Edge Computing-Enhanced Uplink Scheduling for Energy-Constrained Cellular Internet of Things

Abstract

Machine Type Communications (MTC) supports various Machine-to-Machine (M2M) applications and Internet of Things (IoT) services, where the energy efficiency is a key issue. In addition, because the number of IoT devices is rapidly growing, the improvement of the quality of service (QoS) becomes necessary. Many studies investigated the trade-off between the power consumption and the QoS, but did not improve the both at the same time. In this paper, we propose an scheduling algorithm assisted by a emerging technology, Multi-access Edge Computing (MEC), and our algorithm reduces the power consumption the latency as much as possible. In order to evaluate our algorithm, we use OpenAirInterface (OAI) as a testbed, because the simulation platform of LTE is fully developed and reliable. In this paper, the overview of OAI scheduler is introduced, and the implementation of our algorithm using OAI are detailed. Compared with Round-Robin (RR) and Proportional fair (PF) algorithm, our performance study shows that our algorithm improves the performance in terms of the energy consumption and the system performance. Moreover, through the simulation results, we analyze the relation between the computational capability of the MEC server and the system performance.