With the emergence of a large number of businesses and applications based cellular Machine-to-Machine M2M communication such as telematics, smart metering, point-of-sale terminals, and home security, a heavy data traffic which need through the cellular network has been produced. Although many schemes have been proposed to reduce data traffic, they are inefficient in practical application due to poor adaption. In this paper, we focus on how to adaptively offload data traffic for cellular M2M networks. To this end, we propose an adaptive data traffic offloading model AOM. This model can decide whether to adopt opportunistic communications or communicate via cellular networks adaptively. In the AOM, we introduce traffic offloading rate called TOR and local resource consumption rate called LRCR, and analyze them based on continue time Markov chain CTMC. Theory proof and extensive simulations demonstrate that our model is accurate and effective, and can adaptively offload data traffic of cellular M2M networks.
['Tao Lei', 'Shangguang Wang', 'Fangchun Yang']
An Adaptive Data Traffic Offloading Model for Cellular Machine-to-Machine Networks