs and DSDs, and ensure network resources are utilized efficiently. The proposed M-ACB scheme uses a 6D Markov chain to model preamble transfer status of preambles, and estimate the number of access devices for the next time slot. Dynamic regulation of barring factors and preserved DTD and DSD preamble is then applied based on the estimate. Simulation results validated the proposed M-ACB scheme for several key performance indicators, such as success rate, collision rate, time delay, and repeat times.