Estimating Fire Weather Indices via Semantic Reasoning over Wireless Sensor Network Data Streams

Wildfires are frequent, devastating events in Australia that regularly cause significant loss of life and widespread property damage. Fire weather indices are a widely-adopted method for measuring fire danger and they play a significant role in issuing bushfire warnings and in anticipating demand for bushfire management resources. Existing systems that calculate fire weather indices are limited due to low spatial and temporal resolution. Localized wireless sensor networks, on the other hand, gather continuous sensor data measuring variables such as air temperature, relative humidity, rainfall and wind speed at high resolutions. However, using wireless sensor networks to estimate fire weather indices is a challenge due to data quality issues, lack of standard data formats and lack of agreement on thresholds and methods for calculating fire weather indices. Within the scope of this paper, we propose a standardized approach to calculating Fire Weather Indices (a.k.a. fire danger ratings) and overcome a number of the challenges by applying Semantic Web Technologies to the processing of data streams from a wireless sensor network deployed in the Springbrook region of South East Queensland. This paper describes the underlying ontologies, the semantic reasoning and the Semantic Fire Weather Index (SFWI) system that we have developed to enable domain experts to specify and adapt rules for calculating Fire Weather Indices. We also describe the Web-based mapping interface that we have developed, that enables users to improve their understanding of how fire weather indices vary over time within a particular region.Finally, we discuss our evaluation results that indicate that the proposed system outperforms state-of-the-art techniques in terms of accuracy, precision and query performance.Comment: 20pages, 12 figure

Non-cubic occupied voxel lists for robot maps

An alternative to the conventional quantization for occupied voxel lists in both 2D and 3D is presented. The performance metrics of the hexagonal lattice in 2D and the face centred and body centred cubic lattices in 3D are investigated and compared to their square and cubic counterparts. It is found that quantization to alternative lattices yields some improvements. Ultimately, the D3 or face centred cubic lattice is highlighted for its lower quantization error, lower rotation variability and higher order rotational symmetry. It has three times less occupied voxel count pose variability than a standard cubic occupied voxel list. These improvements have implications for SLAM and path planning

Semantic-based detection of segment outliers and unusual events for wireless sensor networks

Environmental scientists have increasingly been deploying wireless sensor networks to capture valuable data that measures and records precise information about our environment. One of the major challenges associated with wireless sensor networks is the quality of the data – and more specifically the detection of segment outliers and unusual events. Most previous research has focused on detecting outliers that are errors that are caused by unreliable sensors and sensor nodes. However, there is an urgent need for the development of new tools capable of identifying, tagging and visualizing erroneous segment outliers and unusual events from sensor data streams. In this paper, we present a SOUE-Detector (Segment Outlier and Unusual Event-Detector) system for wireless sensor networks that combines statistical analyses using Dynamic Time Warping (DTW) with domain expert knowledge (captured via an ontology and semantic inferencing rules). The resulting Web portal enables scientist to efficiently search across a collection of wireless sensor data streams and identify, retrieve and display segment outliers (both erroneous and genuine) within the data streams. In this paper, we firstly describe the detection algorithms, the implementation details and the functionality of the SOUE-Detector system. Secondly we evaluate our approach using data that comprises sensor observations collected from a sensor network deployed in the Springbrook National Park in Queensland, Australia. The experimental results show that the SOUE-Detector can efficiently detect segment outliers and unusual events with high levels of precision and recall

Challenges for RF two-way time-of-flight ranging in Wireless Sensor Networks

In applications where a priori determination of location is infeasible, node localization schemes are desirable, which allow the node to estimate its location during network operation. The majority of these schemes are based on ranging between node pairs, which should ideally be performed without adding cost or size to the sensor node. Two-way time-of-flight schemes can fulfill this desire, by utilizing the measurement of the time-of-flight of electromagnetic waves to determine the distance between two sensor nodes. In this paper, we present the implementation and analysis of such a ranging scheme. Because a small error in time measurement can result in a large distance estimation error, the focus of this work lies on the determination and analysis of influencing factors, which limit the accuracy of round-trip-time measurements. We analyze two main contributing factors to the accuracy of the ranging scheme, namely the radio transceiver clock quantization and the link quality during round-trip-time measurement. These effects and their impact on the overall ranging error have been investigated by means of simulation and experimentation. Initial ranging errors as large as 24 m RMS were observed, which could be reduced to errors between 5 and 8 m RMS by utilizing compensation techniques

A Bayesian method for probable surface reconstruction and decimation

We present a Bayesian technique for the reconstruction and subsequent decimation of 3D surface models from noisy sensor data. The method uses oriented probabilistic models of the measurement noise, and combines them with feature-enhancing prior probabilities over 3D surfaces. When applied to surface reconstruction, the method simultaneously smooths noisy regions while enhancing features, such as corners. When applied to surface decimation, it finds models that closely approximate the original mesh when rendered. The method is applied in the context of computer animation, where it finds decimations that minimize the visual error even under nonrigid deformations

Continous monitoring of reservoir water quality: The Wivenhoe Project

The Lake Wivenhoe Integrated Wireless Sensor Network is conceptually similar to traditional SCADA monitoring and control approaches. However, it is applied in an open system using wireless devices to monitor processes that affect water quality at both a high spatial and temporal frequency. This monitoring assists scientists to better understand drivers of key processes that influence water quality and provide the operators with an early warning system if below standard water enters the reservoir. Both of these aspects improve the safety and efficient delivery of drinking water to the end users

Continuous monitoring of reservoir water quality: The Wivenhoe Project

The Lake Wivenhoe Integrated Wireless Sensor Network is conceptually similar to traditional SCADA monitoring and control approaches. However, it is applied in an open system using wireless devices to monitor processes that affect water quality at both a high spatial and temporal frequency. This monitoring assists scientists to better understand drivers of key processes that influence water quality and provide the operators with an early warning system if below standard water enters the reservoir. Both of these aspects improve the safety and efficient delivery of drinking water to the end users

A DTLS based end-to-end security architecture for the Internet of Things with two-way authentication

In this paper, we introduce the first fully implemented two way authentication security scheme for the Internet of Things (IoT) based on existing Internet standards, especially the Datagram Transport Layer Security (DTLS) protocol. The proposed security scheme is based on the most widely used public key cryptography (RSA), and works on top of standard low power communication stacks. We believe that by relying on an established standard, existing implementations, engineering techniques and security infrastructure can be reused, which enables easy security uptake. We present an implemented system architecture for the proposed scheme based on a low-power hardware platform suitable for the IoT. We further demonstrate its feasibility (low overheads and high interoperability) through extensive evaluation