Machine-learning assisted optimized power-saving techniques for battery-operated Wi-Fi Internet of Things (IoT) devices are provided. These techniques use various IoT device information including Manufacturer Usage Description (MUD) data along with a clustering algorithm to provide an automatic and dynamic computation of Target Wake Time (TWT) power save schedule for the devices. These techniques may be used by next generation 802.11ax access points to more efficiently schedule the target wake time for the 802.11ax supported IoT devices, to thereby prolong the lifetime of battery operated IoT devices connected to the 802.11ac access points.
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Lade, Vaibhav; Mohan, Asha; and Patil, Santosh, "802.11AX FOR INTERNET OF THINGS - MACHINE LEARNING ASSISTED OPTIMIZED POWER SAVE TECHNIQUES FOR IOT DEVICES USING 802.11AX TARGET WAKE TIME", Technical Disclosure Commons, (October 02, 2018)