Tracking Human Mobility using WiFi signals

We study six months of human mobility data, including WiFi and GPS traces recorded with high temporal resolution, and find that time series of WiFi scans contain a strong latent location signal. In fact, due to inherent stability and low entropy of human mobility, it is possible to assign location to WiFi access points based on a very small number of GPS samples and then use these access points as location beacons. Using just one GPS observation per day per person allows us to estimate the location of, and subsequently use, WiFi access points to account for 80\% of mobility across a population. These results reveal a great opportunity for using ubiquitous WiFi routers for high-resolution outdoor positioning, but also significant privacy implications of such side-channel location tracking

The Creation of a Building Map Application for a University Setting

The use of navigational technology in mobile and web devices has sharply increased in recent years. With the capability to create interactive maps now available, navigating in real time between locations has become possible. This is especially essential in areas and organizations experiencing rapid expansion like Liberty University (LU). Therefore, the author proposes a project to create an interactive map application (IMA) for LU’s academic buildings that is scalable and usable through both the university’s website and with a mobile application. There are several considerations that must be taken into account when creating the LU map application, such as development methods, platforms, programming languages, software, userbase, and cost. The software development lifecycle is used in order to properly analyze, plan, design, and implement the LU map application. Activity, use-case, and entity-relationship diagrams are used as a basis for the implementation of the application which is created with a user-centric design and security as a priority. Despite some limitations to the LU map application, it provides a preliminary model of how an interactive building map could be used to provide students and faculty new ways to navigate a university setting

Trajectory Privacy Preservation and Lightweight Blockchain Techniques for Mobility-Centric IoT

Various research efforts have been undertaken to solve the problem of trajectory privacy preservation in the Internet of Things (IoT) of resource-constrained mobile devices. Most attempts at resolving the problem have focused on the centralized model of IoT, which either impose high delay or fail against a privacy-invading attack with long-term trajectory observation. These proposed solutions also fail to guarantee location privacy for trajectories with both geo-tagged and non-geo-tagged data, since they are designed for geo-tagged trajectories only. While a few blockchain-based techniques have been suggested for preserving trajectory privacy in decentralized model of IoT, they require large storage capacity on resource-constrained devices and can only provide conditional privacy when a set of authorities governs the blockchain. This dissertation addresses these challenges to develop efficient trajectory privacy-preservation and lightweight blockchain techniques for mobility-centric IoT. We develop a pruning-based technique by quantifying the relationship between trajectory privacy and delay for real-time geo-tagged queries. This technique yields higher trajectory privacy with a reduced delay than contemporary techniques while preventing a long-term observation attack. We extend our study with the consideration of the presence of non-geo-tagged data in a trajectory. We design an attack model to show the spatiotemporal correlation between the geo-tagged and non-geo-tagged data which undermines the privacy guarantee of existing techniques. In response, we propose a methodology that considers the spatial distribution of the data in trajectory privacy-preservation and improves existing solutions, in privacy and usability. With respect to blockchain, we design and implement one of the first blockchain storage management techniques utilizing the mobility of the devices. This technique reduces the required storage space of a blockchain and makes it lightweight for resource-constrained mobile devices. To address the trajectory privacy challenges in an authority-based blockchain under the short-range communication constraints of the devices, we introduce a silence-based one of the first technique to establish a balance between trajectory privacy and blockchain utility. The designed trajectory privacy- preservation techniques we established are light- weight and do not require an intermediary to guarantee trajectory privacy, thereby providing practical and efficient solution for different mobility-centric IoT, such as mobile crowdsensing and Internet of Vehicles

Seasonal spatial activity patterns of visitors with a mobile exercise application at Seoraksan National Park, South Korea

Visitors’ behavior in national parks can be influenced by seasonal variations in climate and preferred activities. Seasonality can produce different space consumption patterns, and impact visitor experience and natural resource use. The purpose of this study was to explore the seasonal spatial patterns of visitors’ activities using a mobile exercise application within the context of Seoraksan National Park in South Korea. A dataset composed of 5142 starting and ending points of 2639 activities (hiking and walking) created by 1206 mobile exercise application users (January–December 2015) were collected from a leading mobile exercise application operator. GIS-based spatial analytical techniques were used to analyze the spatial patterns of activity points across seasons and days (weekdays/weekends). Results indicated considerable seasonal and daily variations in activity distribution and hot spots (i.e., locations of potential congestion or crowding). The findings enable park managers to mitigate negative impacts to natural resources as well as enhance visitors’ experiences. Also, it allows potential visitors to decide when to visit certain sites via mobile application to ensure optimal conditions. Furthermore, the GPS-based exercise mobile application can be used as a new methodological approach to understand spatio-temporal patterns of visitors’ behavior within national parks and other natural protected areas

Using location-based services to improve mental health interventions

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesThe rapid developments in the functionalities of smartphones and technological innovations play a vital role in providing location-based services in healthcare. A mental health sensor-based software platform has been developed by the Geospatial Technologies research group (Geotec), consisting of an application generation framework that offers basic geospatial building blocks (location tracking, trajectory recording, geo-fencing), communication building blocks (notifications) and a basic visualization of collected data for therapists. The framework has been successfully tested for building an application to treat agoraphobia, addiction, and depression, using location-based notifications. However, defining the places of interest for a patient is addressed to a limited extent only. Thus, therapists have difficulties of identifying and defining multiple places of interest, and the generated apps were therefore mostly limited to single places of interest, which were manually defined. Hence, they are difficult to use in larger areas. This thesis aims to use a location-based service to support therapists in defining places of interest, based on location and place categories. The work is carried out as an extension of the SYMPTOMS platform, and it allows therapists to define multiple places of interest automatically and for larger areas. The added value of the approach (in terms of automation, ease of use, and universally usable of therapies) by the location-based services in improving mental health interventions is evaluated. As a result, the application was found to be usable with SUS score of 91.875 and useful for therapists to define multiple places of interest at the same time which simplifies the configuration process and makes therapies universally usable. Reproducibility self-assessment (https://osf.io/j97zp/): 2, 2, 1, 2, 2 (input data, pre-processing, methods, computational environment, results)