Sarah Mount

Smart care spaces: needs for intelligent at-home care

Pressures on the availability of healthcare spaces, the high costs of institutional care, and the desires of those being cared for, cause a current move toward care either at home or within low-supervision environments. This brings about an important question: how can smart care spaces be created that intelligently link the home care environment to the needs of the cared-for? To a significant degree this involves development of sensored spaces connected to intelligent computer-systems. However, that intelligence requires an understanding of how sensors can provide more than just environmental variables, instead making systems aware of symptoms, comfort and potential needs for intervention. Therefore, this paper discusses the current need for development of smart care spaces, provides an introduction to some of the cost-effective sensors available, and reviews links between sensor data and medical conditions. It will conclude that there is a growing need for smart care spaces that allow effective monitorin...

Map as a Service: A Framework for Visualising and Maximising Information Return from Multi-ModalWireless Sensor Networks

This paper presents a distributed information extraction and visualisation service, called the mapping service, for maximising information return from large-scale wireless sensor networks. Such a service would greatly simplify the production of higher-level, information-rich, representations suitable for informing other network services and the delivery of field information visualisations. The mapping service utilises a blend of inductive and deductive models to map sense data accurately using externally available knowledge. It utilises the special characteristics of the application domain to render visualisations in a map format that are a precise reflection of the concrete reality. This service is suitable for visualising an arbitrary number of sense modalities. It is capable of visualising from multiple independent types of the sense data to overcome the limitations of generating visualisations from a single type of sense modality. Furthermore, the mapping service responds dynamically to changes in the environmental conditions, which may affect the visualisation performance by continuously updating the application domain model in a distributed manner. Finally, a distributed self-adaptation function is proposed with the goal of saving more power and generating more accurate data visualisation. We conduct comprehensive experimentation to evaluate the performance of our mapping service and show that it achieves low communication overhead, produces maps of high fidelity, and further minimises the mapping predictive error dynamically through integrating the application domain model in the mapping service.

Interpolation techniques for building a continuous map from discrete wireless sensor network data

Wireless sensor networks (WSNs) typically gather data at a discrete number of locations. However, it is desirable to be able to design applications and reason about the data in more abstract forms than in points of data. By bestowing the ability to predict inter-node values upon the network, it is proposed that it will become possible to build applications that are unaware of the concrete reality of sparse data. This interpolation capability is realised as a service of the network. In this paper, the ‘map’ style of presentation has been identified as a suitable sense data visualisation format. Although map generation is essentially a problem of interpolation between points, a new WSN service, called the map generation service, which is based on a Shepard interpolation method, is presented. A modified Shepard method that aims to deal with the special characteristics of WSNs is proposed. It requires small storage, can be localised and integrates the information about the application domain to further reduce the map generation cost and improve the mapping accuracy. Empirical analysis has shown that the map generation service is an accurate, a flexible and an efficient method. Copyright

Trove: a physical game running on an ad-hoc wireless sensor network

This paper describes Trove, a physical game implemented on a wireless sensor network (WSN). Architecturally, the WSN is a decentralized system, exhibiting local node processing and information extraction, collaborative inter-node behaviour and local decision making capabilities. From the perspective of the players, Trove is a multi-player, real time, physical game. The user-centered narrative, configuration and game play of Trove ate presented as well as its design and implementation.Trove will be used at Coventry University as a pedagogical aid in under- and postgraduate modules which incorporate concepts from pervasive computing and sensor networks; and also for the dissemination of research work to members of the public. Although educational through its use, the work presented here concerns, from a technical viewpoint, the very specifics of physical WSN design, implementation and deployment and forms a good basis for further proof of concept experimentation within the domain.

Virtual machine warmup blows hot and cold

Virtual Machines (VMs) with Just-In-Time (JIT) compilers are traditionally thought to execute programs in two phases: the initial warmup phase determines which parts of a program would most benefit from dynamic compilation, before JIT compiling those parts into machine code; subsequently the program is said to be at a steady state of peak performance. Measurement methodologies almost always discard data collected during the warmup phase such that reported measurements focus entirely on peak performance. We introduce a fully automated statistical approach, based on changepoint analysis, which allows us to determine if a program has reached a steady state and, if so, whether that represents peak performance or not. Using this, we show that even when run in the most controlled of circumstances, small, deterministic, widely studied microbenchmarks often fail to reach a steady state of peak performance on a variety of common VMs. Repeating our experiment on 3 different machines, we found that at most 43.5% of pairs consistently reach a steady state of peak performance.

E-Education 3.0: Challenges and Opportunities for the Future of iCampuses

In our exciting world of pervasive computing and always-available mobile internet, meeting the educational needs of students has seen a growing trend toward collaborative electronic and mobile learning systems that build on the vision of Web 2.0. However, other trends relevant to modern students must not be ignored, including data freedom, brokerage and interconnectivity. Such factors are associated with the Internet of Things and the vision for Web 3.0, and so include the needs for greater consideration of data context and educational personalization so important to the future of campus-based, distance and vocational study. Therefore, future education can be expected to require a deeper technological connection between students and learning environments, in a manner requiring significant use of sensors, mobile devices, cloud computing and rich-media visualization. This paper considers the challenges associated with adopting such a futuristic concept as a means of enriching learning materials and environments within a university context. It will be concluded that much of the technology required to embrace the vision of Web 3.0 in education already exists, but that further research in key areas is required for the concept to achieve its full potential.

Animating pervasive computing

It is difficult to present new, complex ideas in ways that are informative and interesting in situations where a vast amount of information has to be delivered and understood quickly. Given short attention spans, boredom thresholds and limited capacity to assimilate new information, it is essential to use an effective conveyor of the message, which presents the content in a clear and unambiguous manner whilst helping the receiver to remain alert and focused and integrate the information with existing knowledge. This paper introduces a collaboration between computer scientists and artists to develop an effective, animation based information communication tool, in the form of an application scenario, to be used for communicating to students (and wider audiences) dry technical aspects of Pervasive Computing.

Ten Simple Rules for Digital Data Storage

Data is the central currency of science, but the nature of scientific data has changed dramatically with the rapid pace of technology. This change has led to the development of a wide variety of data formats, dataset sizes, data complexity, data use cases, and data sharing practices. Improvements in high throughput DNA sequencing, sustained institutional support for large sensor networks, and sky surveys with large-format digital cameras have created massive quantities of data. At the same time, the combination of increasingly diverse research teams and data aggregation in portals (e.g. for biodiversity data, GBIF or iDigBio) necessitates increased coordination among data collectors and institutions. As a consequence, “data” can now mean anything from petabytes of information stored in professionally-maintained databases, through spreadsheets on a single computer, to hand-written tables in lab notebooks on shelves. All remain important, but data curation practices must continue to keep pace with the changes brought about by new forms and practices of data collection and storage.

Algorithmic construction of optimal and load balanced clusters in Wireless Sensor Networks

This paper proposes a clustering algorithm - Balanced Minimum Radius Clustering (BMRC) - for use in large scale, distributed Wireless Sensor Networks (WSN). Cluster balancing is an intractable problem to solve in a distributed manner, and distribution is important, by reason of both avoiding specialised node vulnerability and minimising message overhead. The BMRC algorithm described here distributes several of the cluster balancing functions to the cluster-heads. In proposing this algorithm, several tentative claims have been made for it, namely that it is suitable for arbitrary number of cluster heads; that it specifies a way to elect cluster heads and use them to create the local models; that it accomplishes optimal balanced clusters in distributed manner; that it is scalable and it uses the number-of-hops as a clustering parameter; that it is energy efficient. These claims were studied and verified by simulation.

Complex query processing in wireless sensor networks

Greater availability and affordability of wireless technology has led to an increase in the number of wireless sensor network (WSN) applications where sense data is collected at a central user point, commonly outside the network (geographically and topologically) for processing. Resource constraints on nodes in the network coupled with considerable redundancy in the data generated mean that applications have to be developed with an eye to maximising energy efficiency in order to extend network life. In-network computation, and in particular in-network information extraction from data, has been promoted as a technique for achieving this aim. In-network processing, however, has been limited to systems where at most, simple aggregate queries are evaluated, the results of which are communicated to the outside world. There is currently little research into how the idea of in-network processing can be extended and implemented to allow more complex queries to be resolved within the network. This paper examines key applicative query-based approaches that utilise in-network processing for query resolution, identifies their strengths and limitations and puts forward ideas for facilitating in-network complex query processing in WSNs. Finally, preliminary results of experiments, where in-network attribute-based logical abstractions are used for processing complex queries, are presented.