University of California

Nanyang Technological University

Yuxun Zhou

Costas J Spanos

Han Zou

Lihua Xie

Jianfei Yang

An Internet of Things (IoT) device can include a switch module configured to control a power supply for at least one powered device, a WiFi module configured to perform wireless communication and occupancy activity sensing, and a processor configured to determine human activity recognition WiFi measurements from the WiFi module based on the occupancy activity sensing and generate corresponding commands for the switch module based on the determined human activity recognition WiFi measurements.

Intelligent IoT device with wireless-enabled device-free occupancy sensing

A system can include multiple WiFi-enabled commercial off the shelf (COTS) Internet of Things (IoT) devices disposed within an environment and configured to be a transmitter (TX) or a received (RX) to send or receive data over a WiFi radio frequency communication link. A server can be configured to receive and parse the CSI data transmitted from the RX, store the CSI data with a corresponding human identity label collected for training, train a human identification classifier using a Convex Clustered Concurrent Shapelet Learning (CSL) method, and estimate an identification of a user based on the CSI data and the CSL method. The server can be configured to receive and parse the CSI data transmitted from the RX, transfer the CSI data into real-time CSI frames, store the real-time CSI frames in a database, store the real-time CSI frames with a corresponding gesture label collected in an original environment, and estimate and identify the gesture performed by user using a trained target encoder and source classifier.

Device-free human identification and device-free gesture recognition

A mobile device and user association system can include wireless routers to execute software for capturing data including received signal strength (RSS) values and media access controller (MAC) addresses for a number of mobile devices (MDs) from existing wireless fidelity (WiFi) traffic. The system can also include a server to receive the RSS values and MAC addresses of the MDs to estimate a location of each MD and generate historical location data of each MD, identify and filter out temporary MDs, classify each non-temporary MDs as either a static device (SD) or a mobile phone (MP), and associate a user with each SD and MP.

Unsupervised WiFi-enabled device-user association for personalized location-based service

An automatic wireless fine-grained ratio map construction and adaptation system may include a Gaussian process regression (GPR) model constructed with real wireless received signal strength (RSS) measurements collected in a free space to provide coarse RSS estimation in a constrained space, and a generative adversarial network (GAN) to provide fine-grained RSS estimation in the constrained space by using an output of GPR as an input for a generator of GAN, modeling the irregular RSS distributions in complex indoor environments. The system may generate realistic RSS data in the constrained space that has not been manually site-surveyed.

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Chicago, IL, United States of America

Yuxun Zhou