A multi-modal event detection system for river and coastal marine monitoring applications

Abstract—This work is investigating the use of a multi-modal sensor network where visual sensors such as cameras and satellite imagers, along with context information can be used to complement and enhance the usefulness of a traditional in-situ sensor network in measuring and tracking some feature of a river or coastal location. This paper focuses on our work in relation to the use of an off the shelf camera as part of a multi-modal sensor network for monitoring a river environment. It outlines our results in relation to the estimation of water level using a visual sensor. It also outlines the benefits of a multi-modal sensor network for marine environmental monitoring and how this can lead to a smarter, more efficient sensing network

Trust and reputation in multi-modal sensor networks for marine environmental monitoring

Greater temporal and spatial sampling allows environmental processes and the well- being of our waterways to be monitored and characterised from previously unobtainable perspectives. It allows us to create models, make predictions and better manage our environments. New technologies are emerging in order to enable remote autonomous sensing of our water systems and subsequently meet the demands for high temporal and spatial monitoring. In particular, advances in communication and sensor technology has provided a catalyst for progress in remote monitoring of our water systems. However despite continuous improvements there are limitations with the use of this technology in marine environmental monitoring applications. We summarise these limitations in terms of scalability and reliability. In order to address these two main issues, our research proposes that environmental monitoring applications would strongly benefit from the use of a multi-modal sensor network utilising visual sensors, modelled outputs and context information alongside the more conventional in-situ wireless sensor networks. However each of these addi- tional data streams are unreliable. Hence we adapt a trust and reputation model for optimising their use to the network. For our research we use two test sites - the River Lee, Cork and Galway Bay each with a diverse range of multi-modal data sources. Firstly we investigate the coordination of multiple heterogenous information sources to allow more efficient operation of the more sophisticated in-situ analytical instrument in the network, to render the deployment of such devices more scalable. Secondly we address the issue of reliability. We investigate the ability of a multi-modal network to compensate for failure of in-situ nodes in the network, where there is no redundant identical node in the network to replace its operation. We adapt a model from the literature for dealing with the unreliability associated with each of the alternative sensor streams in order to monitor their behaviour over time and choose the most reliable output at a particular point in time in the network. We find that each of the alternative data streams demonstrates themselves to be useful tools in the network. The addition of the use of the trust and reputation model reflects their behaviour over time and demonstrates itself as a useful tool in optimising their use in the network

Integrating multiple sensor modalities for environmental monitoring of marine locations

In this paper we present preliminary work on integrating visual sensing with the more traditional sensing modalities for marine locations. We have deployed visual sensing at one of the Smart Coast WSN sites in Ireland and have built a software platform for gathering and synchronizing all sensed data. We describe how the analysis of a range of different sensor modalities can reinforce readings from a given noisy, unreliable sensor

Investigation into the use of satellite remote sensing data products as part of a multi-modal marine environmental monitoring network

In this paper it is investigated how conventional in-situ sensor networks can be complemented by the satellite data streams available through numerous platforms orbiting the earth and the combined analyses products available through services such as MyOcean. Despite the numerous benefits associated with the use of satellite remote sensing data products, there are a number of limitations with their use in coastal zones. Here the ability of these data sources to provide contextual awareness, redundancy and increased efficiency to an in-situ sensor network is investigated. The potential use of a variety of chlorophyll and SST data products as additional data sources in the SmartBay monitoring network in Galway Bay, Ireland is analysed. The ultimate goal is to investigate the ability of these products to create a smarter marine monitoring network with increased efficiency. Overall it was found that while care needs to be taken in choosing these products, there was extremely promising performance from a number of these products that would be suitable in the context of a number of applications especially in relation to SST. It was more difficult to come to conclusive results for the chlorophyll analysis

Measuring Volatility in Dairy Commodity Prices

The policy environment facing the EU dairy industry continues to undergo considerable change under WTO and CAP reform. Movement away from supply management by the EU and a more liberal global agricultural trading system will involve greater price volatility for dairy commodities. It is anticipated that EU dairy prices will more closely align with world prices. World prices are both lower and more volatile than EU prices and it is further assumed that this increased volatility will be transmitted to EU prices. Price volatility is a concern for a number of reasons as it adds challenges for business planning, debt repayment, and, in some cases, solvency. Representative EU and world butter and SMP (Skim Milk Powder) prices are considered and using the ARMA and GARCH framework their volatility is quantified.Price Volatility, ARMA, GARCH, Butter, SMP, Dairy Policy, Agricultural and Food Policy, Food Consumption/Nutrition/Food Safety,

Managing Price Risk in a Changing Policy Environment: The Case of the EU Dairy Industry

The EU dairy industry faces an unprecedented level of change. The anticipated removal of milk quotas and the move to a less restricted global trade environment will provide the industry with both opportunities and challenges. The primary challenge will be the need for the industry to deal with more volatile prices. Active management of the risks associated with these more volatile prices will help to place the industry in a more competitive position. However this will require the industry and policy makers to embrace a new set of tools. For example the US dairy industry has been much more active in the management of risk and lessons from their experience provide a valuable insight into which tools may be more appropriate in an EU context.Dairy, Risk Management, EU, US,

River water-level estimation using visual sensing

This paper reports our initial work on the extraction of en- vironmental information from images sampled from a camera deployed to monitor a river environment. It demonstrates very promising results for the use of a visual sensor in a smart multi-modal sensor network

Short-term rainfall nowcasting: using rainfall radar imaging

As one of the most useful sources of quantitative precipitation measurement, rainfall radar analysis can be a very useful focus for research into developing methods for rainfall prediction. Because radar can estimate rainfall distribution over a wide range, it is thus very attractive for weather prediction over a large area. Short lead time rainfall prediction is often needed in meteorological and hydrological applications where accurate prediction of rainfall can help with flood relief, with agriculture and with event planning. A system of short-term rainfall prediction over Ireland using rainfall radar image processing is presented in this paper. As the only input, consecutive rainfall radar images are processed to predict the development of rainfall by means of morphological methods and movement extrapolation. The results of a series of experimental evaluations demonstrate the ability and efficiency of using our rainfall radar imaging in a nowcasting system

Image processing for smart browsing of ocean colour data products and subsequent incorporation into a multi-modal sensing framework

Ocean colour is defined as the water hue due to the presence of tiny plants containing the pigment chlorophyll, sediments and coloured dissolved organic material and so water colour can provide valuable information on coastal ecosystems. The ‘Ocean Colour project’ collects data from various satellites (e.g. MERIS, MODIS) and makes this data available online. One method of searching the Ocean Colour project data is to visually browse level 1 and level 2 data. Users can search via location (regions), time and data type. They are presented with images which cover chlorophyll, quasi-true colour and sea surface temperature (11 μ) and links to the source data. However it is often preferable for users to search such a complex and large dataset by event and analyse the distribution of colour in an image before examination of the source data. This will allow users to browse and search ocean colour data more efficiently and to include this information more seamlessly into a framework that incorporates sensor information from a variety of modalities. This paper presents a system for more efficient management and analysis of ocean colour data and suggests how this information can be incorporated into a multi-modal sensing framework for a smarter, more adaptive environmental sensor network

Image processing for smarter browsing of ocean color data products: investigating algal blooms

Remote sensing technology continues to play a significant role in the understanding of our environment and the investigation of the Earth. Ocean color is the water hue due to the presence of tiny plants containing the pigment chlorophyll, sediments, and colored dissolved organic material and so can provide valuable information on coastal ecosystems. We propose to make the browsing of Ocean Color data more efficient for users by using image processing techniques to extract useful information which can be accessible through browser searching. Image processing is applied to chlorophyll and sea surface temperature images. The automatic image processing of the visual level 1 and level 2 data allow us to investigate the occurrence of algal blooms. Images with colors in a certain range (red, orange etc.) are used to address possible algal blooms and allow us to examine the seasonal variation of algal blooms in Europe (around Ireland and in the Baltic Sea). Yearly seasonal variation of algal blooms in Europe based on image processing for smarting browsing of Ocean Color are presented