C-Arc: A Novel Architecture for Next Generation Context- Aware Systems

Computing is becoming increasingly mobile and ubiquitous. This implies that applications and services must be aware and adapt to highly dynamic environments. However, building contextaware mobile services is currently a complex and time consuming task. The emergence of truly ubiquitous computing, enabled by the availability of mobile and heterogeneous devices and an increasing number of commercial off-the-shelf sensing technologies, is hampered by the lack of standard architectural support for the development of context-aware systems. In this paper, the common architecture principles of context-aware systems are presented and the crucial contextaware architecture issues to support the next generation context-aware systems which will enable seamless service provisioning in heterogeneous, dynamically varying computing and communication environments are identified and discussed. Furthermore, a novel architecture, CArc,is proposed to aid in the development of the next generation context-aware systems. A prototype implemented of C-Arc is also presented to demonstrate the architecture. C-Arc provides support for most of the tasks involved in dealing with context, namely acquiring context from various sources, interpreting context and disseminating context.Keywords: Context-aware architecture, context-aware systems, context-aware mobile services,mobile and ubiquitous computing

Multi-Sensor Context-Awareness in Mobile Devices and Smart Artefacts

The use of context in mobile devices is receiving increasing attention in mobile and ubiquitous computing research. In this article we consider how to augment mobile devices with awareness of their environment and situation as context. Most work to date has been based on integration of generic context sensors, in particular for location and visual context. We propose a different approach based on integration of multiple diverse sensors for awareness of situational context that can not be inferred from location, and targeted at mobile device platforms that typically do not permit processing of visual context. We have investigated multi-sensor context-awareness in a series of projects, and report experience from development of a number of device prototypes. These include development of an awareness module for augmentation of a mobile phone, of the Mediacup exemplifying context-enabled everyday artifacts, and of the Smart-Its platform for aware mobile devices. The prototypes have been explored in various applications to validate the multi-sensor approach to awareness, and to develop new perspectives of how embedded context-awareness can be applied in mobile and ubiquitous computing

Multi-purpose infrastructure for delivering and supporting mobile context-aware applications

The use of contextual information in mobile devices is receiving increasing attention in mobile and ubiquitous computing research. An important requirement for mobile development today is that devices should be able to interact with the context. In this paper we present a series of contributions regarding previous work on context-awareness. In the first place, we describe a client-server architecture that provides a mechanism for preparing target non context-aware applications in order to be delivered as context-aware applications in a semi-automatic way. Secondly, the framework used in the server to instantiate specific components for context-awareness, the Implicit Plasticity Framework, provides independence from the underlying mobile technology used in client device, as it is shown in the case studies presented. Finally, proposed infrastructure deals with the interaction among different context constraints provided by diverse sensors. All of these contributions are extensions to the infrastructure based on the Dichotomic View of plasticity, which now offers multi-purpose support

Acquiring in situ training data for context-aware ubiquitous computing applications

Ubiquitous, context-aware computer systems may ultimately enable computer applications that naturally and usefully respond to a user's everyday activity. Although new algorithms that can automatically detect context from wearable and environmental sensor systems show promise, many of the most flexible and robust systems use probabilistic detection algorithms that require extensive libraries of training data with labeled examples. In this paper, we describe the need for such training data and some challenges we have identified when trying to collect it while testing three contextdetection systems for ubiquitous computing and mobile applications. Author Keywords Context-aware, ubiquitous, computing, supervised learning, experience sampling, user interface design ACM Classification Keywords H5.m Information interfaces and presentation (e.g. HCI): Miscellaneous

Human-Centric Ontology-Based Context Modelling In Tourism

A lot of work has been done up to now in the so called context-aware research field on the one hand and on the ontology research field on the other. Research has been conducted both considering context-awareness and ontology as clearly distinct research disciplines and also utilizing ontologies as a tool for context management. However, context-based applications have only been possible at a laboratory environment so far and they have always worked under very certain, pre-established pre-requisites in a not very stable nor efficient manner, which actually does not fulfil the nature of Ubiquitous Computing vision. Representation and use of context plays a crucial role in many modern IT applications. The ability to process contextual information and perform context-based reasoning is essential not only for mobile and ubiquitous computing systems, but also for a wide range of tourism applications. This paper presents a novel semantic-based human-centric approach to the notion of context that represents an attempt to make Contextual Computing services available to the general public

An Efficient Rule-Based Distributed Reasoning Framework for Resource-bounded Systems

© 2018, The Author(s). Over the last few years, context-aware computing has received a growing amount of attention among the researchers in the IoT and ubiquitous computing community. In principle, context-aware computing transforms a physical environment into a smart space by sensing the surrounding environment and interpreting the situation of the user. This process involves three major steps: context acquisition, context modelling, and context-aware reasoning. Among other approaches, ontology-based context modelling and rule-based context reasoning are widely used techniques to enable semantic interoperability and interpreting user situations. However, implementing rich context-aware applications that perform reasoning on resource-bounded mobile devices is quite challenging. In this paper, we present a context-aware systems development framework for smart spaces, which includes a lightweight efficient rule engine and a wide range of user preferences to reduce the number of rules while inferring personalized contexts. This shows rules can be reduced in order to optimize the inference engine execution speed, and ultimately to reduce total execution time and execution cost

Designing Contextualized Learning

Specht, M. (2008). Designing Contextualized Learning. In H. H. Adelsberger, Kinshuk, J. M. Pawlowski & D. Sampson (Eds.), Handbook on Information Technologies for Education and Training (2th ed., pp. 101-111). Springer, Berlin Heidelberg 2008: International Handbook on Information Systems Series.Contextualized and ubiquitous learning are relatively new research areas that combine the latest developments in ubiquitous and context aware computing with pedagogical approaches relevant to structure more situated and context aware learning support. Searching for different backgrounds of mobile and contextualized learning authors have identified the relations between existing educational paradigms and new classes of mobile appli- cations for education (Naismith, Lonsdale, Vavoula, & Sharples, 2004). Furthermore best practices of mobile learning applications have been iden- tified and discussed in focused workshops (Stone, Alsop, Briggs, & Tomp- sett, 2002; Tatar, Roschelle, Vahey, & Peunel, 2002). Especially in the area of educational field trips (Equator Project, 2003; RAFT, 2003) in the last years innovative approaches for intuitive usage of contextualized mo- bile interfaces have been developed. The following paper describes the motivation and background for con- textualizing learning and illustrates the implementation of a service based and flexible learning toolkit developed in the RAFT project for supporting contextualized collaborative learning support