Human Mobility Monitoring using WiFi: Analysis, Modeling, and Applications

Author

Amee Trivedi, University of Massachusetts AmherstFollow

Author ORCID Identifier

https://orcid.org/0000-0003-3427-3333

AccessType

Open Access Dissertation

Document Type

thesis

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Computer Science

Year Degree Awarded

2021

Month Degree Awarded

September

First Advisor

Prashant Shenoy

Second Advisor

Deepak Ganesan

Third Advisor

Jeremy Gummeson

Fourth Advisor

Tauhidur Rahman

Subject Categories

Data Science | Graphics and Human Computer Interfaces | Other Computer Sciences

Abstract

Understanding and modeling humans and device mobility has fundamental importance in mobile computing, with implications ranging from network design and location-aware technologies to urban infrastructure planning. Today's users carry a plethora of devices such as smartphones, laptops, tablets, and smartwatches, with each device offering a different set of services resulting in different usage and mobility leading to the research question of understanding and modeling multiple user device trajectories. Additionally, prior research on mobility focuses on outdoor mobility when it is known that users spend 80% of their time indoors resulting in wide gaps in knowledge in the area of indoor mobility of users and devices. Here, I try to fill the gaps in mobility modeling in the areas of understanding and modeling indoor-outdoor human mobility as well as multi-device mobility. In this thesis, I propose the characterization and modeling of human and device mobility. Further, I design and deploy mobility-aware applications for contact tracing of infectious diseases and energy-aware Heating, Ventilation, and Air Conditioning (HVAC) scheduling. I try and answer a sequence of four primary inter-related questions : (1) how is indoor and outdoor user mobility different, (2) are multiple device trajectories belonging to a single user correlated, (3) how to model indoor mobility of users and (4) how to design effective mobility aware applications that are easily deployable and align with long term goals of sustainability as well relay positive societal impact. The insights gained from each question serves as a base to build up on the next question in the series. I present answers to these questions across three main parts of my thesis. The first part comprises of characterization and analysis of human and device mobility. In this part I design and develop tool to extract device trajectories from WiFi system logs "syslog" and map devices to users. These extracted trajectories and device to user mapping are used to characterize and empirically analyze the mobility of users at varying spatial granularity (indoor, outdoor) and extract device mobility correlations between multiple devices of users and forms the first part of my thesis. In the second part, based on the insights gained from the multi-granular and multi-device mobility characterization stated above, I argue that mobility is inherently hierarchical in nature and propose novel indoor human mobility modeling approach. Third, I leverage the passively observed mobility to design mobility-aware applications that either look back or look ahead in time. WiFiTrace is a look back or backtracking application that is a network-centric contact tracing tool to aid healthcare workers in manual contact tracing of infectious diseases and iSchedule is a look ahead machine learning based mobility-aware energy-saving application that predicts Heating, Ventilation, and Air Conditioning (HVAC) schedule for higher energy savings while increasing user comfort.

DOI

https://doi.org/10.7275/24236655

Recommended Citation

Trivedi, Amee, "Human Mobility Monitoring using WiFi: Analysis, Modeling, and Applications" (2021). Doctoral Dissertations. 2375.
https://doi.org/10.7275/24236655 https://scholarworks.umass.edu/dissertations_2/2375

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.