Entity

Time filter

Source Type


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. Source


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. Source


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. Source


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. Source


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. Source

Discover hidden collaborations