Context-aware Machine-to-Machine communications

As a key enabler of Internet of things, cellular network based Machine-to-Machine (M2M) communications have been growing rapidly in recent years, being used in a wide range of services such as security, metering, health, remote control, tracking, and so on. A critical issue in M2M communications is the energy efficiency as typically the machine devices are powered by batteries of low capacity and thus, it is the key to optimize their consumption. To achieve higher energy efficiency, this paper proposes the adoption of contexts through a generic context-aware framework for M2M communications. With this framework, machine devices dynamically adapt their settings depending on a series of characteristics such as data reporting mode, QoS features, and network conditions to achieve higher energy efficiency and extend the operating lifetime of M2M networks. Simulation results are provided for four commonly used M2M applications. The results demonstrate considerable energy savings and operating lifetime extension on the network when the proposed context-aware framework is used. Thus, it is shown that contexts play an important role on the energy efficiency of M2M systems.