Steve Neely

Resolving uncertainty in context integration and abstraction: context integration and abstraction

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.

Scalable information dissemination for pervasive systems: implementation and evaluation

Pervasive computing systems require large amounts of information to be available to devices in order to support context-aware applications. Information must be routed from the sensors that provide it to the applications that consume it in a timely fashion. However, the potential size and ad hoc nature of these environments makes the management of communications a non-trivial task. One proposed solution to this problem uses gossiping, a class of probabilistic routing protocol, to disseminate context information throughout the environment. Gossiping algorithms require far less in the way of guarantees about network structure, reliability, and latency than alternative approaches, but are unproven in real world scenarios. We describe the on-going development of a framework for evaluating the performance of these algorithms within the context of pervasive environments.

Adaptive middleware for autonomic systems

The increasingly dynamic nature of resource discovery and binding in modern large-scale distributed and mobile systems poses significant challenges for existing middleware platforms. Future platforms must provide strong support for adaptive behaviour in order both to maintain and optimise services in the face of changing context. We use a survey of existing middleware systems to develop some core themes that characterise and constrain the ability of these approaches to support the development of adaptive and autonomic systems, and draw some possible trends for developing future platforms more appropriate to these domains.RésuméLa nature toujours plus dynamique de la découverte et de l’attachement de ressources dans les grands systèmes mobiles et distribués modernes pose des défis significatifs pour les plates-formes logicielles intermédiaires. Ces futures plates-formes devront fournir un large support en ce qui concerne le comportement adaptif afin de maintenir et optimiser les services face à un changement de contexte. Nous exploitons une étude des systèmes logiciels intermédiaires pour développer des thèmes centraux qui caractérisent et limitent la portée de ces approches dans le développement de systèmes autonomes et adaptatifs, et nous exposons des tendances plausibles pour le développement de futures plates-formes plus appropriées à ces domaines.

An evaluation framework for disseminating context information with gossiping

As we gain access to increasing volumes of context data, we face the problem of moving this information from the sensors that produce it to the applications that consume it. Our approach to this problem uses gossiping, a probabilistic routing protocol, to disseminate context information throughout the environment. We present on-going work on evaluating the performance of different gossiping protocols for this purpose.

Harvesting for full-text retrieval

We propose an approach to Distributed Information Retrieval based on the periodic and incremental centralisation of full-text indices of widely dispersed and autonomously managed content sources. Inspired by the success of the Open Archive Initiatives protocol for metadata harvesting, the approach occupies middle ground between: (i) the crawling of content, and (ii) the distribution of retrieval. As in crawling, some data moves towards the retrieval process, but it is statistics about the content rather than content itself. As in distributed retrieval, some processing is distributed along with the data, but it is indexing rather than retrieval itself. We show that the approach retains the good properties of centralised retrieval without renouncing to cost-effective resource pooling. We discuss the requirements associated with the approach and identify two strategies to deploy it on top of the OAI infrastructure.