Towards efficient M2M communications for Internet of Things

Abstract

Machine-to-Machine (M2M) communication utilizes wired or wireless communication channels to enable universal data transmission between massive devices under little or no human intervention. With an unparalleled number of M2M devices being deployed to support a variety of smart-world systems powered by Internet of Things technologies, the heterogeneity, uncertainty and complexity of M2M communications have increased enormously and resource scheduling is challenged. To address these issues, in this dissertation, we have made two contributions. First, we propose a time series framework (TSF) to model and forecast timescale network traffc dynamics in M2M communications and it is capable of providing useful guidance for effective resource scheduling. Second, we model the traffc arrival patterns of time-driven, event-driven and loop-driven M2M applications and develop a dynamic rate adaptation (DRA) scheme to obtain an optimized service rate distribution among a mixture of diverse M2M applications.