Performance Improvements of Batch Data Model for Machine-to-Machine Communications

In recent years, rapid growth in the popularity of machine-to-machine (M2M) applications has enabled the deployment of a large number of M2M devices. In addition, 3GPP proposes the use of higher layer connections among M2M devices to link to LTE-advanced core networks. To support the massive numbers of device connections, it is essential that the cost imposed by the unnecessary transmission of data in core networks be reduced. This paper investigates how to apply the batch data model to reduce the data update frequency in M2M core networks. To achieve an acceptable ratio of updated devices while avoiding a high data update rate from the devices/gateway to the server, for each data access issuing by an M2M application, we dynamically pull not-yet-updated data from the gateway in respect to different kinds of application requirements. An analytical model is developed for the proposed batch data model with data pulling to evaluate the transmission cost and updated device ratio in M2M networks.