Using activity-oriented design methods (AODM) to investigatemobile learning

The past few years have witnessed significant interest and developments in researching mobile learning, with a lot of important contributions being made towards understanding and defining mobile learning. However, current research efforts are being redirected towards a new agenda to establish appropriate methods for investigating mobile learning, as this book testifies. This chapter contributes to this research effort by articulating how to adapt Activity-Oriented Design Methods (AODM – see Mwanza, 2002) for use in mobile learning research

A survey on mobility-induced service migration in the fog, edge, and related computing paradigms

The final publication is available at ACM via http://dx.doi.org/10.1145/3326540With the advent of fog and edge computing paradigms, computation capabilities have been moved toward the edge of the network to support the requirements of highly demanding services. To ensure that the quality of such services is still met in the event of users’ mobility, migrating services across different computing nodes becomes essential. Several studies have emerged recently to address service migration in different edge-centric research areas, including fog computing, multi-access edge computing (MEC), cloudlets, and vehicular clouds. Since existing surveys in this area focus on either VM migration in general or migration in a single research field (e.g., MEC), the objective of this survey is to bring together studies from different, yet related, edge-centric research fields while capturing the different facets they addressed. More specifically, we examine the diversity characterizing the landscape of migration scenarios at the edge, present an objective-driven taxonomy of the literature, and highlight contributions that rather focused on architectural design and implementation. Finally, we identify a list of gaps and research opportunities based on the observation of the current state of the literature. One such opportunity lies in joining efforts from both networking and computing research communities to facilitate future research in this area.Peer ReviewedPreprin

Development of ambient intelligence systems based on collaborative task models

So far, the Ambient Intelligence (AmI) paradigm has been applied to the development of a great variety of real systems. They use advanced technologies such as ubiquitous computing, natural interaction and active spaces, which become part of social environments. In the design of AmI systems, the inherent collaboration among users (with the purpose of achieving common goals) is usually represented and treated in an ad-hoc manner. However, the development of this kind of systems can take advantage of rich design models which embrace concepts in the domain of collaborative systems in order to provide the adequate support for explicit or implicit collaboration. Thereby, relevant requirements to be satisfied, such as an effective coordination of human activities by means of task scheduling, demand to dynamically manage and provide group- and context-awareness information. This paper addresses the integration of both proactive and collaborative aspects into a unique design model for the development of AmI systems; in particular, the proposal has been applied to a learning system. Furthermore, the implementation of this system is based on a blackboardbased architecture, which provides a well-defined high-level interface to the physical layer.This research is partially supported by a Spanish R&D Project TIN2004-03140, Ubiquitous Collaborative Adaptive Training (U-CAT)

The Smart Mobile Application Framework (SMAF) - Exploratory Evaluation in the Smart City Contex

What makes mobile apps "smart"? This paper challenges this question by seeking to identify the inherent characteristics of smartness. Starting with the etymological foundations of the term, elements of smart behavior in software applications are extracted from the literature, elaborated and contrasted. Based on these findings we propose a Smart Mobile Application Framework incorporating a set of activities and qualities associated with smart mobile software. The framework is applied to analyze a specific mobile application in the context of Smart Cities and proves its applicability for uncovering the implementation of smart concepts in real-world settings. Hence, this work contributes to research by conceptualizing a new type of application and provides useful insights to practitioners who want to design, implement or evaluate smart mobile applications

Project knowledge into project practice: generational issues in the knowledge management process

This paper considers Learning and Knowledge Transfer within the project domain. Knowledge can be a tenuous and elusive concept, and is challenging to transfer within organizations and projects. This challenge is compounded when we consider generational differences in the project and the workplace. This paper looks at learning, and the transfer of that generated knowledge. A number of tools and frameworks have been considered, together with accumulated extant literature. These issues have been deliberated through the lens of different generational types, focusing on the issues and differences in knowledge engagement and absorption between Baby Boomers, Generation X, and Generation Y/Millennials. Generation Z/Centennials have also been included where appropriate. This is a significant issue in modern project and organizational structures. Some recommendations are offered to assist in effective knowledge transfer across generational types.Accepted manuscrip

An architecture for adaptive task planning in support of IoT-based machine learning applications for disaster scenarios

The proliferation of the Internet of Things (IoT) in conjunction with edge computing has recently opened up several possibilities for several new applications. Typical examples are Unmanned Aerial Vehicles (UAV) that are deployed for rapid disaster response, photogrammetry, surveillance, and environmental monitoring. To support the flourishing development of Machine Learning assisted applications across all these networked applications, a common challenge is the provision of a persistent service, i.e., a service capable of consistently maintaining a high level of performance, facing possible failures. To address these service resilient challenges, we propose APRON, an edge solution for distributed and adaptive task planning management in a network of IoT devices, e.g., drones. Exploiting Jackson's network model, our architecture applies a novel planning strategy to better support control and monitoring operations while the states of the network evolve. To demonstrate the functionalities of our architecture, we also implemented a deep-learning based audio-recognition application using the APRON NorthBound interface, to detect human voices in challenged networks. The application's logic uses Transfer Learning to improve the audio classification accuracy and the runtime of the UAV-based rescue operations