Energy-efficient scheduling scheme with spatial and temporal aggregation for small and massive transmissions in LTE-M networks

Abstract

Machine-to-machine (M2M) communication is one of the key technologies to realize Internet of Things (IoT). Since IoT applications are mainly for smart sensing, such as metering, home surveillance, disaster detection, and e-health, their special sensing/uploading behaviors will result in periodic and/or event-driven small data transmissions, which may potentially decrease the radio resource efficiency. On the other hand, due to the frequent communication nature of sensing data for IoT applications, the power consumption is increasing dramatically. To reduce the power consumption of IoT devices, the 3rd Generation Partnership Project (3GPP) has defined the discontinuous reception/discontinuous transmission (DRX/DTX) mechanism to allow devices to turn off their radio interfaces and go to sleep in various patterns. However, how to optimize the DRX/DTX scheduling while improving the resource efficiency is still an open issue. In this paper, we investigate an uplink resource allocation problem over long-term evolution machine-to-machine (LTE-M) networks, which is standardized by 3GPP to improve performance on IoT. In this network, we consider the periodic, event-driven, and query-based IoT traffic while minimizing the devices' power consumption. We prove this problem to be NP-complete and propose an aggregation-efficient DRX/DTX scheduling (AEDS) scheme. This scheme takes advantage of both spatial and temporal data aggregation while applying DRX/DTX for energy saving. Specifically, the scheme consists of three phases. The first phase exploits long-term static scheduling for periodic data to ensure the latency and data rate while minimizing the devices' wake-up time. The second phase tries to decrease devices' power consumption through precisely determining their DRX/DTX configurations. Finally, the third phase employs short-term dynamic scheduling for event-driven and query-based data to improve transmission efficiency. Therefore, both small data and power consumption problems are relieved. Extensive simulation results show that the proposed scheme can improve resource efficiency, enlarge network capacity while reducing power consumption compared to the existing schemes. (C) 2018 Elsevier B.V. All rights reserved.