Link-Layer Identification of Device Signatures: Wi-Fi Sensing for Crowd Analytics

Abstract

The Johns Hopkins University Applied Physics Laboratory (APL) Link-Layer Identification of Device Signatures (LLIDS) research effort uses machine learning techniques to identify unique wireless device signatures from patterns in link-layer data. Identifying signatures can increase situational awareness, assist in estimating crowd sizes, provide pattern of life, and protect facilities and infrastructure through activity surveillance. Link-layer Wi-Fi data are unique because they can be collected without access to a network and with devices that have low size, weight, and power (SWaP) requirements. The LLIDS multilayer system design breaks down link-layer data into unique device signatures using a combination of pattern recognition and state-of-the-art algorithms.