Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

Detection of new intentions from users for software service evolution in human-centric context-aware environments using Conditional Random Fields

The capability to accurately and efficiently obtain users’ new requirements is critical for software evolution, so that timely improvements can be made to systems to adapt to the rapidly changing environment. However, current software evolution cycles are often undesirably long because the elicitation of new requirements is mostly based on system performance or delayed user feedback and slow-paced manual analysis of requirements engineers. In this thesis, I propose a general methodology that employs Conditional Random Fields (CRF) as the mathematical foundation to provide quantitative exploration of users’ new intentions that often indicate their new requirements. My methodology is supposed to be applicable in context-aware software environments, and beneficial for discovering new requirements sooner and considerably shortening software evolution cycles. First of all, a situation-centric specification language – SiSL, is proposed to formalize the concepts and ontology of the application domains of our methodology. In SiSL, the domain of discourse is divided into five sorts of entities: action, desire, object, situation and situation-sequence. Another two important concepts, context and intention, are defined based on the five basic entities. A set of axioms are proposed to explain the relations among action, context values and desires. Based on the concepts and axioms in SiSL, a domain knowledge base which can completely describe and specify user’s behaviors and desires in human-centric context-aware environments can be constructed. To infer a user’s desire based on a peculiar form of observations and a specific detection mechanism for user’s new intentions, which may imply new requirements, the Conditional Random Fields (CRF) method is applied as a mathematical foundation to support my research work. In this thesis, the main part of a CRF model, a set of feature functions, specify the relations between observations (actions and context values) and human internal mental states (desires). To infer user’s desires, the CRF model accepts a sequence of observations as the input and calculates the score for each possible sequence-labeling, and outputs the sequence-labeling with the highest score as the inferred desire sequence. By using the CRF method, more accurate desire inference, the precondition for new intention detection, can be achieved compared with other statistical methods. To detect users’ potential new intentions, a CRF model which encodes users’ standard behavior patterns should be built as the metrics for outlier detection. The training data for building the standard CRF model are collected from observing user behaviors that are expected to conform to the system design. In the result of desire inference using the CRF model, the divergent behaviors will be labeled with desires in low confidence, and they can be singled out and analyzed for eliciting user’s potentially new intentions. Besides the divergent behaviors, user’s desire transitions and erroneous behaviors will also be analyzed for detecting new requirements or system drawbacks. The detected potential user’s new intention will be verified, analyzed and summarized to generate a formally new intention, which will drive system evolution through modifications or acquiring new functionalities to satisfy the new requirements. An experiment on a research library system has been conducted to demonstrate how to apply our methodology in detection of users’ new intentions and driving system evolution. Finally, this thesis discusses the threats to validity for our methodology and experiment

Towards Learning ‘Self’ and Emotional Knowledge in Social and Cultural Human-Agent Interactions

Original article can be found at: http://www.igi-global.com/articles/details.asp?ID=35052 Copyright IGI. Posted by permission of the publisher.This article presents research towards the development of a virtual learning environment (VLE) inhabited by intelligent virtual agents (IVAs) and modeling a scenario of inter-cultural interactions. The ultimate aim of this VLE is to allow users to reflect upon and learn about intercultural communication and collaboration. Rather than predefining the interactions among the virtual agents and scripting the possible interactions afforded by this environment, we pursue a bottomup approach whereby inter-cultural communication emerges from interactions with and among autonomous agents and the user(s). The intelligent virtual agents that are inhabiting this environment are expected to be able to broaden their knowledge about the world and other agents, which may be of different cultural backgrounds, through interactions. This work is part of a collaborative effort within a European research project called eCIRCUS. Specifically, this article focuses on our continuing research concerned with emotional knowledge learning in autobiographic social agents.Peer reviewe

A software development framework for context-aware systems

The beginning of the new century has been characterised by the miniaturisation and accessibility of electronics, which has enabled its widespread usage around the world. This technological background is progressively materialising the future of the remainder of the century, where industry-based societies have been moving towards information-based societies. Information from users and their environment is now pervasively available, and many new research areas have born in order to shape the potential of such advancements. Particularly, context-aware computing is at the core of many areas such as Intelligent Environments, Ambient Intelligence, Ambient Assisted Living or Pervasive Computing. Embedding contextual awareness into computers promises a fundamental enhancement in the interaction between computers and humans. While traditional computers require explicit commands in order to operate, contextually aware computers could also use information from the background and the users to provide services according to the situation. But embedding this contextual awareness has many unresolved challenges. The area of context-aware computing has attracted the interest of many researchers that have presented different approaches to solve particular aspects on the implementation of this technology. The great corpus of research in this direction indicates that context-aware systems have different requirements than those of traditional computing. Approaches for developing context-aware systems are typically scattered or do not present compatibility with other approaches. Existing techniques for creating context-aware systems also do not focus on covering all the different stages of a typical software development life-cycle. The contribution of this thesis is towards the foundation layers of a more holistic approach, that tries to facilitate further research on the best techniques for developing these kinds of systems. The approach presents a framework to support the development not only with methodologies, but with open-source tools that facilitate the implementation of context-aware systems in mobile and stationary platforms

Intention Prediction Mechanism In An Intentional Pervasive Information System

International audienceNowadays, the development of pervasive technologies has allowed the improvement of services availability. These services, offered by information systems (IS), are becoming more pervasive, i.e., accessed anytime, anywhere. However, those pervasive information systems (PIS) remain too complex for the user, who just wants a service satisfying his needs. This complexity requires considerable efforts from the user in order to select the most appropriate service. Thus, an important challenge in PIS is to reduce user's understanding effort. In this chapter, we propose to enhance PIS transparency and productivity through a user-centred vision based on an intentional approach. We propose an intention prediction approach. This approach allows anticipating user's future requirements, offering the most suitable service in a transparent and discrete way. This intention prediction approach is guided by the user's context. It is based on the analysis of the user's previous situations in order to learn user's behaviour in a dynamic environment