Toward a multidisciplinary model of context to support context-aware computing

Capturing, defining, and modeling the essence of context are challenging, compelling, and prominent issues for interdisciplinary research and discussion. The roots of its emergence lie in the inconsistencies and ambivalent definitions across and within different research specializations (e.g., philosophy, psychology, pragmatics, linguistics, computer science, and artificial intelligence). Within the area of computer science, the advent of mobile context-aware computing has stimulated broad and contrasting interpretations due to the shift from traditional static desktop computing to heterogeneous mobile environments. This transition poses many challenging, complex, and largely unanswered research issues relating to contextual interactions and usability. To address those issues, many researchers strongly encourage a multidisciplinary approach. The primary aim of this article is to review and unify theories of context within linguistics, computer science, and psychology. Summary models within each discipline are used to propose an outline and detailed multidisciplinary model of context involving (a) the differentiation of focal and contextual aspects of the user and application's world, (b) the separation of meaningful and incidental dimensions, and (c) important user and application processes. The models provide an important foundation in which complex mobile scenarios can be conceptualized and key human and social issues can be identified. The models were then applied to different applications of context-aware computing involving user communities and mobile tourist guides. The authors' future work involves developing a user-centered multidisciplinary design framework (based on their proposed models). This will be used to design a large-scale user study investigating the usability issues of a context-aware mobile computing navigation aid for visually impaired people

Context-Aware Recommendation Systems in Mobile Environments

Nowadays, the huge amount of information available may easily overwhelm users when they need to take a decision that involves choosing among several options. As a solution to this problem, Recommendation Systems (RS) have emerged to offer relevant items to users. The main goal of these systems is to recommend certain items based on user preferences. Unfortunately, traditional recommendation systems do not consider the user’s context as an important dimension to ensure high-quality recommendations. Motivated by the need to incorporate contextual information during the recommendation process, Context-Aware Recommendation Systems (CARS) have emerged. However, these recent recommendation systems are not designed with mobile users in mind, where the context and the movements of the users and items may be important factors to consider when deciding which items should be recommended. Therefore, context-aware recommendation models should be able to effectively and efficiently exploit the dynamic context of the mobile user in order to offer her/him suitable recommendations and keep them up-to-date.The research area of this thesis belongs to the fields of context-aware recommendation systems and mobile computing. We focus on the following scientific problem: how could we facilitate the development of context-aware recommendation systems in mobile environments to provide users with relevant recommendations? This work is motivated by the lack of generic and flexible context-aware recommendation frameworks that consider aspects related to mobile users and mobile computing. In order to solve the identified problem, we pursue the following general goal: the design and implementation of a context-aware recommendation framework for mobile computing environments that facilitates the development of context-aware recommendation applications for mobile users. In the thesis, we contribute to bridge the gap not only between recommendation systems and context-aware computing, but also between CARS and mobile computing.<br /

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

A model for context awareness for mobile applications using multiple-input sources

Context-aware computing enables mobile applications to discover and benefit from valuable context information, such as user location, time of day and current activity. However, determining the users’ context throughout their daily activities is one of the main challenges of context-aware computing. With the increasing number of built-in mobile sensors and other input sources, existing context models do not effectively handle context information related to personal user context. The objective of this research was to develop an improved context-aware model to support the context awareness needs of mobile applications. An existing context-aware model was selected as the most complete model to use as a basis for the proposed model to support context awareness in mobile applications. The existing context-aware model was modified to address the shortcomings of existing models in dealing with context information related to personal user context. The proposed model supports four different context dimensions, namely Physical, User Activity, Health and User Preferences. A prototype, called CoPro was developed, based on the proposed model, to demonstrate the effectiveness of the model. Several experiments were designed and conducted to determine if CoPro was effective, reliable and capable. CoPro was considered effective as it produced low-level context as well as inferred context. The reliability of the model was confirmed by evaluating CoPro using Quality of Context (QoC) metrics such as Accuracy, Freshness, Certainty and Completeness. CoPro was also found to be capable of dealing with the limitations of the mobile computing platform such as limited processing power. The research determined that the proposed context-aware model can be used to successfully support context awareness in mobile applications. Design recommendations were proposed and future work will involve converting the CoPro prototype into middleware in the form of an API to provide easier access to context awareness support in mobile applications

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

A Constructive Memory Architecture for Context Awareness

Context-aware computing is a mobile computing paradigm in which applications can discover, use, and take advantage of contextual information, such as the location, tasks and preferences of the user, in order to adapt their behaviour in response to changing operating environments and user requirements. A problem that arises is the inability to respond to contextual information that cannot be classified into any known context. Many context-aware applications require all discovered contextual information to exactly match a type of context, otherwise the application will not react responsively. The ability to learn and recall contexts based on the contextual information discovered has not been very well addressed by previous context-aware applications and research. The aim of this thesis is to develop a component middleware technology for mobile computing devices for the discovery and capture of contextual information, using the situated reasoning concept of constructive memory. The research contribution of this thesis lies in developing a modified architecture for context-aware systems, using a constructive memory model as a way to learn and recall contexts from previous experiences and application interactions. Using a constructive memory model, previous experiences can be induced to construct potential contexts, given a small amount of learning and interaction. The learning process is able to map the many variations of contextual information currently discovered by the user with a predicted type of context based on what the application has stored and seen previously. It only requires a small amount of contextual information to predict a context, something common context-aware systems lack, as they require all information before a type of context is assigned. Additionally, some mechanism to reason about the contextual information being discovered from past application interactions will be beneficial to induce contexts for future experiences

A Constructive Memory Architecture for Context Awareness

Context-aware computing is a mobile computing paradigm in which applications can discover, use, and take advantage of contextual information, such as the location, tasks and preferences of the user, in order to adapt their behaviour in response to changing operating environments and user requirements. A problem that arises is the inability to respond to contextual information that cannot be classified into any known context. Many context-aware applications require all discovered contextual information to exactly match a type of context, otherwise the application will not react responsively. The ability to learn and recall contexts based on the contextual information discovered has not been very well addressed by previous context-aware applications and research. The aim of this thesis is to develop a component middleware technology for mobile computing devices for the discovery and capture of contextual information, using the situated reasoning concept of constructive memory. The research contribution of this thesis lies in developing a modified architecture for context-aware systems, using a constructive memory model as a way to learn and recall contexts from previous experiences and application interactions. Using a constructive memory model, previous experiences can be induced to construct potential contexts, given a small amount of learning and interaction. The learning process is able to map the many variations of contextual information currently discovered by the user with a predicted type of context based on what the application has stored and seen previously. It only requires a small amount of contextual information to predict a context, something common context-aware systems lack, as they require all information before a type of context is assigned. Additionally, some mechanism to reason about the contextual information being discovered from past application interactions will be beneficial to induce contexts for future experiences

Quality of Context in Context-Aware Systems

Context-aware Systems (CASs) are becoming increasingly popular and can be found in the areas of wearable computing, mobile computing, robotics, adaptive and intelligent user interfaces. Sensors are the corner stone of context capturing however, sensed context data are commonly prone to imperfection due to the technical limitations of sensors, their availability, dysfunction, and highly dynamic nature of environment. Consequently, sensed context data might be imprecise, erroneous, conflicting, or simply missing. To limit the impact of context imperfection on the behavior of a context-aware system, a notion of Quality of Context (QoC) is used to measure quality of any information that is used as context information. Adaptation is performed only if the context data used in the decision-making has an appropriate quality level. This paper reports an analytical review for state of the art quality of context in context-aware systems and points to future research directions