We present a method to reduce the device battery consumption to efficiently support battery‐operated machine‐to‐machine (M2M) communication in 3GPP long‐term evolution. The long‐term evolution discontinuous reception (DRX) is a key mechanism in reducing the device energy consumption, and we discuss how the traffic behaviour of machines in the Internet of things scenarios differs from the typical cellular user of today, for whom the current DRX mechanism is optimised for. We list typical transactions in M2M scenarios and discuss how the DRX operation is affected. We continue by introducing a power consumption model for M2M devices. Our assumption is that the device transmits small amounts of data in the uplink with deterministic intervals. The model takes into account the energy consumption in the active and the nonactive periods of the communication, which alternate depending on the DRX configuration. We use the model with different parameter settings referring to potential future M2M devices and identify the parameters, which contribute most to the device energy consumption. The results indicate that making the current maximum DRX cycle length longer will lead to significant gains in the energy consumption of M2M devices compared with what is possible today. Our key contributions include the discussion of the DRX mechanism in the Internet of things scenarios and the realistic assumptions for the potential of trading the responsiveness of a device for energy consumption gain with very long DRX cycles. Copyright © 2013 John Wiley & Sons, Ltd.

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The dual-control problem for time-varying or non-linear stochastic systems is inherently analytically and computationally untractable due to the demand of alternating minimizations and mean value computations. Hence, it has to be approached using approximations leading to suboptimal dual control. The core of the successful approximative controller is its ability to be able to consider future changes in the development of the parameters.This paper presents an analysis of the dual-control concept, and a comparison between a number of suboptimal controllers. The analytical comparisons are based on a reformulation of the dual-control problem. This reformulation makes it possible to interpret and understand the nature of di!erent approximations to dual control, in particular the Adaptive Predictive Controller (APC) and the Active Suboptimal Dual Controller (ASOD). Furthermore it makes the origin of the computational problems encountered more clear, and suggests new alternatives for approximation. The analysis is carried through on relatively simple examples and is illustrated with simulations. The performance of the controllers when applied to more complicated time-varying systems is illustrated with simulations as well.

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