Abstract
Machine-to-Machine (M2M) communication technology has been utilized in 4G LTE to support a variety of M2M applications, which require real-time data reporting. In the literature, many uplink scheduling algorithms have been proposed for 4G LTE. Nevertheless, most of these scheduling algorithms are mainly designed for human-to-human applications, not for M2M applications. These scheduling algorithms typically focus on the fact that the machines in M2M communication are battery-powered. Considering that the energy budget of the battery is limited, these scheduling schemes aim to reduce the energy consumption. We note that the current literatures consider either the problem of sleep-time maximization or transmission power minimization, but not both. In this paper, we systematically consider all energy-consumption factors of M2M real-time reporting. We propose two energy-efficient scheduling algorithms to minimize the total energy consumption of the machines and these two algorithms depend on the distance between a machine and the serving LTE eNB. The first algorithm is for the case when the distance is long and it aims to reduce the energy consumption for the machines to transmit their data. The second algorithm is for the case when the distance is short. It aims to reduce the active slots of the machines and also determines whether the machines are worth going into the sleep mode or just staying at the active mode but idle state. The experiment results show that our algorithms have better performance than the com- pared algorithms in terms of energy consumption. Additionally, our algorithms can also maintain scheduling success ratios and fairness.