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.

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Context Aware Computing for The Internet of Things: A Survey

Context-aware systems offer entirely new opportunities for application developers and for end users by gathering context data and adapting systems behaviour accordingly. Especially in combination with mobile devices, these mechanisms are of high value and are used to increase usability tremendously. In this paper, we present common architecture principles of context-aware systems and derive a layered conceptual design framework to explain the different elements common to most context-aware architectures. Based on these design principles, we introduce various existing context-aware systems focusing on context-aware middleware and frameworks, which ease the development of context-aware applications. We discuss various approaches and analyse important aspects in context-aware computing on the basis of the presented systems.

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A survey on context-aware systems

In this provocative book, Barwise and Perry tackle the slippery subject of \"meaning, \" a subject that has long vexed linguists, language philosophers, and logicians.

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Situations and Attitudes.

The development of the Internet in recent years has made it possible and useful to access many different information systems anywhere in the world to obtain information. While there is much research on the integration of heterogeneous information systems, most commercial systems stop short of the actual integration of available data. Data fusion is the process of fusing multiple records representing the same real-world object into a single, consistent, and clean representation.This article places data fusion into the greater context of data integration, precisely defines the goals of data fusion, namely, complete, concise, and consistent data, and highlights the challenges of data fusion, namely, uncertain and conflicting data values. We give an overview and classification of different ways of fusing data and present several techniques based on standard and advanced operators of the relational algebra and SQL. Finally, the article features a comprehensive survey of data integration systems from academia and industry, showing if and how data fusion is performed in each.

Data fusion

Here we propose an OWL encoded context ontology (CONON) for modeling context in pervasive computing environments, and for supporting logic-based context reasoning. CONON provides an upper context ontology that captures general concepts about basic context, and also provides extensibility for adding domain-specific ontology in a hierarchical manner. Based on this context ontology, we have studied the use of logic reasoning to check the consistency of context information, and to reason over low-level, explicit context to derive high-level, implicit context. By giving a performance study for our prototype, we quantitatively evaluate the feasibility of logic based context reasoning for nontime-critical applications in pervasive computing environments, where we always have to deal carefully with the limitation of computational resources.

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Ontology based context modeling and reasoning using OWL

A survey of context modelling and reasoning techniques

The paper discusses the Gaia metaoperating system which extends the reach of traditional operating systems to manage ubiquitous computing habitats and living spaces as integrated programmable environments. Gaia exports services to query, access, and use existing resources and context, and provides a framework to develop user-centric, resource-aware, multidevice, context-sensitive, and mobile applications.

A middleware infrastructure for active spaces

Ubiquitous Computing advocates the construction of massively distributed systems that help transform physical spaces into computationally active and intelligent environments. The design of systems and applications in these environments needs to take account of heterogeneous devices, mobile users and rapidly changing contexts. Most importantly, agents in ubiquitous and mobile environments need to be context-aware so that they can adapt themselves to different situations. In this paper, we argue that ubiquitous computing environments must provide middleware support for context-awareness. We also propose a middleware that facilitates the development of context-aware agents. The middleware allows agents to acquire contextual information easily, reason about it using different logics and then adapt themselves to changing contexts. Another key issue in these environments is allowing autonomous, heterogeneous agents to have a common semantic understanding of contextual information. Our middleware tackles this problem by using ontologies to define different types of contextual information. This middleware is part of Gaia, our infrastructure for enabling Smart Spaces.

A middleware for context-aware agents in ubiquitous computing environments

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Context simplifies and enriches human-human interaction. However, enhancing human-computer interaction through the use of context remains a difficult task. Applications in pervasive and mobile environments need to be context-aware so that they can adapt themselves to rapidly changing situations. One of the problems is that there is no common, reusable model for context used by these environments. In this paper, we propose a model of context that is based on first order predicate calculus. The first order model allows complex rules involving contexts to be written. It also enables automated inductive and deductive reasoning to be done on contextual information. The first order model allows an expressive description of context using Boolean operators and existential and universal quantifiers. Based on this model, an infrastructure to enable context-awareness in ubiquitous computing environments has been developed. The infrastructure allows the easy development and deployment of various sensors and context-aware applications. It also allows distributed reasoning to take place. This context infrastructure is part of our smart spaces framework for ubiquitous computing, Gaia.

Anand Ranganathan

Papers

A survey of context modelling and reasoning techniques

A middleware infrastructure for active spaces

A middleware for context-aware agents in ubiquitous computing environments

A middleware for context-aware agents in ubiquitous computing environments

An infrastructure for context-awareness based on first order logic