Handling Emergent Conflicts in Adaptable Rule-based Sensor Networks

This thesis presents a study into conflicts that emerge amongst sensor device rules when such devices are formed into networks. It describes conflicting patterns of communication and computation that can disturb the monitoring of subjects, and lower the quality of service. Such conflicts can negatively affect the lifetimes of the devices and cause incorrect information to be reported. A novel approach to detecting and resolving conflicts is presented. The approach is considered within the context of home-based psychiatric Ambulatory Assessment (AA). Rules are considered that can be used to control the behaviours of devices in a sensor network for AA. The research provides examples of rule conflict that can be found for AA sensor networks. Sensor networks and AA are active areas of research and many questions remain open regarding collaboration amongst collections of heterogeneous devices to collect data, process information in-network, and report personalised findings. This thesis presents an investigation into reliable rule-based service provisioning for a variety of stakeholders, including care providers, patients and technicians. It contributes a collection of rules for controlling AA sensor networks. This research makes a number of contributions to the field of rule-based sensor networks, including areas of knowledge representation, heterogeneous device support, system personalisation, and in particular, system reliability. This thesis provides evidence to support the conclusion that conflicts can be detected and resolved in adaptable rule-based sensor networks

Exploring conflicts in rule-based Sensor Networks

This paper addresses rule conflicts within wireless sensor networks. The work is situatedwithin psychiatric ambulatory assessment settings where patients are monitored in andaround their homes. Detecting behaviours within these settings favours sensor networks,while scalability and resource concerns favour processing data on smart nodes incorporatingrule engines. Such monitoring involves personalisation, thereby becoming important toprogram node rules on the fly. Since rules may originate from distinct sources and changeover time, methods are required to maintain rule consistency. Drawing on lessons fromFeature Interaction, the paper contributes novel approaches for detecting and resolving rule-conflict across sensor networks

Personalised Ambient Monitoring: Supporting Mental Health at Home

Many who suffer from Bipolar Disorder are keen to control their disease with as little external medical intervention as possible. Self help through websites, meetings, and questionnaires are commonly employed approaches. The PAM project has worked to help this process. It has endeavoured to form an ambient system of monitoring to provide objective feedback to bipolar sufferers. Particular effort has been made to allow the system network of sensors to be personalised and ambient, and operate without the need for a centralised resource. So a sensor system that embeds the processing of the sensor data has been developed. It allows the processing to be changed at run-time to allow personalisation and for changes in behaviour over time. This chapter describes the current status of the project; in particular it describes the rule-based system that the project developed, and an initial technical trial and its outcomes. The rule-based approach and the trial description should be of general interest to both technical developers and practitioners. The latter part of the chapter however is aimed more at the technical developer and focuses on the technical outcomes from the trial with a focus on the programmability aspects and addresses consistency issues that arise with such a flexible programming environment

Dynamically Programmable m-Psychiatry System For Self-Management of Bipolar Disorder

A rule-oriented approach to programming mobile psychiatric monitoring systems was designed. Initial simulations of rule processing have tested system personalisation issues and reviewed characteristics of the rule-oriented approach including the degree of task expressiveness and ease of expressing domain knowledge. A technical trial is being prepared to analyse the approach in a non-simulated environment

Telecare Service Challenge: Conflict Detection

Telecare and telehealth system services can be dynamically configured to collect, analyse, store, and adapt to multimodal data about people as they go about their activities of daily life. These services need to be able to personalise to subjects and adapt to changes in lifestyles, environments and technology. Such dynamic adaptability may be well supported by a low-level rule programming approach; however measures may need to be taken to limit the emergence of conflicts between the distributed rulesets owing to differing programmatic assumptions and unexpected changes. Here, we consider types of conflict that might arise when a variety of care devices are brought together and begin to rely on each others' services. This paper describes a distributed rule-based conflict detection approach for use with heterogeneous mobile and home care devices. We propose methods that make it possible to detect certain forms of rule conflict. To do so, we introduce Event Calculus based logic for writing device rules and an analytical framework for conflict detection

The Design and Evaluation of Personalised Ambient Mental Health Monitors

Mobile and environmental sensing technology can be used to assess human behaviour and mental health trajectories outside of laboratories and in ecologically-relevant settings. To achieve maximum benefit, the set of equipment and the monitoring patterns must be personalised to respect individual needs and fit into individual lifestyles. We have developed a sensor network infrastructure for mobile phones and homecare using a rule-oriented programming architecture to monitor the activity signatures of people with Bipolar Disorder (BD). We believe that the use of this rule-based paradigm within the network for a mental health setting to be a contribution of this work. We are evaluating the effectiveness of the technology in an ongoing technical trial with control participants as a precursor to studying the effectiveness of the system for use with people with BD. In this paper, we report the design and development of the monitoring system along with preliminary findings from the technical trial of the system, and discuss future developments

The DAMES Metadata Approach

The DAMES project will provide high quality data management activities services to the social science research community based on an e-social science infrastructure. The infrastructure is supported by the collection and use of metadata to describe datasets and other social science resources. This report reviews the metadata requirements of the DAMES services, reviews a number of metadata standards, and discusses how the selected standards can be used to support the DAMES services. The kinds of metadata focussed upon in this report include metadata for describing social science microdatasets and other resources such as data analysis processing instruction files, metadata for grouping and linking datasets, and metadata for describing the provenance of data as it is transformed through analytical procedures. The social science metadata standards reviewed include: • The Common Warehouse Metamodel (CWM) • The Data Documentation Initiative (DDI) versions 2 and 3 • Dublin Core • Encoded Archival Description (EAD) • e-Government Metadata Standard (e-GMS) • ELSST and HASSET • MAchine-Readable Cataloging (MARC) • Metadata Encoding and Transmission Standard (METS) • MetaDater • Open Archives Initiative (OAI) • Open Archival Information System (OAIS) • Statistical Data and Metadata Exchange (SDMX) • Text Encoding Initiative (TEI) The review concludes that the DDI standard version 3.0 is the most appropriate one to be used in the DAMES project and explains how best to integrate the standard into the project. This includes a description of how to capture metadata upon resource registration, upgrade the metadata from accessible resources available throughthe GEODE project, use the metadata for resource discovery, and generate provenance metadata during data transformation procedures. In addition, a “metadata wizard” is described to help with data management activities

Collaborative systems for enhancing the analysis of social surveys: the grid enabled specialist data environments

This paper describes a group of online services which are designed to support social survey research and the production of statistical results. The 'Grid Enabled Specialist Data Environment' (GESDE) services constitute three related systems which offer facilities to search for, extract and exploit supplementary data and metadata concerned with the measurement and operationalisation of survey variables. The services also offer users the opportunity to deposit and distribute their own supplementary data resources for the benefit of dissemination and replication of the details of their own analysis. The GESDE services focus upon three application areas: specialist data relating to the measurement of occupations; educational qualifications; and ethnicity (including nationality, language, religion, national identity). They identify information resources related to the operationalisation of variables which seek to measure each of these concepts - examples include coding frames, crosswalk and translation files, and standardisation and harmonisation recommendations. These resources constitute important supplementary data which can be usefully exploited in the analysis of survey data. The GESDE services work by collecting together as much of this supplementary data as possible, and making it searchable and retrievable to others. This paper discusses the current features of the GESDE services (which have been designed as part of a wider programme of ‘e-Science’ research in the UK), and considers ongoing challenges in providing effective support for variable-oriented statistical analysis in the social sciences

Metadata Creation, Transformation and Discovery for Social Science Data Management: The DAMES Project Infrastructure

This paper discusses the use of metadata, underpinned by DDI (Data Documentation Initiative), to support social science data management. Social science data management refers broadly to the discovery, preparation, and manipulation of social science data for the purposes of research and analysis. Typical tasks include recoding variables within a dataset, and linking data from different sources. A description is given of the DAMES project (Data Management through e-Social Science), a UK project which is building resources and services to support quantitative social science data management activities. DAMES provides generic facilities for performing (and recording) operations on data. Specific resources include support for analysis through micro-simulation, and support for access to specialist data on occupations, educational qualifications, measures of ethnicity and immigration, social care, and mental health. The DAMES project tools and services can generate, use, transform, and search metadata that describe social science datasets (including microdata from social survey datasets and aggregate-level macrodata). On DAMES, these metadata are described by various standards including DDI Version 2, DDI Version 3, JSDL (Job Submission Definition Language), and the purpose-designed JFDL (Job Flow Definition Language). The paper describes how DAMES uses metadata with a range of resources that are integrated with a job execution infrastructure, a Web portal, and a tool for data fusion

Personalised Ambient Monitoring (PAM) for People with Bipolar Disorder

This paper presents the architecture and preliminary trial results of a monitoring system for patients with bipolar disorder containing environmental and wearable sensors