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The broad deployment of 802.11 (a.k.a., WiFi) access points and significantenhancement of the energy efficiency of these wireless transceivers hasresulted in increasing interest in building 802.11-based IoT systems.Unfortunately, the main energy efficiency mechanisms of 802.11, namely PSM andAPSD, fall short when used in IoT applications. PSM increases latency andintensifies channel access contention after each beacon instance, and APSD doesnot inform stations about when they need to wake up to receive their downlinkpackets. In this paper, we present a new mechanism---edge-assisted predictivesleep scheduling (EAPS)---to adjust the sleep duration of stations while theyexpect downlink packets. We first implement a Linux-based access point thatenables us to collect parameters affecting communication latency. Using thisaccess point, we build a testbed that, in addition to offering traffic patterncustomization, replicates the characteristics of real-world environments. Wethen use multiple machine learning algorithms to predict downlink packetdelivery. Our empirical evaluations confirm that when using EAPS the energyconsumption of IoT stations is as low as PSM, whereas the delay of packetdelivery is close to the case where the station is always awake.

EAPS: Edge-Assisted Predictive Sleep Scheduling for 802.11 IoT Stations