Accessible and Inclusive Content and Applications

As devices have become smaller and more pervasive, usage scenarios that have historically been common for people with disabilities are finding more general application for all users. Overall, the consideration of accessibility improves the usability of applications for all users. This chapter will discuss standards for accessibility, inclusive design, and topics related to the development of accessible mobile content and applications. The discussion will apply to mobile content, such as EPUB documents, and topics related to Web, native, and hybrid applications.

Preserving User Privacy and Security in Context-Aware Mobile Platforms

Contemporary smartphones are capable of generating and transmitting large amounts of data about their users. Recent advances in collaborative context modeling combined with a lack of adequate permission model for handling dynamic context sharing on mobile platforms have led to the emergence of a new class of mobile applications that can access and share embedded sensor and context data. Most of the time such data is used for providing tailored services to the user but it can lead to serious breaches of privacy. We use Semantic Web technologies to create a rich notion of context. We also discuss challenges for context aware mobile platforms and present approaches to manage data flow on these devices using semantically rich fine-grained context-based policies that allow users to define their privacy and security need using tools we provide.

Mobile Application and User Analytics

Mobile analytics is the systematic study of mobile device and application usage, and application performance, for the purpose of improving service quality. This chapter motivates the need for mobile analytics as an essential cog in the emerging economy built around devices, applications, and communication. A taxonomy of mobile analytics problems is presented, and technical details of a typical mobile analytics solution are discussed. Scale, heterogeneity, dynamically changing environments, and diverse privacy requirements pose challenges to collecting and processing data for such analysis. This chapter examines how analytics solutions handle these challenges. The core of the chapter consists of a technical section describing the general architecture of a mobile analytics solution, procedures to collect and process data, event monitoring infrastructure, system administration processes, and privacy management policies. Case studies of a number of analytics solutions available as commercial products or prototypes are presented.

Combining Static Code Analysis and Machine Learning for Automatic Detection of Security Vulnerabilities in Mobile Apps

Mobile devices have revolutionized many aspects of our lives. Without realizing it, we often run on them programs that access and transmit private information over the network. Integrity concerns arise when mobile applications use untrusted data as input to security-sensitive computations. Program-analysis tools for integrity and confidentiality enforcement have become a necessity. Static-analysis tools are particularly attractive because they do not require installing and executing the program, and have the potential of never missing any vulnerability. Nevertheless, such tools often have high false-positive rates. In order to reduce the number of false positives, static analysis has to be very precise, but this is in conflict with the analysis' performance and scalability, requiring a more refined model of the application. This chapter proposes Phoenix, a novel solution that combines static analysis with machine learning to identify programs exhibiting suspicious operations. This approach has been widely applied to mobile applications obtaining impressive results.

Mobile Application Testing

Statistics hold that 80% of the mobile applications are deleted after just one-time use. A significant reason for this can be attributed to the quality of the mobile application, thus impressing on the need for testing a mobile application before it is made available on the app stores. At the same time, the mobile application lifecycle time is shrinking. So while operating systems used to get release about once in a couple of years, mobile operating systems get updated within months. And talking of apps, new apps are expected to be built and released in a matter of weeks. This impresses the need for automated mechanisms to do mobile testing. The space of mobile application testing is challenging owing to the variety of phone devices, the operating systems and the conditions under which an app can be used by the user in the wild. This chapter is focused on tools and techniques that are used for automated testing of mobile applications.

Mobile + Cloud

A mobile phones provides portability and personalized computing with ubiquitous connectivity. This combination makes them an ideal choice to use for various applications of personal use. The portability of mobile devices is the most important and useful feature of mobile devices. However, portability is achieved at the high cost of limited power and computation ability of the mobile device. Cloud computing fulfills the need of providing more computation power to complete the tasks that cannot be done on a mobile platform. The cloud provides an always available platform and do not have typical limitations, e.g. limited battery and computation power, of mobile platforms. Therefore combining cloud computing with mobile provides us best of both worlds i.e. we have a computing platform available for us all the time which we move, and yet we can access services and perform tasks that require high-power computation.

Trust Profiling to Enable Adaptive Trust Negotiation in Mobile Devices

In order to secure mobile devices, there has been movement to trust negotiation where two entities are able to establish a measure of mutual trust, even if no prior contact between either entity has existed in the past. This chapter explores adaptive trust negotiation in a mobile environment as a means to dynamically adjust security parameters based on the level of trust established during the negotiation process thereby enhancing mobile security. To accomplish this, the chapter proposes a trust profile that contains a proof of history of successful access to sensitive data to facilitate identification and authentication for adaptive trust negotiation. The trust profile consists of a set of X.509 identity and attribute certificates, where a certificate is added whenever a user via a mobile application makes a successful attempt to request data from a server where no relationship between the user and server has previously existed as a result of trust negotiation. Our approach allows the user to collect an ever-growing amount of profile data for future adaptive trust negotiation.

MEmoIt

In the recent years, smartphones became part of everyday life for most people. Their computational power and their sensing capabilities unlocked a new universe of possibilities for mobile developers. However, mobile development is still a young field and various pitfalls need to be avoided. In this chapter, the authors present several aspects of mobile development that need to be considered carefully. More specifically, this chapter covers topics like energy efficient sensing, smart computing, trade-off between accuracy and simplicity, data storage and cloud integration. These aspects are illustrated based on the authors' experience building a lifelogging application for the past two years.

Participatory Sensing for City-Scale Applications

The rapid advancements in sensing, computation and communications have led to the proliferation of smart phones. People-centric sensing is a scientific paradigm which empowers citizens with sensor-embedded smartphones, to contribute to micro and macro-scale urban sensing applications – either implicitly (in an opportunistic manner) or explicitly (in a participatory manner). Community-based urban sensing applications, are typically participatory in nature. For instance, commuters reporting on a transit overload may explicitly need to provide an input through an app to report on the overload. This chapter will focus on the trends, challenges and applications of participatory sensing systems. Additionally, they will understand the solution requirements for effective deployments of such systems in real scenarios.

Mobile Location Tracking

One of the key things that differentiate mobile devices from static computing platforms is the ability to provide information about the device user's location. While the raw location is often useful, it is the ability to understand the user's context that makes this capability so powerful. This chapter will review the technologies used today to provide location tracking of mobile devices and which are best for different types of use cases. It will also address challenges associated with location tracking, such as accuracy, performance and privacy.