Center for Sensor Web Technologies

Dublin, Ireland

Center for Sensor Web Technologies

Dublin, Ireland
Time filter
Source Type

Champin P.-A.,University Claude Bernard Lyon 1 | Champin P.-A.,Center for Sensor Web Technologies | Briggs P.,Center for Sensor Web Technologies | Coyle M.,Center for Sensor Web Technologies | Smyth B.,Center for Sensor Web Technologies
Knowledge-Based Systems | Year: 2010

The so-called Social Web has helped to change the very nature of the Internet by emphasising the role of our online experiences as new forms of content and service knowledge. In this paper we describe an approach to improving mainstream Web search by harnessing the search experiences of groups of like-minded searchers. We focus on the HeyStaks system ( and look in particular at the experiential knowledge that drives its search recommendations. Specifically we describe how this knowledge can be noisy, and we describe and evaluate a recommendation technique for coping with this noise and discuss how it may be incorporated into HeyStaks as a useful feature. © 2009 Elsevier B.V. All rights reserved.

Gui H.,Wuhan University | Roantree M.,Dublin City University | Roantree M.,Center for Sensor Web Technologies
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

XML has become a widely used standard for data representation, distribution and sharing. The concept of the Sensor Web has led to web generated sensor data in many diverse applications where delivery of the sensed data takes place using the Web. In order to obtain useful knowledge from XML sensor data, data warehouse and OLAP applications aimed at providing support for decision making for operational data must be developed. In this paper, we present a pipeline design based OLAP data cube construction framework designated for real time web generated sensor data, transforming sensor data into XML streams conforming to an underlying data warehouse logical model, which constructs corresponding data cubes. As part of this work, we discuss how our cube construction and acceleration strategy improves the efficiency in managing large volumes of XML data. © Springer-Verlag 2013.

Campana T.,Center for Sensor Web Technologies | O'Hare G.M.P.,University College Dublin
SENSORNETS 2013 - Proceedings of the 2nd International Conference on Sensor Networks | Year: 2013

A diverse range of faults and errors can occur within a wireless sensor network (WSN), and it is difficult to predict and classify them, especially post-deployment within the environment. Current monitoring and debugging techniques prove deficient for systems which contain bugs characteristic of both distributed and embedded systems. The challenge that faces researchers is how to comprehensively address network, node and data level anomalies within WSNs through the creation, collection and aggregation of local state information while minimizing additional network traffic and node energy expenditure. This paper introduces Intellectus which seeks to develop sensor motes that are both self and environment aware. The sensor node relies on local information in order to monitor itself and that of its neighborhood, by adding a learning approach based upon perceived events and their associated frequency.

Gui H.,Wuhan University | Roantree M.,Center for Sensor Web Technologies
Procedia Computer Science | Year: 2012

Ambient systems generate large volumes of data for many of their application areas with XML often the format for data exchange. As a result, large scale ambient systems such as smart cities require some form of optimization before different components can merge their data streams. In data warehousing, the cube structure is often used for optimizing the analytics process with more recent structures such as dwarf, providing new orders of magnitude in terms of optimizing data extraction. However, these systems were developed for relational data and as a result, we now present the development of an XML dwarf to manage ambient systems generating XML data. © 2012 Published by Elsevier Ltd.

Esparza S.G.,Center for Sensor Web Technologies | O'Mahony M.P.,Center for Sensor Web Technologies | Smyth B.,Center for Sensor Web Technologies
Res. and Dev. in Intelligent Syst. XXIX: Incorporating Applications and Innovations in Intel. Sys. XX - AI 2012, 32nd SGAI Int. Conf. on Innovative Techniques and Applications of Artificial Intel. | Year: 2012

There is no doubting the incredible impact of Twitter on how we communicate, access and share information online. Currently users can follow other users or hashtags in order to benefit from a stream of data from people they trust or on topics that matter to them. However at the moment the following granularity of Twitter means that users cannot limit their information streams to a set of topics by a given user. Thus, even the most carefully curated information streams can quickly become polluted with extraneous content. In this paper we describe our initial steps to improve this situation by proposing a profiling approach that can be used for information filtering purposes as well as recommendation purposes. First, we demonstrate that it is feasible to automatically profile the interests of users by using machine learning techniques to classify the pages that they share via their tweets. We then go on to describe how this profiling mechanism can be used to organise and filter Twitter information streams. In particular we present a system that provides for a more fine-grained way to follow users on specific topics and thereby refine the standard Twitter timeline based on a user's core topical interests. © Springer-Verlag London 2012.

Zhang Z.,Center for Sensor Web Technologies | Gurrin C.,Center for Sensor Web Technologies | Guo J.,Center for Sensor Web Technologies
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

In this paper, we describe an interactive video browsing system based on a graph of linked video objects. The system automatically organizes unstructured video archives by exploiting visual content similarity between objects in the videos. By generating a video link graph, the system can conceptually groups the videos that contains same objects together for searching and browsing. Both the chosen measures of video object similarity and the video data mining technologies are discussed here and included in the related software demonstrator. In addition, the software offers a query-by-image-example video search capability to jump into the video graph at a certain point to begin browsing the archive. © Springer-Verlag 2013.

Campbell A.G.,Center for Sensor Web Technologies | Gorgu L.,Center for Sensor Web Technologies | Kroon B.,Center for Sensor Web Technologies | Lillis D.,Center for Sensor Web Technologies | And 2 more authors.
2013 IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2013 | Year: 2013

With the increasing availability of sensors within smartphones and within the world at large, a question arises about how this sensor data can be leveraged by Augmented Reality (AR) devices. AR devices have traditionally been limited by the capability of a given device's unique set of sensors. Connecting sensors from multiple devices using a Sensor Web could address this problem. Through leveraging this SensorWeb existing AR environments could be improved and new scenarios made possible, with devices that previously could not have being used as part of an AR environment. This paper proposes the use of SIXTH: a middleware designed to generate a Sensor Web, which allows a device to leverage heterogeneous external sensors within its environment to help facilitate the creation of richer AR experiences. This paper will present a worst case scenario, in which the device chosen will be a see-through, Android-based Head Mounted Display that has no access to sensors. This device is transformed into an AR device through the creation of a Sensor Web allowing it to sense its environment facilitated through the use of SIXTH. © 2013 IEEE.

Wang W.,Tyndall National Institute | Wang N.,Tyndall National Institute | Jafer E.,Tyndall National Institute | Hayes M.,Tyndall National Institute | And 2 more authors.
2010 2nd Conference on Environmental Science and Information Application Technology, ESIAT 2010 | Year: 2010

Wireless sensor network technology emerged in recent years with numerous potential applications. The building environment and energy monitoring (BEEM) is among the most important ones. The design of such smart wireless sensing system is presented in this paper. The proposed system consists of low power Tyndall wireless sensor node hardware with light energy harvesting featured power supply and energy management system for long-term deployment. Energy consumption, light level, temperature and humidity parameters are measured and transmitted via a 2.4GHz Zigbee wireless network. Evaluations of the system are conducted in a local office building with a total of 62 nodes operating with varying functions. The evaluation results of the system including the measured energy and environmental data are presented. The evaluation results show that this design is world's first known indoor light energy harvesting powered BEEM system. ©2010 IEEE.

Curto V.F.,Center for Sensor Web Technologies | Curto V.F.,Dublin City University | Fay C.,Center for Sensor Web Technologies | Coyle S.,Center for Sensor Web Technologies | And 14 more authors.
Sensors and Actuators, B: Chemical | Year: 2012

This work presents the fabrication, characterisation and the performance of a wearable, robust, flexible and disposable chemical barcode device based on a micro-fluidic platform that incorporates ionic liquid polymer gels (ionogels). The device has been applied to the monitoring of the pH of sweat in real time during an exercise period. The device is an ideal wearable sensor for measuring the pH of sweat since it does not contain any electronic part for fluidic handle or pH detection and because it can be directly incorporated into clothing, head- or wristbands, which are in continuous contact with the skin. In addition, due to the micro-fluidic structure, fresh sweat is continuously passing through the sensing area providing the capability to perform continuous real time analysis. The approach presented here ensures immediate feedback regarding sweat composition. Sweat analysis is attractive for monitoring purposes as it can provide physiological information directly relevant to the health and performance of the wearer without the need for an invasive sampling approach. © 2012 Elsevier B.V. All rights reserved.

Richter C.,Dublin City University | Richter C.,Center for Sensor Web Technologies | O'Connor N.E.,Center for Sensor Web Technologies | Marshall B.,Dublin City University | Moran K.,Dublin City University
Journal of Applied Biomechanics | Year: 2014

The aim of this study is to propose a novel data analysis approach, an analysis of characterizing phases (ACP), that detects and examines phases of variance within a sample of curves utilizing the time, magnitude, and magnitude-time domains; and to compare the findings of ACP to discrete point analysis in identifying performance-related factors in vertical jumps. Twenty-five vertical jumps were analyzed. Discrete point analysis identified the initial-to-maximum rate of force development (P = .006) and the time from initial-to-maximum force (P = .047) as performance-related factors. However, due to intersubject variability in the shape of the force curves (ie, non-, uni- and bimodal nature), these variables were judged to be functionally erroneous. In contrast, ACP identified the ability to apply forces for longer (P < .038), generate higher forces (P < .027), and produce a greater rate of force development (P < .003) as performance-related factors. Analysis of characterizing phases showed advantages over discrete point analysis in identifying performance-related factors because it (i) analyses only related phases, (ii) analyses the whole data set, (iii) can identify performance-related factors that occur solely as a phase, (iv) identifies the specific phase over which differences occur, and (v) analyses the time, magnitude and combined magnitude-time domains. © 2014 Human Kinetics, Inc.

Loading Center for Sensor Web Technologies collaborators
Loading Center for Sensor Web Technologies collaborators