Great Barrier Reef Project

Two sub projects under the Great Barrier Reef Project are:

1. Anomaly detection in wireless sensor networks.

2. SensorMap for The Great Barrier Reef.

1. Anomaly Detection in Wireless Sensor Networks:

        The Great Barrier Reef (GBR) consists of 3200 coral reefs extended over 280,000km² [3] (see Figure 1). Understanding the patterns of thermal stress and other environmental parameters is essential for monitoring the health of the coral reefs (eg., refer [10] for environmental change and impacts on GBR). The health of the coral reefs can be affected by cold water intrusions, hot water intrusions, coral calcification, ocean acidification, coral-algae phase shifts and the spread of coral diseases due to temperature increases. For example, a temperature rise of about 2-3 degrees Celsius over the normal maximum summer temperature can kill corals [12]. In order to monitor the health of the coral reefs, sea temperatures need to be measured in fine spatial resolutions at various depths. Traditional methods of using satellite images can only reveal water surface temperature distributions at coarse spatial resolutions such as 1km². This resolution does not provide sufficient detail to investigate the cause of coral bleaching or coral growth events in the GBR, such as 2002 coral bleaching event [3, 11]. Measurements at small spatial scales and at various depths are required in order to enable an in-depth analysis of the implications and causes of these bleaching events.

     

Figure 1: Great Barrier Reef [3].                 Figure 2: Sensor nodes used in Nelly Bay, Magnetic Island [9]. 

Another important factor in the analysis of the reef is the abundance of a sea water organism called plankton, which plays an important role in the GBR food chain [1]. Understanding plankton production recently became popular due to its ability to recycle CO₂ and therefore its potential role in global climate change. The productivity of plankton in the GBR is influenced by the nutrient rich cold water intrusions that originate in the Coral Sea and upwell on the reef. Monitoring high frequency sea temperature changes due to daily tides and upwelling in near real-time enables the study of how the changing sea temperature affects the abundance of plankton [1]. Further more, nutrient concentrations like nitrate, phosphate and silicate are strongly correlated with water temperatures [2].

The proposed coral reef monitoring system using wireless sensor networks in the Davis Reef, North Queensland involves the placement of a number of environmental sensors that measure temperature, salinity, light and oxygen [3]. Current infrastructure installed in this reef site includes a sensor gateway, which provides the aggregation point for sensor data, a hybrid power supply utilising solar cells and a battery, a high speed microwave link, which operates using a 'humidity duct' at a data rate of 10Mbps, and cameras [4, 7, 8]. A wireless sensor network test bed is currently being installed in Nelly Bay, Magnetic Island [9] (refer to figure 2). The sensor network consists of two sensor arrays that comprise four moorings, each having seven temperature sensors vertically positioned below the ocean surface 2m apart [5]. An accelerometer sensor is also being deployed at each node to measure the wave tidal frequency.

The sensor measurements collected by such sensor network deployments can become contaminated with errors (either fully or partially) due to either loss of calibration of the sensing elements or faulty sensor nodes (e.g., see graph in [1]). Such errors are highly prevalent in sensors and electronic equipment deployed in the harsh marine environment [1]. This contamination may gradually accumulate over a period of time (gradual drift), or occur in one-off transients. Such errors need to be detected at their source in real time and corrected, in order to collect reliable data from the sensor network deployments. Further more, there is a need to automatically detect natural events of interest in the monitored environment, such as cold water intrusions. Once these events occur, we need the ability to automatically adjust the sampling frequency and type of measurements collected in response to the event of interest.

The general problem of detecting interesting changes from the normal observed behavior in sensor measurements is known as anomaly detection. An anomaly can be caused by an unusual change in the phenomena (e.g., water temperature or nutrient concentration), or by faulty sensors: that cause incorrect measurements, or even by malicious events such as security attacks in sensor networks [6]. Important challenges for the management of sensor networks in complex environments such as the GBR are the detection, inference, reporting and correcting of anomalies. Centralised solutions to anomaly detection, which involve collection of all data from sensors to a centralised node for processing, can be communication intensive and thus very energy inefficient. An alternative approach for anomaly detection in sensor networks is to use in-network processing in order to prolong the lifetime of the resource constrained wireless sensor networks. Our research into distributed anomaly detection in wireless sensor networks addresses the above challenges in order to provide a reliable, energy efficient and self-correcting wireless sensor network for use in small to large scale deployments.

People Involved: Mr. Sutharshan Rajasegarar, Dr. Christopher Leckie, A/Prof. Marimuthu Palaniswami, Prof. James C Bezdek, Dr. Yee Wei Law and Dr. Jayavardhana Gubbi.

Related Publications:

* Sutharshan Rajasegarar, Christopher Leckie and Marimuthu Palaniswami,"Anomaly Detection in Wireless Sensor Networks". To appear in IEEE Wireless Communication Magazine, (accepted 16 April 2007) 14 pages, ISSN 1536-1284., 2008.

* Sutharshan Rajasegarar, Christopher Leckie, Marimuthu Palaniswami, and James C. Bezdek, "Quarter Sphere Based Distributed Anomaly Detection in Wireless Sensor Networks", in the IEEE International Conference on Communications (IEEE ICC 2007), pp.3864-3869, (Glasgow, Scotland), June 2007.

* Sutharshan Rajasegarar, James C. Bezdek, Christopher Leckie and Marimuthu Palaniswami, "Analysis of Anomalies in IBRL Data from a Wireless Sensor Network Deployment", in the International Conference on Sensor Technologies and Applications (SENSORCOMM 2007), pp 158-163, (Valencia, Spain), Oct 2007.

* Sutharshan Rajasegarar, Christopher Leckie, Marimuthu Palaniswami, and James C. Bezdek, "Distributed Anomaly Detection in Wireless Sensor Networks", in the Tenth IEEE International Conference on Communications Systems (IEEE ICCS 2006), (Singapore), Oct 2006.

* Maen Takruri, Sutharshan Rajasegarar, Subhash Challa, Christopher Leckie and Marimuthu Palaniswami, "Online Drift Correction in Wireless Sensor Networks Using Spatio-Temporal Modeling", Accepted for The 11th International Conference on Information Fusion (Fusion 2008), (Cologne, Germany), June-July 2008. To appear...
 

References:

[1] Olga Bondarenko, Stuart Kininmonth and Michael Kingsford, "Underwater Sensor Networks, Oceanography and Plankton Assemblages", in the Proc. of International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2007), pp 657-662, (Melbourne, Australia), 2007.

[2] M.J. Furnas and A.W. Mitchell, Nutrient inputs into the central Great Barrier Reef (Australia) from subsurface intrusions of Coral Sea waters: a two-dimensional displacement model. In Continental Shelf Research, 1996. 16(9): pp. 1127-1148.

[3] Stuart Kininmonth, Scott Bainbridgea, Ian Atkinsonc, Eric Gilla, Laure Barrald and Romain Vidaude (2004), "Sensor Networking the Great Barrier Reef", Spatial Sciences Qld Journal, Spring 2004, pp34-38.

[4] Cameron Huddlestone-Holmes, Gilles Gigan, Graham Woods, Adam Ruxton, Ian Atkinson, and Stuart Kininmonth, Infrastructure for a Sensor Network on Davies Reef, Great Barrier Reef, in the Proc. of International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2007) , pp 657-662, (Melbourne, Australia), 2007.

[5] Olga Bondarenko, Stuart Kininmonth and Michael Kingsford, Coral Reef Sensor Network Deployment for Collecting Real Time 3-D Temperature Data with Correlation to Plankton Assemblages, in the Proc. of International Conference on Sensor Technologies and Applications (SENSORCOMM 2007), pp 204-209, (Valencia, Spain), 2007

[6] A Perrig, J Stankovic, and D Wagner, Security in wireless sensor networks, in Wireless Personal Communications, vol 37, no 3-4, 2006.

[7] http://www.reefgrid.org/sensors/

[8] http://www.qcif.edu.au/industry/ReefGrid.htm

[9] http://www.reeffutures.org/sensornet/display.cfm

[10] http://www.aims.gov.au/pages/research/research-teams/rt-environmental-change-and-impacts.html

[11] http://www.reeffutures.org/topics/bleach/event.cfm

[12] http://www.reeffutures.org/topics/bleach/temp.cfm

 

2. SensorMap for The Great Barrier Reef:

     Recent developments in technology together with widely observed climate change phenomena have revealed coral reef ecosystems as critical areas greatly susceptible to impact of global climate variations as well as other man-made influences, but also as early indicators of such events. The need to understand and protect such delicate ecosystems has created an urgent demand for the sensor networks technologies to be deployed in order to perform essential environmental monitoring and information collection. This data can then be analysed by higher level systems such as a semantic web to eventually provide predictive information on destructive events such as coral bleaching. 

The Australian Institute of Marine Science (AIMS) has a number of autonomous weather stations which collect environmental data on a half-hourly basis. This information is automatically quality checked and stored in the data centre before being delivered to web based visualisation tools. Collecting real-time data at appropriate temporal and spatial scales is critical to understanding complex marine processes and the emerging generation of 'smart' sensors presents new opportunities for automated intelligent monitoring of marine systems. This program of scientific research and engineering development, requiring extensive cross disciplinary collaboration, will convert the existing weather stations into a true sensor network.

Autonomous smart sensor based systems provide one way to obtain this data from the scale of oceans to the scale of individual corals. The development of a suite of technologies to deliver a robust, simple but effective technology platform to support sensor webs has become a high priority for a number of marine and environmental agencies. SensorMap project will be conducted in conjunction with programs that are utilising the existing AIMS weather station infra-structure as its base and extending this using a number of technologies and a number of partners including the University of Melbourne (Australia), James Cook University (Australia), the University of Twente (The Netherlands) and Ambient Systems (The Netherlands). Some of the technical obstacles are similar for any marine based monitoring system and mainly revolve around fouling, powering equipment and the general problems of maintaining equipment in a remote and hostile environment. There are also a number of new sensor network challenges that need to be addressed. This includes: the implementation of high-capacity communication links to remote areas; the storage and manipulation of the large volumes of data generated (including video); the integration of the data into modelling and visualisation systems and the ability to manage and maintain a system that is inherently more complex than the simple passive systems deployed currently.

The SensorMap on The Great Barrier Reef project will provide a valuable interface between the sensors and higher level objectives of multidisciplinary research teams around the world, from sensor networks researchers to marine biologists. Utilising the core infrastructure associated with a sensor network deployment currently in progress on the Great Barrier Reef, this project will aid in the collection and dissemination of a diverse range of unique sensor data.

People Involved:  A/Prof Marimuthu Palaniswami, A/Prof Ian Atkinson, Mr. Stuart Kininmonth, Dr. Slaven Marusic, Mr. Sutharshan Rajasegarar, Dr Jayavardhana Gubbi.

 

NOTE:  Reef Data from Davis reef and some other existing weather stations in Australian reef sites are published in Microsoft SensorMap on February 2008. This is a ISSNIP-AIMS collaboration to publish reef data using Microsoft SensorMap Project. 

Real data can be viewed on the SensorMap from the following link:

http://atom.research.microsoft.com/sensormap/?la=-25.562265014427517&lo=133.41796875&zl=5&ms=r&st=&tf=1,1,1,1,1&fp=-37.774666687323204,144.946596622467.

More detailed view of the sensor data can be viewed from http://www.aims.gov.au/pages/facilities/weather-stations/davies-data.html.

 

References:

[1] Stuart Kininmonth, Scott Bainbridgea, Ian Atkinsonc ,Eric Gilla, Laure Barrald and Romain Vidaude (2004), Sensor Networking the Great Barrier Reef, Spatial Sciences Qld journal, Spring 2004, p34-38

[2] http://www.aims.gov.au/

[3] http://www.coralreefeon.org/

[4] http://atom.research.microsoft.com/sensormap/