Ricky Robinson

An Autonomic Context Management System for Pervasive Computing

Context-aware applications adapt to changing computing environments or changing user circumstances/tasks. Context information that supports such adaptations is provided by the underlying infrastructure, which gathers, pre-processes and provisions context information from a variety of context information sources. Such an infrastructure is prone to failures and disconnections that negatively impact on the ability of context-aware applications to adapt (and therefore dramatically impact on their usability). This paper describes a model-based autonomic context management system (ACoMS) that can dynamically configure and reconfigure its context information gathering and pre-processing functionality in order to provide fault tolerant provisioning of context information. The approach uses standards based descriptions of context information sources to increase openness, interoperability and scalability of context-aware systems.

Experiences in Using CC/PP in Context-Aware Systems

Future pervasive systems will be based on ubiquitous, often mobile, interconnected devices supporting mobile users in their computing tasks. These systems need to be context-aware in order to cope with highly dynamic environments. In this paper, we present a context model and a context management system able to support a pervasive system infrastructure. This context model is based on the CC/PP standard proposed to support content negotiation between Web browsers and servers. We have defined a set of CC/PP components and attributes that allow to express a variety of context information types and relationships between context descriptions. The paper discusses pros and cons of using CC/PP as a basis for a context model and a context management system.

Sensor Standards: Overview and Experiences

Sensors are swiftly finding their way into real-world applications, from farming to factory floor monitoring. In parallel with this deployment, several standards are being developed to extend greater interoperability between sensor systems. In this paper, we explore a subset of the various sensor standards, and relate our experiences in attempting to integrate some of these standards for the purposes of developing an autonomic context manager.

Context-aware routing in wireless mesh networks

Wireless mesh networks promise to deliver resilient connectivity among network nodes, allowing data to be relayed seamlessly between mobile clients and infrastructure. Routing is the vital process in archiving self-configuration, self-healing and, to some degree, self-optimization. However, the heterogeneity of network nodes and highly dynamic network topologies create new challenges for developing efficient and adaptive routing solutions. The increasing amount and complexity of information that routing solutions have to consider, in order to cope with the changing network situation and/or user requirements, is a key challenge. We propose adopting a reconfigurable context management system to simplify the task of accessing a variety of information required by adaptive routing protocols and to hide the low-level complexities of information sources management. In addition, we show how our middleware supports faulttolerance of various information failures, freeing protocol developers to concentrate on improving the routing mechanism and the metric information model of routing.

XCML: A Runtime Representation for the Context Modelling Language

The context modelling language (CML), derived from object role modeling (ORM), is a powerful approach for capturing the pertinent object types and relationships between those types in context-aware applications. Its support for data quality metrics, context histories and fact type classifications make it an ideal design tool for context-aware systems. However, CML currently lacks a suitable representation for exchanging context models and instances in distributed systems. A runtime representation can be used by context-aware applications and supporting infrastructure to exchange context information and models between distributed components, and it can be used as the storage representation when relational database facilities are not present. This paper shows the benefits of using CML for modelling context as compared to commonly used RDF/OWL-based context models, shows that translations of CML to RDF or OWL are lossy, discusses existing techniques for serialising ORM models, and presents an alternative XML-based representation for CML called XCML

Street computing: towards an integrated open data application programming interface (API) for cities

This special issue of the Journal of Urban Technology brings together five articles that are based on presentations given at the Street Computing Workshop held on 24 November 2009 in Melbourne in conjunction with the Australian Computer-Human Interaction conference (OZCHI 2009). Our own article introduces the Street Computing vision and explores the potential, challenges, and foundations of this research trajectory. In order to do so, we first look at the currently available sources of information and discuss their link to existing research efforts. Section 2 then introduces the notion of Street Computing and our research approach in more detail. Section 3 looks beyond the core concept itself and summarizes related work in this field of interest. We conclude by introducing the papers that have been contributed to this special issue.

A preference modelling approach to support intelligibility in pervasive applications

Context-aware applications do not always adapt their behaviours in ways that users expect due to a variety of reasons. Applications that lack intelligibility are often incapable of offering explanations to users as to why they decided to adapt their behaviours in certain ways, and providing feedback mechanisms for users to take control of any unwanted adaptation. This can lead to loss of user trust, satisfaction and acceptance of these applications. However, providing intelligibility and user control in applications are non-trivial; it involves exposing internal working components that influence the adaptation decisions, rendering them understandable to non-technical users, and enabling user modifications to those components to correct unexpected adaptations. This paper describes a user preference model regarding application adaptations. The goal is to support intelligibility in applications by facilitating users in generation of clear mental models that enable them to understand the links between particular contextual situations and various adaptive actions. It also aims to support user control of application behaviours by assisting developers in the creation of appropriate feedback mechanisms. These are essential in preventing user frustration at erratic application behaviours.

A complex systems approach to service discovery

Complex systems are those systems composed of many, often very simple, interacting autonomous entities. Interactions between these entities give rise to behaviour and patterns at the global level that cannot be predicted by examining the behaviour of any single individual component in the system. By this definition, pervasive computing environments are complex systems. This paper develops the idea that complex systems theory can aid the design of service discovery, and that results from the field of complex networks research can be applied to service discovery protocols to improve their scalability and robustness. We describe the influences of complex systems theory on the design of an existing service discovery protocol for pervasive computing environments, and show that the application of complex systems ideas can improve scalability, performance and robustness of service discovery protocols.

Reconfigurable middleware for sensor based applications

The pervasive computing paradigm introduced context-aware applications that adapt themselves to their surrounding environment based on context information. Context information may be of different types including sensed context information gathered from a variety of sensors. In pervasive computing user intervention/interactions with their application should be minimised. On the other hand, pervasive computing environments are very dynamic environments -context information that supports adaptation decisions may not always be available due to user mobility and potential network and/or sensor failures. As context information supports autonomous adaptation of applications, the provision of context information itself has to be managed by an autonomic system able to both recognise and recover from context delivery failures. This paper presents the architecture of a context management system that is reconfigurable with regard to sensor and/or network failure and can support dynamic discovery and replacement of sensors. One of the components of this middleware architecture, the model and management of sensor descriptions, is described in more detail as it plays an important role in sensor discovery and replacement.

Defeasible preferences for intelligible pervasive applications to enhance eldercare

Adaptation decisions made by context-aware applications on behalf of users are based on evaluations of current context and preferences of users. This context information is imperfect by nature and can cause applications to behave in ways that users do not expect. Applications that exhibit unwanted behaviour will negatively impact their usability and violate the trust users have in them. Intelligibility and control in applications can help users to understand why they decided to behave in certain ways, and to forgive the applications by enabling users to override the undesirable adaptation. This paper presents a non-monotonic rule based approach (defeasible logic) for modelling user preferences, which serves as the basis of decision-making of application adaptations. It facilitates automatic generation of explanations regarding reasoning of defeasible preferences. The model also supports creation of feedback mechanisms for nontechnical users to formulate their own preferences independently, and modify the adaptation decision process to control application behaviours. Moreover, to demonstrate its applicability we have designed a set of evolvable situations and a context model, which complement the defeasible preferences for building smart home applications to enhance health care of the elderly.