Lorcan Coyle's Home on Tinterweb

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The ability to identify the behavior of people in a home is at the core of Smart Home functionality. Such environments are equipped with sensors that unobtrusively capture information about the occupants. Reasoning mechanisms transform the technical, frequently noisy data of sensors into meaningful interpretations of occupant activities. Time is a natural human way to reason about activities. Peoples' activities in the home often have an identifiable routine; activities take place at distinct times throughout the day and last for predicable lengths of time. However, the inclusion of temporal information is still limited in the domain of activity recognition. Evidence theory is gaining increasing interest in the field of activity recognition, and is suited to the incorporation of time related domain knowledge into the reasoning process. In this paper, an evidential reasoning framework that incorporates temporal knowledge is presented. We evaluate the effectiveness of the framework using a third party published smart home dataset. An improvement in activity recognition of 70% is achieved when time patterns and activity durations are included in activity recognition. We also compare our approach with Naïve Bayes classifier and J48 Decision Tree, with temporal evidence theory achieving higher accuracies than both classifiers.

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Much research on context quality in context-aware systems divides into two strands: (1) the qualitative identification of quality measures and (2) the use of uncertain reasoning techniques. In this paper, we combine these two strands, exploring the problem of how to identify and propagate quality through the different context layers in order to support the context reasoning process. We present a generalised, structured context quality model that supports aggregation of quality from sensor up to situation level. Our model supports reasoning processes that explicitly aggregate context quality, by enabling the identification and quantification of appropriate quality parameters. We demonstrate the efficacy of our model using an experimental sensor data set, gaining a significant improvement in situation recognition for our voting based reasoning algorithm.

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Highly sensorised systems present two parallel challenges: how to design a sensor suite that can efficiently and cost-effectively support the needs of given services; and to extract the semantically relevant interpretations, or "situations", from the flood of context data collected by the sensors. We describe mathematical structures called situation lattices that can be used to address these two problems simultaneously, allowing designers to both design and refine situation identification whilst offering insights into the design of sensor suites. We validate the accuracy and efficiency of our technique against a third-party data set and demonstrate how it can be used to evaluate sensor suite designs.

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Situations, the semantic interpretations of context, provide a better basis for selecting adaptive behaviours than context itself. The definition of situations typically rests on the ability to define logical expressions and inference methods to identify particular situations. In this paper we extend this approach to provide for efficient organisation and selection in systems with large numbers of situations having structured relationships to each other. We apply lattice theory to define a specialisation relationship across situations, and show how this can be used to improve the identification of situations using lattice operators and uncertain reasoning. We demonstrate the technique against a real-world dataset.

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Pervasive computing is typically highly sensor-driven, but sensors provide only evidence of fact rather than facts themselves. The uncertainty of sensor data will affect each component in a pervasive computing system, which may decrease the quality of its provided services. We provide a general model to represent semantics of uncertainty in different levels (e.g., sensor, lower-level context and higher-level context). Within our model, fine-grained approaches are applied to evaluate and propagate uncertainties. They will help to resolve the uncertainty in each process of context management so that the effect of uncertainty on system services will be minimised.

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Context-aware systems typically use data sensed from the environment to drive adaptive behaviour. Sensed data is inherently imprecise and uncertain; in addition, new uncertainties are introduced when sensed data is fused with other data to infer context at the more abstract level of situations. We present an uncertainty model which aggregates context uncertainty and provides mechanisms to capture uncertainty at situation level. We demonstrate the application of the model in a sample scenario using an experimental data set.

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Location detection systems that use tags are a popular means of determining a user's location. These systems are characterised as requiring the user to carry an identity tag that is detected by sensors, which typically use some form of triangulation to determine location. Although estimates for precision for these systems are published by the respective manufacturers the customer experience can vary widely. This paper proposes an evaluation framework which will allow different systems to be compared more directly. This framework is specifically targeted at evaluating the experiences of tagging humans, which can cause particular difficulties due to the fact that many tag-based systems use communication frequencies that cannot pass easily through the human body.

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Event-based systems are a popular substrate for distributing information derived from sensors to be used in driving adaptive behaviour. This paper argues that using events directly provides a poor model of context, and that a hybrid approach that uses events to populate and maintain a distributed knowledge base offers a more stable solution. The inherent uncertainties in both sensor data and reasoning imply that traditional knowledge-based system techniques applied to context be extended to deal with more uncertain reasoning

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Much recent research has focused on using situations rather than individual pieces of context as a means to trigger adaptive system behaviour. While current research on situations emphasises their representation and composition, they do not provide an approach on how to organise and identify their occurrences efficiently. This paper describes how lattice theory can be utilised to organise situations, which reflects the internal structure of situations such as generalisation and dependence. We claim that situation lattices will prove beneficial in identifying situations, and maintaining the consistency and integrity of situations. They will also help in resolving the uncertainty issues inherent in context and situations by working with Bayesian Networks.

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