Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Multi-Dimensional-Personalization in mobile contexts

During the dot com era the word "personalisation” was a hot buzzword. With the fall of the dot com companies the topic has lost momentum. As the killer application for UMTS or the mobile internet has yet to be identified, the concept of Multi-Dimensional-Personalisation (MDP) could be a candidate. Using this approach, a recommendation of mobile advertisement or marketing (i.e., recommendations or notifications), online content, as well as offline events, can be offered to the user based on their known interests and current location. Instead of having to request or pull this information, the new service concept would proactively provide the information and services – with the consequence that the right information or service could therefore be offered at the right place, at the right time. The growing availability of "Location-based Services“ for mobile phones is a new target for the use of personalisation. "Location-based Services“ are information, for example, about restaurants, hotels or shopping malls with offers which are in close range / short distance to the user. The lack of acceptance for such services in the past is based on the fact that early implementations required the user to pull the information from the service provider. A more promising approach is to actively push information to the user. This information must be from interest to the user and has to reach the user at the right time and at the right place. This raises new requirements on personalisation which will go far beyond present requirements. It will reach out from personalisation based only on the interest of the user. Besides the interest, the enhanced personalisation has to cover the location and movement patterns, the usage and the past, present and future schedule of the user. This new personalisation paradigm has to protect the user’s privacy so that an approach supporting anonymous recommendations through an extended "Chinese Wall“ will be described

Sensing Depression

The hallmark indicator of depressive disorders is a presence of sad, empty, or irritable mood, accompanied by somatic and cognitive changes that significantly affect the individuals capacity to function. The overall goal of our project is to provide a tool for doctors to effortlessly detect depression, and in effect achieve greater coverage in detecting depression over the general population. We use machine learning techniques to create a mobile application that infers a smartphone users severity of depression from data scraped off their phone and social media websites. Through our study, we have demonstrated the feasibility of this approach to diagnosing depression, achieving an average testset RMSE of 5.67 across all modalities in the task of PHQ-9 score predictions

Measuring and designing social mechanisms using mobile phones

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 157-168).A key challenge of data-driven social science is the gathering of high quality multi-dimensional datasets. A second challenge relates to the design and execution of social experiments in the real world that are as reliable as those within a controlled laboratory, yet yield more practical results. We introduce the Social Functional Mechanism-design and Relationship Imaging, or "SocialfMRI" - an approach that enhances existing computational social science methodologies by bridging rich data collection strategies with experimental interventions. In this thesis, we demonstrate the value of the Social fMRI approach in our Friends and Family study. We transformed a young-family residential community into a living laboratory for 15 months, through a very fine-grained and longitudinal data collection process combined with targeted experimental interventions. Through the derived dataset of unprecedented quality, the Social fMRI approach allows us to gain insights into intricate social mechanisms and interpersonal relationships within the community in ways not previously possible. This thesis delivers the following contributions: (1) A methodology combining a rich-data experimental approach together with carefully designed interventions, (2) a system supporting the methodology - implemented, field-tested, and released to the world as an open-source framework with a growing community of users, (3) a dataset collected using the system, comprising what is, to date, the richest real-world dataset of its genre, (4) a very large set of experimental findings that contribute to our understanding of important research questions in computational social science in addition to demonstrating the methodology's potential. Among the results described in this thesis are the design and evaluation of a novel mechanism for social support in a health-related context, the observation that the diffusion of mobile applications relies more on the face-to-face interaction ties than on self-perceived friendship ties, and a gained understanding of the evolution of modeling and prediction processes over time and varying sample sizes.by Nadav Aharony.Ph.D

Tagging amongst friends: an exploration of social media exchange on mobile devices

Mobile social software tools have great potential in transforming the way users communicate on the move, by augmenting their everyday environment with pertinent information from their online social networks. A fundamental aspect to the success of these tools is in developing an understanding of their emergent real-world use and also the aspirations of users; this thesis focuses on investigating one facet of this: the exchange of social media. To facilitate this investigation, three mobile social tools have been developed for use on locationaware smartphone handsets. The first is an exploratory social game, 'Gophers' that utilises task oriented gameplay, social agents and GSM cell positioning to create an engaging ecosystem in which users create and exchange geotagged social media. Supplementing this is a pair of social awareness and tagging services that integrate with a user's existing online social network; the 'ItchyFeet' service uses GPS positioning to allow the user and their social network peers to collaboratively build a landscape of socially important geotagged locations, which are used as indicators of a user's context on their Facebook profile; likewise 'MobiClouds' revisits this concept by exploring the novel concept of Bluetooth 'people tagging' to facilitate the creation of tags that are more indicative of users' social surroundings. The thesis reports on findings from formal trials of these technologies, using groups of volunteer social network users based around the city of Lincoln, UK, where the incorporation of daily diaries, interviews and automated logging precisely monitored application use. Through analysis of trial data, a guide for designers of future mobile social tools has been devised and the factors that typically influence users when creating tags are identified. The thesis makes a number of further contributions to the area. Firstly, it identifies the natural desire of users to update their status whilst mobile; a practice recently popularised by commercial 'check in' services. It also explores the overarching narratives that developed over time, which formed an integral part of the tagging process and augmented social media with a higher level meaning. Finally, it reveals how social media is affected by the tag positioning method selected and also by personal circumstances, such as the proximity of social peers

Exploiting behavioral biometrics for user security enhancements

As online business has been very popular in the past decade, the tasks of providing user authentication and verification have become more important than before to protect user sensitive information from malicious hands. The most common approach to user authentication and verification is the use of password. However, the dilemma users facing in traditional passwords becomes more and more evident: users tend to choose easy-to-remember passwords, which are often weak passwords that are easy to crack. Meanwhile, behavioral biometrics have promising potentials in meeting both security and usability demands, since they authenticate users by who you are , instead of what you have . In this dissertation, we first develop two such user verification applications based on behavioral biometrics: the first one is via mouse movements, and the second via tapping behaviors on smartphones; then we focus on modeling user web browsing behaviors by Fitts\u27 Law.;Specifically, we develop a user verification system by exploiting the uniqueness of people\u27s mouse movements. The key feature of our system lies in using much more fine-grained (point-by-point) angle-based metrics of mouse movements for user verification. These new metrics are relatively unique from person to person and independent of the computing platform. We conduct a series of experiments to show that the proposed system can verify a user in an accurate and timely manner, and induced system overhead is minor. Similar to mouse movements, the tapping behaviors of smartphone users on touchscreen also vary from person to person. We propose a non-intrusive user verification mechanism to substantiate whether an authenticating user is the true owner of the smartphone or an impostor who happens to know the passcode. The effectiveness of the proposed approach is validated through real experiments. to further understand user pointing behaviors, we attempt to stress-test Fitts\u27 law in the wild , namely, under natural web browsing environments, instead of restricted laboratory settings in previous studies. Our analysis shows that, while the averaged pointing times follow Fitts\u27 law very well, there is considerable deviations from Fitts\u27 law. We observe that, in natural browsing, a fast movement has a different error model from the other two movements. Therefore, a complete profiling on user pointing performance should be done in more details, for example, constructing different error models for slow and fast movements. as future works, we plan to exploit multiple-finger tappings for smartphone user verification, and evaluate user privacy issues in Amazon wish list

Mobile Big Data Analytics in Healthcare

Mobile and ubiquitous devices are everywhere around us generating considerable amount of data. The concept of mobile computing and analytics is expanding due to the fact that we are using mobile devices day in and out without even realizing it. These mobile devices use Wi-Fi, Bluetooth or mobile data to be intermittently connected to the world, generating, sending and receiving data on the move. Latest mobile applications incorporating graphics, video and audio are main causes of loading the mobile devices by consuming battery, memory and processing power. Mobile Big data analytics includes for instance, big health data, big location data, big social media data, and big heterogeneous data. Healthcare is undoubtedly one of the most data-intensive industries nowadays and the challenge is not only in acquiring, storing, processing and accessing data, but also in engendering useful insights out of it. These insights generated from health data may reduce health monitoring cost, enrich disease diagnosis, therapy, and care and even lead to human lives saving. The challenge in mobile data and Big data analytics is how to meet the growing performance demands of these activities while minimizing mobile resource consumption. This thesis proposes a scalable architecture for mobile big data analytics implementing three new algorithms (i.e. Mobile resources optimization, Mobile analytics customization and Mobile offloading), for the effective usage of resources in performing mobile data analytics. Mobile resources optimization algorithm monitors the resources and switches off unused network connections and application services whenever resources are limited. However, analytics customization algorithm attempts to save energy by customizing the analytics process while implementing some data-aware techniques. Finally, mobile offloading algorithm decides on the fly whether to process data locally or delegate it to a Cloud back-end server. The ultimate goal of this research is to provide healthcare decision makers with the advancements in mobile Big data analytics and support them in handling large and heterogeneous health datasets effectively on the move

PROFILING - CONCEPTS AND APPLICATIONS

Profiling is an approach to put a label or a set of labels on a subject, considering the characteristics of this subject. The New Oxford American Dictionary defines profiling as: “recording and analysis of a person’s psychological and behavioral characteristics, so as to assess or predict his/her capabilities in a certain sphere or to assist in identifying a particular subgroup of people”. This research extends this definition towards things demonstrating that many methods used for profiling of people may be applied for a different type of subjects, namely things. The goal of this research concerns proposing methods for discovery of profiles of users and things with application of Data Science methods. The profiles are utilized in vertical and 2 horizontal scenarios and concern such domains as smart grid and telecommunication (vertical scenarios), and support provided both for the needs of authorization and personalization (horizontal usage).:The thesis consists of eight chapters including an introduction and a summary. First chapter describes motivation for work that was carried out for the last 8 years together with discussion on its importance both for research and business practice. The motivation for this work is much broader and emerges also from business importance of profiling and personalization. The introduction summarizes major research directions, provides research questions, goals and supplementary objectives addressed in the thesis. Research methodology is also described, showing impact of methodological aspects on the work undertaken. Chapter 2 provides introduction to the notion of profiling. The definition of profiling is introduced. Here, also a relation of a user profile to an identity is discussed. The papers included in this chapter show not only how broadly a profile may be understood, but also how a profile may be constructed considering different data sources. Profiling methods are introduced in Chapter 3. This chapter refers to the notion of a profile developed using the BFI-44 personality test and outcomes of a survey related to color preferences of people with a specific personality. Moreover, insights into profiling of relations between people are provided, with a focus on quality of a relation emerging from contacts between two entities. Chapters from 4 to 7 present different scenarios that benefit from application of profiling methods. Chapter 4 starts with introducing the notion of a public utility company that in the thesis is discussed using examples from smart grid and telecommunication. Then, in chapter 4 follows a description of research results regarding profiling for the smart grid, focusing on a profile of a prosumer and forecasting demand and production of the electric energy in the smart grid what can be influenced e.g. by weather or profiles of appliances. Chapter 5 presents application of profiling techniques in the field of telecommunication. Besides presenting profiling methods based on telecommunication data, in particular on Call Detail Records, also scenarios and issues related to privacy and trust are addressed. Chapter 6 and Chapter 7 target at horizontal applications of profiling that may be of benefit for multiple domains. Chapter 6 concerns profiling for authentication using un-typical data sources such as Call Detail Records or data from a mobile phone describing the user behavior. Besides proposing methods, also limitations are discussed. In addition, as a side research effect a methodology for evaluation of authentication methods is proposed. Chapter 7 concerns personalization and consists of two diverse parts. Firstly, behavioral profiles to change interface and behavior of the system are proposed and applied. The performance of solutions personalizing content either locally or on the server is studied. Then, profiles of customers of shopping centers are created based on paths identified using Call Detail Records. The analysis demonstrates that the data that is collected for one purpose, may significantly influence other business scenarios. Chapter 8 summarizes the research results achieved by the author of this document. It presents contribution over state of the art as well as some insights into the future work planned

Designing the user experience of a spatiotemporal automated home heating system: a holistic design and implementation process

This research explores technological interventions to reduce energy use in the domestic sector, a notable contributor to the global energy footprint. In the UK elevated challenges associated with renovating an outdated, poorly performing housing stock render a search for alternatives to provide immediate energy saving at low cost. To solve this problem, this thesis takes a holistic design approach to designing and implementing a spatiotemporal heating solution, and aims to investigate experiences of comfort, thermal comfort concepts for automated home heating, users’ interactions and experiences of living with such a system in context, and the underlying utility of quasi-autonomous spatiotemporal home heating. The mixed-methods research process was employed to explore and answer four questions: 1) what is the context within which these home heating interfaces are used, 2) to what extent can spatiotemporal automated heating minimise energy use while providing thermal comfort, 3) how are different heating strategies experienced by users, and 4) How do visibility of feedback, and intelligibility affect the user experience related to understanding and control? Ideation techniques were used to explore the context within which the designs are used with regard to all factors and actors in play and resulted in a conceptual model of the context to be used as a UX design brief. This developed model used mismatches between users’ expectations and reality to indicate potential thermal comfort behaviour actions and mapped the factors within the home context that affected these mismatches. Potential user inclusion through participatory design provided stakeholder insight and interface designs concepts to be developed into prototypes. The results of a prototype probe study using these prototypes showed that intelligibility should not be an interface design goal in itself, but rather fit in with broader UX design agenda regarding data levels, context specificity, and timescales. Increased autonomy in the system was shown not to directly diminish the experience of control, but rather, control or the lack of originated from an alignment of expectations and reality. A quasi-autonomous spatiotemporal heating system design (including a novel heating control algorithm) was coupled with the design of a smartphone interface and the resultant system was deployed in a low-technology solution demonstrating the potential for academic studies to explore such automated systems in-situ in the intended environment over a long period of time. Assessment of the novel control algorithm in an emulated environment demonstrated its fitness for purpose in reducing the amount of energy required to provide adequate levels of thermal comfort (by a factor of seven compared with EnergyStar recommended settings for programmable thermostats), and that these savings can be increased by including occupants’ thermal preference as a variable in the control algorithm. Field deployment of that algorithm in a low-tech sensor-based heating system assessed the user experience of the automated heating system and its mobile application-based control interface, as well as demonstrated the user thermal comfort experience of two different heating strategies. The results highlighted the potential to utilise the lower energy-use “minimise discomfort” strategy without compromising user thermal comfort in comparison to a “maximise comfort” strategy. Diverse heating system use behaviours were also identified and conceptualised alongside users’ experiences in line with the developed conceptual model. A rich picture analysis of all previous findings was utilised to provide a model of the design space for home automated heating systems, and was used to draw interface design guidelines for a broader range of home automation control interfaces. The work presented here served as important first steps in demonstrating the importance of assessing UX of automated home heating systems in situ over elongated periods of time. Novel contributions of (i) conceptual model of automated systems’ domestic context and thermal comfort behaviours within, (ii) nudging this behaviour by selecting a “minimise discomfort” heating strategy over “maximise comfort”, (iii) using UX to influence user expectations and subsequently energy behaviour, and (iv) inclusion of thermal preference in domestic heating control algorithm were all resultant of examining naturally occurring behaviours in their natural setting. As such, they are important exploratory discoveries and require replication, but provide new research directions that would allow reduction of domestic energy use without compromise

Privacy in Smart Homes Using Privacy Impact Assessment to Inspect Privacy Issues in a Smart Home

IoT has an ever-increasing amount of development as more and more different devices connect to the Internet and become IoT devices. For the regular private user, the smart home may be the most enticing domain of IoT as it can be used to ease their lives. Smart home and smart home devices are one of the subfields of the Internet of Things. They allow the inhabitants to control various home devices remotely from anywhere within the house or anywhere in the world at any particular time. Smart homes have several benefits. They are improving the quality of individuals' lives, as individuals can control their various smart devices at any time. In addition, a smart home allows individuals to have greater control of their energy use. Other pros of smart homes include complete control over devices, increased convenience, and insurance benefits. However, regardless of the many benefits of smart homes, they are also associated with various challenges. Security and privacy are significant challenges related to the smart home environment. This thesis will discuss the privacy impact of smart homes and smart devices. Four different devices have been included, and each device will be analyzed to conclude what private sensitive information they collect. Moreover, a privacy impact assessment (PIA) tool will be used to conclude whether our manual analysis of the devices was correct or not. Lastly, we will propose some solutions that we consider will increase the protection of users' privacy