IEEE 802.11 (Standard)

Mobility monitoring in urban environments can provide valuable insights into pedestrian and vehicle movement – where people want to go, how they get there, and the challenges they face along the way. Today, local governments can automate the acquisition of such data using video surveillance to understand the potential impact of investment and policy decisions. However, public disapproval of computer vision due to privacy concerns opens opportunities for research into alternative tools built with privacy constraints at the core of the design. WiFi sensing emerges as a promising solution. Modern mobile devices ubiquitously support the 802.11 standard and regularly emit WiFi probe requests for network discovery. We can passively monitor this traffic to estimate the levels of congestion in public spaces.
In this dissertation, we address three fundamental research problems pertaining to developing streetscape-scale mobility intelligence: scalable infrastructure for WiFi signal capture, passive device localization, and device re-identification.

IEEE 802.11 networks successfully satisfy high data demands and are cheaper compared to cellular networks. Modern mobile computers and phones are equipped with 802.11 and are VoIP capable. Current network designs do not dynamically accommodate changes in the usage. We propose a dynamic power control algorithm that provides greater capacity within a limited geographic region. Most other power algorithms necessitate changes in 802.11 requiring hardware changes. Proposed algorithm only requires firmware updates to enable dynamic control of APs transmit power. We use earlier studies to determine the limit of the number of users to optimize power. By lowering transmit power of APs with large number of users, we can effectively decrease the cell size. The resulting gap is then covered by dynamically activating additional APs. This also provides greater flexibility and reduces the network planning costs.