Data Monitoring and Analysis in Wireless Networks

Various wireless network technologies have been created to meet the ever-increasing demand for wireless access to the Internet, such as wireless local area network, cellular network, sensor network and many more. The communication devices have transformed from large computational servers to small wireless hand-held devices, ranging from laptops, tablets, smartphones to small sensors. The advances of these wireless networks (e.g., faster network speed) and their intensive usages result in an enormous growth of network data in terms of volume, diversity, and complexity. All of these changes have raised complicated issues of network measurement and management. In the first part of this thesis, I study how WiFi network characteristics impact network forensics investigation and home security monitoring. I first focus on network forensics investigation and propose a wireless forensic monitoring system to collect trace digests of WiFi activities and facilitate cybercrime investigation. Then, I design and develop a low-cost home security system based on WiFi networks for physical intruder detection. Two methods - MAC-based detection and RSSI-variance-based detection, are proposed based on the characteristics of WiFi networks. In the second part, I study how to effectively and efficiently model multiple coevolving time series, which is ubiquitous in network measurement especially in wireless sensor networks. Two comprehensive algorithms are proposed to address three prominent challenges of mining coevolving sensor measured traces: (a) high order; (b) contextual constraints; and (c) temporal smoothness

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Modeling, Predicting and Capturing Human Mobility

Realistic models of human mobility are critical for modern day applications, specifically for recommendation systems, resource planning and process optimization domains. Given the rapid proliferation of mobile devices equipped with Internet connectivity and GPS functionality today, aggregating large sums of individual geolocation data is feasible. The thesis focuses on methodologies to facilitate data-driven mobility modeling by drawing parallels between the inherent nature of mobility trajectories, statistical physics and information theory. On the applied side, the thesis contributions lie in leveraging the formulated mobility models to construct prediction workflows by adopting a privacy-by-design perspective. This enables end users to derive utility from location-based services while preserving their location privacy. Finally, the thesis presents several approaches to generate large-scale synthetic mobility datasets by applying machine learning approaches to facilitate experimental reproducibility

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Application of data analytics and machine learning on data collected by smartphones to understand human behavioural patterns

A growing number of health studies seek to leverage smartphone-based recording to continuously monitor consenting participants’ health behaviours, including those related to mental health, mobility, and activity. So as to better understand health risks and the influence of the environment on human physical and mental health conditions, such studies commonly use smartphones to collect health behaviour relevant metrics such as screen state, app usage, location, activity level, browsing behaviour, etc. They also typically use survey instruments incorporating questionnaires, voice recordings, photos, multi-media content on which the user is asked to provide feedback, etc. When the data volume and variety grow substantially --- such as is common with sensed data --- then challenges associated with data quantity, quality, diversity, trustworthiness, etc. also increase significantly. Because most health scientists are unfamiliar with tools and concepts required for effective analysis of such high-volume and high-velocity data, it is challenging for health scientists alone to perform the computationally intensive analyses needed to secure certain types of insight from the collected data. The primary objective of this thesis is to provide computational mechanisms to support research teams associated with 3 distinct case studies utilizing smartphone-based data, so as to help obtain insights accessible to team health scientists. The data sets for these three studies were collected from participants using a pre-existing smartphone based application named Ethica. Such data was accumulated over a period ranging from 2 weeks to 6 months – with the study period differing across the three studies – through a set of surveys and mobile sensors such as those for the battery, screen state, GPS, etc. This thesis addresses three significant challenges associated with the extraction and processing of smartphone data. The first is the computational burden and intricacies associated with data extraction, preprocessing and analytic steps. The second consists of a need for handling omitted and missing data points with the help of machine learning and statistical methods. The final challenge covered here is to secure valuable findings from these data sets through exploratory analysis following examination of participant adherence patterns and evaluation of the quantity and quality of the data collected. The methods applied in this thesis are useful for other studies using the Ethica platform because of the shared structure of Ethica datasets and the capacity of the code to be reused and readily adapted for other such datasets