Context-driven progressive enhancement of mobile web applications: a multicriteria decision-making approach

Personal computing has become all about mobile and embedded devices. As a result, the adoption rate of smartphones is rapidly increasing and this trend has set a need for mobile applications to be available at anytime, anywhere and on any device. Despite the obvious advantages of such immersive mobile applications, software developers are increasingly facing the challenges related to device fragmentation. Current application development solutions are insufficiently prepared for handling the enormous variety of software platforms and hardware characteristics covering the mobile eco-system. As a result, maintaining a viable balance between development costs and market coverage has turned out to be a challenging issue when developing mobile applications. This article proposes a context-aware software platform for the development and delivery of self-adaptive mobile applications over the Web. An adaptive application composition approach is introduced, capable of autonomously bypassing context-related fragmentation issues. This goal is achieved by incorporating and validating the concept of fine-grained progressive application enhancements based on a multicriteria decision-making strategy

A Mobile Query Service for Integrated Access to Large Numbers of Online Semantic Web Data Sources

From the Semantic Web’s inception, a number of concurrent initiatives have given rise to multiple segments: large semantic datasets, exposed by query endpoints; online Semantic Web documents, in the form of RDF files; and semantically annotated web content (e.g., using RDFa), semantic sources in their own right. In various mobile application scenarios, online semantic data has proven to be useful. While query endpoints are most commonly exploited, they are mainly useful to expose large semantic datasets. Alternatively, mobile RDF stores are utilized to query local semantic data, but this requires the design-time identification and replication of relevant data. Instead, we present a mobile query service that supports on-the-fly and integrated querying of semantic data, originating from a largely unused portion of the Semantic Web, comprising online RDF files and semantics embedded in annotated webpages. To that end, our solution performs dynamic identification, retrieval and caching of query-relevant semantic data. We explore several data identification and caching alternatives, and investigate the utility of source metadata in optimizing these tasks. Further, we introduce a novel cache replacement strategy, fine- tuned to the described query dataset, and include explicit support for the Open World Assumption. An extensive experimental validation evaluates the query service and its alternative components

Cultivating Community Interactions in Citizen Science: Connecting People to Each Other and the Environment

Citizen science leverages a distributed user-base which participates in crowd-sourced scientific inquiry. Geotagger is a citizen science project that allows people to collaboratively investigate the natural world around them and share their findings. Citizens are rarely compensated for their work and individual contributors can feel isolated which leads to motivation problems. This thesis focuses on engaging citizen scientists and motivating their contributions via social interaction and engagement. As a part of this work, a number of social enhancements have been developed as extensions to the existing Geotagger project. These enhancements and their effect on social engagement were evaluated using in-field studies and design investigations with children. In the studies, children engaged effectively with each other using the social enhancements in Geotagger, and showed a preference for the application that included these social enhancements

Reducing User Perceived Latency in Smart Phones Exploiting IP Network Diversity

The Fifth Generation (5G) wireless networks set its standard to provide very high data rates, Ultra-Reliable Low Latency Communications (URLLC), and significantly improved Quality of Service (QoS). 5G networks and beyond will power up billions of connected devices as it expands wireless services to edge computing and the Internet of Things (IoT). The Internet protocol suite continues its evolution from IPv4 addresses to IPv6 addresses by increasing the adoption rate and prioritizing IPv6. Hence, Internet Service Providers (ISP's) are using the address transition method called dual-stack to prioritize the IPv6 while supporting the existing IPv4. But this causes more connectivity overhead in dual-stack as compared to the single-stack network due to its preference schema towards the IPv6. The dual-stack network increases the Domain Name System (DNS) resolution and Transmission Control Protocol (TCP) connection time that results in higher page loading time, thereby significantly impacting the user experience. Hence, we propose a novel connectivity mechanism, called NexGen Connectivity Optimizer (NexGenCO), which redesigns the DNS resolution and TCP connection phases to reduce the user-perceived latency in the dual-stack network for mobile devices. Our solution utilizes the IP network diversity to improve connectivity through concurrency and intelligent caching. NexGenCO is successfully implemented in Samsung flagship devices with Android Pie and further evaluated using both simulated and live-air networks. It significantly reduces connectivity overhead and improves page loading time up to 18%

Graphy: Exploring the potential of the Contacts application

The number of mobile devices is growing very fast. Smart phones and tablets are, step by step, replacing desktops and laptops as the primary method of computing in daily life. Along with the rapid evolution of mobile devices, the applications on them are undergoing fast transformation. We can see many improvements in traditional applications (messaging, calling, etc.) like multimedia text messages, video calls, voice over IP and so forth. However, the Contacts application has not changed much while it has many potentials. In this thesis, we propose a new model which improves the Contacts application by introducing three novel capabilities: searching for contacts by their miscellaneous information, retaining knowledge of contacts via a tags system, and establishing a Personal Social Network which consists of the relationships between the contacts. By introducing these capabilities, the model helps its users to accomplish new tasks which are not currently handled by modern Contacts applications. Furthermore, the model is implemented and become a fully functional prototype on iOS and Android. The prototype is then evaluated in a user study and a system performance test. The studies yield positive results which indicate that the three new capabilities are valuable and should be included in today’s Contacts applications

noteEd - A web-based lecture capture system

Electronic capture and playback of lectures has long been the aim of many academic projects. Synote is an application developed under MACFoB (Multimedia Annotation and Community Folksonomy Building) project to synchronise the playback of lecture materials. However, Synote provides no functionality to capture such multimedia. This project involves the creation of a system called noteEd, which will capture a range of multimedia from lectures and make them available to Synote. This report describes the evolution of the noteEd project throughout the design and implementation of the proposed system. The performance of the system was checked in a user acceptance test with the customer, which is discussed after screenshots of our solution. Finally, the project management is presented containing a final project evaluation

RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices

BACKGROUND: With a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable, and extensible platform is of high interest to the open source mHealth community. The European Union Innovative Medicines Initiative Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) program is an exemplary project with the requirements to support the collection of high-resolution data at scale; as such, the Remote Assessment of Disease and Relapse (RADAR)-base platform is designed to meet these needs and additionally facilitate a new generation of mHealth projects in this nascent field. // OBJECTIVE: Wide-bandwidth networks, smartphone penetrance, and wearable sensors offer new possibilities for collecting near-real-time high-resolution datasets from large numbers of participants. The aim of this study was to build a platform that would cater for large-scale data collection for remote monitoring initiatives. Key criteria are around scalability, extensibility, security, and privacy. // METHODS: RADAR-base is developed as a modular application; the backend is built on a backbone of the highly successful Confluent/Apache Kafka framework for streaming data. To facilitate scaling and ease of deployment, we use Docker containers to package the components of the platform. RADAR-base provides 2 main mobile apps for data collection, a Passive App and an Active App. Other third-Party Apps and sensors are easily integrated into the platform. Management user interfaces to support data collection and enrolment are also provided. // RESULTS: General principles of the platform components and design of RADAR-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-CNS projects: Multiple Sclerosis, Epilepsy, and Depression cohorts. // CONCLUSIONS: RADAR-base is a fully functional, remote data collection platform built around Confluent/Apache Kafka and provides off-the-shelf components for projects interested in collecting mHealth datasets at scale