7 research outputs found
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Human Mobility Monitoring using WiFi: Analysis, Modeling, and Applications
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\u27s 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
Analyzing the Impact of Covid-19 Control Policies on Campus Occupancy and Mobility via Passive WiFi Sensing
Mobile sensing has played a key role in providing digital solutions to aid
with COVID-19 containment policies. These solutions include, among other
efforts, enforcing social distancing and monitoring crowd movements in indoor
spaces. However, such solutions may not be effective without mass adoption. As
more and more countries reopen from lockdowns, there remains a pressing need to
minimize crowd movements and interactions, particularly in enclosed spaces.
This paper conjectures that analyzing user occupancy and mobility via deployed
WiFi infrastructure can help institutions monitor and maintain safety
compliance according to the public health guidelines. Using smartphones as a
proxy for user location, our analysis demonstrates how coarse-grained WiFi data
can sufficiently reflect indoor occupancy spectrum when different COVID-19
policies were enacted. Our work analyzes staff and students' mobility data from
three different university campuses. Two of these campuses are in Singapore,
and the third is in the Northeastern United States. Our results show that
online learning, split-team, and other space management policies effectively
lower occupancy. However, they do not change the mobility for individuals
transitioning between spaces. We demonstrate how this data source can be put to
practical application for institutional crowd control and discuss the
implications of our findings for policy-making.Comment: 25 pages, 18 figure