Virtual environment trajectory analysis:a basis for navigational assistance and scene adaptivity

This paper describes the analysis and clustering of motion trajectories obtained while users navigate within a virtual environment (VE). It presents a neural network simulation that produces a set of five clusters which help to differentiate users on the basis of efficient and inefficient navigational strategies. The accuracy of classification carried out with a self-organising map algorithm was tested and improved to in excess of 85% by using learning vector quantisation. This paper considers how such user classifications could be utilised in the delivery of intelligent navigational support and the dynamic reconfiguration of scenes within such VEs. We explore how such intelligent assistance and system adaptivity could be delivered within a Multi-Agent Systems (MAS) context

The Citizen Observatory: Enabling Next Generation Citizen Science

Background: Citizen science offers an attractive paradigm for addressing some of the complex problems facing society. However, translating the paradigm\u27s potential into meaningful action and sustainable impact remains a formidable challenge. Historically, the citizen science landscape was fractured into silos of activities; nonetheless, it has demonstrably delivered credible results. An innovative concept of the Citizen Observatory offers a tractable means of mitigating many of the recurring issues that historically afflicted citizen science initiatives, thus empowering a new generation of citizen scientists. Citizen Observatories may be regarded as open, standardised software platforms for community-based monitoring of any phenomenon of interest. Objectives: This paper seeks to validate a Citizen Observatory in a traditional citizen science context, that of butterfly recording. Methods/Approach: A case study was undertaken in a UNESCO-designated Biosphere Reserve. Results: A community of citizen scientists successfully recorded various observations concerning butterflies, their feeding behaviours, and their habitat. The resultant dataset was made available to the local government environmental agency. Conclusions: The Citizen Observatory model offers a realistic basis for enabling more sustainable participatory science activities. Such developments have implications for non-government organisations, businesses, and local governments

The TRUST Project: Immersive Play for Children in Hospitals and Rehabilitation

In recent years, children’s hospitals worldwide have investigated using aspects of video games and other media tools available to young patients to address such problems as isolation, stress management, and rehabilitation. There is a growing body of evidence suggesting that these communities, despite being limited to text interfaces, can improve pain scores and may improve depressive symptoms, reduce anxiety, and raise self-esteem. The objectives of the TRUST Project is to develop game-based interactive play in order to aid in children rehabilitation and ease the stresses associated with hospital scenarios. The play environment is designed to be inclusive, i.e. not solely for able-bodied and able-minded people. The virtual environments and game scenarios have been tailored to an audience of 8 to 13 year old children with varying degrees of abilities

AGEO : Natural hazard prevention and awareness raising through citizen observatories

ABSTRACT:The Platform for Atlantic Geohazard Risk Management (AGEO) is a new project co-financed under the Interreg Programme for the Atlantic Area which aims to launch five Citizens’ Observatory pilots on geohazards according to regional priorities.info:eu-repo/semantics/publishedVersio

Data Quality and Trust: Review of Challenges and Opportunities for Data Sharing in IoT

Existing research recognizes the critical role of quality data in the current big-data and Internet of Things (IoT) era. Quality data has a direct impact on model results and hence business decisions. The growth in the number of IoT-connected devices makes it hard to access data quality using traditional assessments methods. This is exacerbated by the need to share data across different IoT domains as it increases the heterogeneity of the data. Data-shared IoT defines a new perspective of IoT applications which benefit from sharing data among different domains of IoT to create new use-case applications. For example, sharing data between smart transport and smart industry can lead to other use-case applications such as intelligent logistics management and warehouse management. The benefits of such applications, however, can only be achieved if the shared data is of acceptable quality. There are three main practices in data quality (DQ) determination approaches that are restricting their effective use in data-shared platforms: (1) most DQ techniques validate test data against a known quantity considered to be a reference; a gold reference. (2) narrow sets of static metrics are used to describe the quality. Each consumer uses these metrics in similar ways. (3) data quality is evaluated in isolated stages throughout the processing pipeline. Data-shared IoT presents unique challenges; (1) each application and use-case in shared IoT has a unique description of data quality and requires a different set of metrics. This leads to an extensive list of DQ dimensions which are difficult to implement in real-world applications. (2) most data in IoT scenarios does not have a gold reference. (3) factors endangering DQ in shared IoT exist throughout the entire big-data model from data collection to data visualization, and data use. This paper aims to describe data-shared IoT and shared data pools while highlighting the importance of sharing quality data across various domains. The article examines how we can use trust as a measure of quality in data-shared IoT. We conclude that researchers can combine such trust-based techniques with blockchain for secure end-to-end data quality assessment

A Framework to Implement IoT Network Performance Modelling Techniques for Network Solution Selection

No single network solution for Internet of Things (IoT) networks can provide the required level of Quality of Service (QoS) for all applications in all environments. This leads to an increasing number of solutions created to fit particular scenarios. Given the increasing number and complexity of solutions available, it becomes difficult for an application developer to choose the solution which is best suited for an application. This article introduces a framework which autonomously chooses the best solution for the application given the current deployed environment. The framework utilises a performance model to predict the expected performance of a particular solution in a given environment. The framework can then choose an apt solution for the application from a set of available solutions. This article presents the framework with a set of models built using data collected from simulation. The modelling technique can determine with up to 85% accuracy the solution which performs the best for a particular performance metric given a set of solutions. The article highlights the fractured and disjointed practice currently in place for examining and comparing communication solutions and aims to open a discussion on harmonising testing procedures so that different solutions can be directly compared and offers a framework to achieve this within IoT networks