부산대학교 도서관

The broad deployment of 802.11 (a.k.a. Wi-Fi) access points and the significant energy-efficiency improvement of 802.11 transceivers have resulted in increasing interest in building 802.11-based Internet of Things (IoT) systems. Unfortunately, the power saving mechanisms of 802.11 fall short when used in IoT applications, especially because they do not take into account the delays caused by various factors, such as buffering, interference, and round-trip delay. In this article, we present edge-assisted predictive sleep scheduling (EAPS) to adjust the sleep duration of stations while they are expecting downlink packets. We first implement a Linux-based access point that enables us to collect parameters affecting communication latency. Using this access point, we build a testbed that, in addition to offering traffic pattern customization, replicates the characteristics of real-world environments. We then use multiple machine learning algorithms to predict downlink packet delivery. Our empirical evaluations confirm that with EAPS, the energy consumption of IoT stations is as low as power save mode, whereas the delay of packet delivery is close to the case where the station is always awake.