Implications of data-intensive applications for next generation mobile networks

Lately, the mobile data market has moved into a growth stage triggered by two facts: affordability of mobile broadband, and availability of data-friendly devices. At this stage, market growth is no longer dependent on push strategies from suppliers; on the contrary, demand is now driving the market. However, it will not be easy for mobile operating companies to cope up with the demand to come in the near future. The infrastructure that is needed to support corresponding demand is far from completion. Operators are forced to make heavy investments to upgrade and expand their networks. To decide how to handle the present and upcoming demand, they need to identify and understand the characteristics of the scenarios they face. This is precisely the aim of this article, which provides figures on the consequences for mobile infrastructures of a generalised mobile media uptake. Data from the Spanish mobile deployment case have been used to arrive at practical figures and illustration of results, but the conclusions are easily extended to other countries and regions

Mining mobile apps reviews to support release planning

The mobile apps market is a tremendous success, with millions of apps downloaded and used every day by users spread all around the world. For apps’ developers, having their apps published on one of the major app stores (e.g. Google Play market) is just the beginning of the apps lifecycle. Indeed, in order to successfully compete with the other apps in the market, an app has to be updated frequently by adding new attractive features and by fixing existing bugs. Clearly, any developer interested in increasing the success of her app should try to implement features desired by the app’s users and to fix bugs affecting the user experience of many of them. A precious source of information to decide how to collect users’ opinions and wishes is represented by the reviews left by users on the store from which they downloaded the app. However, to exploit such information the app’s developer should manually read each user review and verify if it contains useful information (e.g. suggestions for new features). This is something not doable if the app receives hundreds of reviews per day, as happens for the very popular apps on the market. In this work, our aim is to provide support to mobile apps developers by proposing a novel approach exploiting data mining, natural language processing, machine learning, and clustering techniques in order to classify the user reviews on the basis of the information they contain (e.g. useless, suggestion for new features, bugs reporting). Such an approach has been empirically evaluated and made available in a web-­‐based tool publicly available to all apps’ developers. The achieved results showed that the developed tool: (i) is able to correctly categorise user reviews on the basis of their content (e.g. isolating those reporting bugs) with 78% of accuracy, (ii) produces clusters of reviews (e.g. groups together reviews indicating exactly the same bug to be fixed) that are meaningful from a developer’s point-­‐of-­‐view, and (iii) is considered useful by a software company working in the mobile apps’ development market.

Mobile Web applications

This document presents the work that was elaborated at the company Present Technologies as part of the academic discipline Internship/Industrial Project for the Master’s degree in Informatics and Systems, Software Development branch, at Instituto Superior de Engenharia de Coimbra. The area of the mobile web applications has grown exponentially over the last few years turning it into a very dynamic field where new development platforms and frameworks are constantly emerging. Thus, the internship consisted in the study of two new mobile operating systems, Tizen and Firefox OS, as well as two frameworks for packaging of mobile web applications – Adobe PhoneGap and Appcelerator Titanium. These platforms are in the direct interest of Present Technology since it pretends to use them in its future projects in general and in the Phune Gaming project in particular. Since Television is one of the Present Technologies’ business areas, during the course of the internship it was decided to perform additionally a study of two Smart TV platforms, namely Samsung Smart TV and Opera TV, which was considered as a valuable knowledge for the company. For each of the platforms was performed a study about its architecture, supported standards and the development tools that are provided, nevertheless the focus was on the applications and for this reason a practical case study was conducted. The case studies consisted in the creation of a prototype or packaging of an application, for the case of the packaging tools, in order to prove the feasibility of the applications for the Present Technologies’ needs. The outcome of the work performed during the internship is that it raised the awareness of Present Technology of the studied platforms, providing it with prototypes and written documentation for the platforms’ successful usage in future projects.

Dynamic multi-resolution spatial object derivation for mobile and WWW applications

Online geographic information systems provide the means to extract a subset of desired spatial information from a larger remote repository. Data retrieved representing real-world geographic phenomena are then manipulated to suit the specific needs of an end-user. Often this extraction requires the derivation of representations of objects specific to a particular resolution or scale from a single original stored version. Currently standard spatial data handling techniques cannot support the multi-resolution representation of such features in a database. In this paper a methodology to store and retrieve versions of spatial objects at, different resolutions with respect to scale using standard database primitives and SQL is presented. The technique involves heavy fragmentation of spatial features that allows dynamic simplification into scale-specific object representations customised to the display resolution of the end-user's device. Experimental results comparing the new approach to traditional R-Tree indexing and external object simplification reveal the former performs notably better for mobile and WWW applications where client-side resources are limited and retrieved data loads are kept relatively small.

Survey and applications of standardized energy metrics to mobile networks

The energy consumption and the energy efficiency have become very important issue in optimizing the current as well as in designing the future telecommunications networks. The energy and power metrics are being introduced in order to enable assessment and comparison of the energy consumption and power efficiency of the telecommunications networks and other transmission equipment. The standardization of the energy and power metrics is a significant ongoing activity aiming to define the baseline energy and power metrics for the telecommunications systems. This article provides an up-to-date overview of the energy and power metrics being proposed by the various standardization bodies and subsequently adopted worldwide by the equipment manufacturers and the network operators. © Institut Télécom and Springer-Verlag 2012.and Springer-Verlag 2012.

Developments in technologies for human-centric mobile computing and applications

With recent expansions in technology, mobile computing continues to play a vital role in all aspects of our lives. Digital technology tools such as Web browsing, media tracking, social media, and emailing have made mobile technology more than just a means of communication but has widespread use in business and social networks. Developments in Technologies for Human-Centric Mobile Computing and Applications is a comprehensive collection of knowledge and practice in the development of technologies in human –centric mobile technology. This book focuses on the developmental aspects of mobile technology; bringing together researchers, educators, and practitioners to encourage readers to think outside of the box.

Improving Smartphones Battery Life by Reducing Energy Waste of Background Applications

Smartphones have undergone a remarkable evolution over the last few years, from simple calling devices to full fledged computing devices where multiple services and applications run concurrently. Unfortunately, battery capacity increases at much slower pace, resulting as a main bottleneck for Internet connected smartphones. Several software-based techniques have been proposed in the literature for improving the battery life. Most common techniques include data compression, packet aggregation or batch scheduling, offloading partial computations to cloud, switching OFF interfaces (e.g., WiFi or 3G/4G) periodically for short intervals etc. However, there has been no focus on eliminating the energy waste of background applications that extensively utilize smartphone resources such as CPU, memory, GPS, WiFi, 3G/4G data connection etc. In this paper, we propose an Application State Proxy (ASP) that suppresses/stops the applications on smartphones and maintains their presence on any other network device. The applications are resumed/restarted on smartphones only in case of any event, such as a new message arrival. In this paper, we present the key requirements for the ASP service and different possible architectural designs. In short, the ASP concept can significantly improve the battery life of smartphones, by reducing to maximum extent the usage of its resources due to background applications.

Dynalog: an automated dynamic analysis framework for characterizing android applications

Android is becoming ubiquitous and currently has the largest share of the mobile OS market with billions of application downloads from the official app market. It has also become the platform most targeted by mobile malware that are becoming more sophisticated to evade state-of-the-art detection approaches. Many Android malware families employ obfuscation techniques in order to avoid detection and this may defeat static analysis based approaches. Dynamic analysis on the other hand may be used to overcome this limitation. Hence in this paper we propose DynaLog, a dynamic analysis based framework for characterizing Android applications. The framework provides the capability to analyse the behaviour of applications based on an extensive number of dynamic features. It provides an automated platform for mass analysis and characterization of apps that is useful for quickly identifying and isolating malicious applications. The DynaLog framework leverages existing open source tools to extract and log high level behaviours, API calls, and critical events that can be used to explore the characteristics of an application, thus providing an extensible dynamic analysis platform for detecting Android malware. DynaLog is evaluated using real malware samples and clean applications demonstrating its capabilities for effective analysis and detection of malicious applications.