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Views from the coalface: chemo-sensors, sensor networks and the semantic sensor web

Views from the coalface: chemo-sensors, sensor networks and the semantic sensor web. Jer Hayes – CLARITY / IBM Dublin, Ireland. Who?.

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Views from the coalface: chemo-sensors, sensor networks and the semantic sensor web

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  1. Views from the coalface:chemo-sensors, sensor networks and the semantic sensor web Jer Hayes – CLARITY / IBM Dublin, Ireland

  2. Who? The Adaptive sensors group (ASG) is the sensor element of the CLARITY: Centre for Sensor Web Technologies, a joint DCU-UCD research partnership funded by Science Foundation Ireland under 07/CE/I1147. CLARITY is a research centre that  focuses on the intersection between two important research areas - Adaptive Sensing and Information Discovery. IBM is a CLARITY industry partner. Various Irish-based centres under the Innovative Environmental Solutions grouping.

  3. ASG Novel sensing…

  4. About me… I currently work for IBM within CLARITY… Remote sensing: sea surface temp. Testing wireless sensor networks at sea Food technology: spoilage sensor

  5. Outline Sensor networks Problems with sensors – bias? Core problems & an example sensor system Intelligence in the network Summary

  6. Sensor networks The semantic sensor web offers the unique opportunity to unify the real and virtual world. We are on the cusp of unifying real-world and virtual world… Large scale sensor-networks will be built around internet-enable devices (in some cases only the base-station may be internet enabled).

  7. Physical sensor bias Biased towards considering sensors to be like thermistors which is understandable as they exhibit almost ideal behaviour: low cost, long-life, very low-power, small form factor, high accuracy and precision, rugged, reliable, etc. Bias colours the expectations of SSW/WSN researchers in that they expect all sensors to conform to this ideal. Sensors aren’t always reliable leaching of active components from sensing membranes, physical damage, lack of selectivity, baseline drift and biofouling (particularly in the marine environment!).

  8. Dealing with raw data streams Given any sensor we can ask - what does this data stream mean ? Generally speaking data streams are not self identifying We require outside information, metadata, to understand the stream. The main driver for the use of metadata so far has been data sharing. Scientists generate large amounts of data and often we wish to share this data with other researchers.

  9. Data sharing… SEACOOS:southeastern Atlantic coastal ocean observatory system It involves 13 universities and institutions NETCDF file format Distributed Oceanographic Data Systems (DODS) Open Source Project for a Network Data Access Protocol (OPeNDAP)

  10. Core problems from our perspective The heterogeneity of data sources and data transport methods that all must neatly fit into the SSW. The quality of the data must be described and understood. Data streams from different sources and modalities (esp. contextual information) which vary across many dimensions, including spatial, temporal, granularity of data, must be integrated. The SSW must be capable of supporting analytics (e.g. decision making) across the SSW nodes.

  11. Phosphate system Component of “SmartCoast” project, which aims to develop a smart water quality monitoring system, to aid compliance with increased monitoring requirements under the Water Framework Directive. Phosphate is a key limiting nutrient in freshwater ecosystems. Eutrophication: A major water quality problem in Ireland and many other countries Elevated nutrient levels lead to excessive growth of algae and aquatic plants Oxygen depletion  fish kills Algal blooms  toxicity in water bodies

  12. Objective and Requirements Develop an autonomous, remotely controlled phosphate sensor capable of monitoring PO43- at appropriate levels at remote locations over long deployments Requirements: Sensitive Stable chemistry Communicate wirelessly Low power Robust & portable Low cost & low maintenance requirements

  13. Principle of Operation Yellow method for phosphate detection Forms vanadomolybdophosphoric acid (yellow) Absorption proportional to phosphate conc. Advantages Excellent reagent stability Fast reaction time (minutes) Microfluidic technology Minimizes reagent consumption, storage requirements and pumping power UV-LED and photodiode Low powered, inexpensive & sensitive optical detection

  14. Current Status Mark II sensor designed to build on the successes and address the limitations of the original. Improvements Lower power, more flexible fluid handling system. More sensitive optical detection system. More reliable and lower powered communications using GSM modem in SMS mode. 2 point calibration protocol. Solar panel for energy harvesting during long deployments. Improved ruggedisation. Yeah, so what? What about the semantic sensor web?!!!

  15. Problems at the coalface… How do we plug this sensor into a sensor network and / or the semantic sensor web? What? Where? When? Who? What is “context” for this sensor? How do we find it and can we trust it? Weather? Other water quality parameters. Data from models? Topology, soil type, land use in river basin district… For other sensors what “context” is could be complex. Can the network control the device properly? Can it change sampling rates etc.? Do we just pass on data or can we control the device? Who is allowed to control it?

  16. Problems at the coalface… We are looking for a “complete picture” – so data streams will come from hardware & software, e.g. modelling. Air quality… Ground based in-situ sensors Remote sensing Models: chemical transport, weather etc. Software sensors (Models/Database & Hardware sensors?

  17. Analysed using IR gas sensor Chemometric program analyses data and decides if concentrations are within threshold limits Gas sample extracted Borehole well If thresholds are exceeded, a message to sent to personnel onsite to investigate further CH4 VOCs CO2 Landfill gas generation

  18. Intelligence? OGC's Sensor Web Enablement (SWE): Where does the landfill site fit in? Where should the analytics take place? How do we know contextual information is accurate? Should “bad data” be released?

  19. Summary… Sensors aren’t as reliable as we’d like to think. Need to account for data quality…. Contextual information is required for the “complete picture”. From a large variety of possible sources…

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