2013-10-10 robust and trusted crowd-sourcing and crowd-tasking in the future internet

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ISESs 2013 Presentation of the challenges we encountered while developing the Mobile Data Acquisition Framework (MDAF => new name is "ubicity") in ENVIROFI project. …

ISESs 2013 Presentation of the challenges we encountered while developing the Mobile Data Acquisition Framework (MDAF => new name is "ubicity") in ENVIROFI project.

Related to "robust and trusted crowd-sourcing and crowd-tasking in the future internet" ISESs paper.

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  • Denis, you were quite secretive on the subject till now , hopefully CRISMA continues and we will have an opportunity to discuss your results ;-)
    Congratulations !

    M.
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  • Two out of three ENVIROFI scenarios are strongly biased towards citizen scientists, mobile crowdsourcing and crowdtasking and local situation awareness.
  • In some senses, humans are „bad sensors“. They are non-standardized, difficult to calibrate, don‘t like the idea of working 24/7, easily bored and their accuracy and sensitivity erratically varies over time. However, they also excell at pattern recognition and interpretation of the results.
    This makes them complementary to hardware sensors and very valuable for some types of applications.
  • Unlike standard monitoring systems, the human sensors (and to a lesser extent also information from user-owned sensors) inevitably deliver conflicting and incomplete information. The Quality assurance of such data often relies on combination of peer review, expert opinions and various indicators.
    In MDAF, all these results can happily co-exist even if they are contradicting each other. The decision „what is the reality?“ is only made at the level of „application specific view“, taking into account the owners interests and trust in various data sources.
    As a result, it is perfectly possible to generate several conflicting „realities“ from the same data set. E.g. a Greanpeace applicaiton will show different reality than a fisherman association applicaiton simply because they make different assumprions concerning the relative importance and trustworthiness of the data.
    The „applicaiton specific view“ has not been fully implemented, but the technology is the same as the one used for the quality assurance part.
  • The architecture shown here goes beyond the ENVIROFI pilots and indicates our ideas what woudl be possible to do with FI-Ware GEs in the future.
  • This demo has been developed within the project scope and brought to working PoC status. We are confident that the concept will work well when we start making „real“ applicaitons. In fact, we (AIT) are using this in CRISMA project now, so the number of available widgets and our know-how steadily rises…
  • Tasking of volunteers and experts is a key to data collection and quality assurance. It is crucial to task the users which are both able and willing to perform this task, while avoiding the information overflow.
    In ENVIROFI, AIT was able to develop a concept and basic technology which will allow us to implement the context- and profile- specific tasking in the future projects.
  • I‘m not sure which is the license for the slides which I „inherited“ here, sorry. I‘m sure that *I* can use them, and I presume the right to re-use them will be granted to anyone who asks. Please contact the respective consortium leaders.

Transcript

  • 1. “ENVIROfying” the Future Internet THE ENVIRONMENTAL OBSERVATION WEB FOR THE CROSS-DOMAIN FI-PPP APPLICATIONS Robust and trusted crowd-sourcing and crowd- tasking in the Future Internet ISESS 2013, Oct. 09-11 2013 Denis Havlik, Maria Egly, Hermann Huber, Peter Kutschera, Markus Falgenhauer, Markus Cizek (all AIT Austrian Institute of Technology GmbH.)
  • 2. Image from: http://favim.com/image/270658/ 2
  • 3. Copyright © ENVIROFI Project Consortium 3 Enviromatics meet Future Internet Future Internet • Networking technology • Infrastructure as a Service • Internet of Things, Content, People INSPIRE, GMES, SISE • Geospatial • Environmental Observations • Model Web, Sensor Web, • Data Fusion, Uncertainty ENVIROFI FI-PPP Environmental Usage Area • FI Requirements • Specific Enablers • Envirofied cross-area Applications
  • 4. ENVIROFI Scenarios 1. Bringing Biodiversity into the Future Internet • Enabled biodiversity surveys with advanced ontologies • Analysis, quality assurance and dissemination of biodiversity data 1. Personal Information System for Air Pollutants, allergens and meteorological conditions • Enhance human to environment interaction • Atmospheric conditions and pollution in “the palm of your hand” 1. Collaborative Usage of Marine Data Assets • Assess needs of key marine user communities • Selection of representative marine use cases for further trial: leisure and tourism, ocean energy devices, aquaculture, oil spill alert Copyright © 2013 ENVIROFI Project Consortium 4
  • 5. Copyright © 2013 Denis Havlik, AIT Austrian Institute of Technology GmbH. 5
  • 6. Challenge: human sensors Copyright © 2013 Denis Havlik, AIT Austrian Institute of Technology GmbH. Illustration by Scoobay (http://www.flickr.com/photos/scoobay/224565711/) 6
  • 7. Motivation matters! Copyright © 2013 Denis Havlik, AIT Austrian Institute of Technology GmbH. 7
  • 8. Challenge: plausibility and QA 8 Image Classifier SE Quality Assessment SE classify & check images Image Archive SE manage images MDAF server mobile acquisition General User leaf images metadata (e.g. geotag) Expert leaf species manual assessment © 2013 ENVIROFI Project Consortium
  • 9. Microlearning helps! Copyright © 2013 Denis Havlik, AIT Austrian Institute of Technology GmbH. 9 Objective Possible approach How to use the application? Tooltips or popup messages on first use (implemented) Training to recognise objects Scavenger hunt for known and tagged objects Learn to avoid misidentifications control questions & feedback A-posteriori feedback Notify user when more info on the object is available (implemented) Classify data & assess users knowledge Generalized re-capcha principle Microlearning in a sense of „learning while doing“ is crucial for quality of information but still not sufficiently taken into account. On TODO list.
  • 10. Observation DB Challenges: no single truth 10 Plausibility/Confidence checks Consensus building Previous situation knowledge Habitat Informatio n Image Recognition Reporters Reputation Observ. on things (independent, conflicting, incomplete) Observations on observations (identification, plausibility, annotation) Application specific views (fusion, meaning uncertainty) Sensor Networks ENVIROFI observations ENVIROFI observations Integrate existing data Integrate existing data USE Copyright © 2013 Denis Havlik, AIT Austrian Institute of Technology GmbH.
  • 11. Challenge: FI-Ware integration 11 „Cloud Edge“ GE: Field-deployment of observations server; (P2P?) information exchange over local WLAN „Cloud Edge“ GE: Field-deployment of observations server; (P2P?) information exchange over local WLAN 11 Observations & Situation Awareness Cloud Storage Storing of BLOBS (photos, videos) Cloud Storage Storing of BLOBS (photos, videos) Marketplace GEs: sales, revenue sharing Marketplace GEs: sales, revenue sharing Pub/sub GE: Events processing & dissemination Pub/sub GE: Events processing & dissemination Security GEs: user & right mgm.; legal compliance Security GEs: user & right mgm.; legal compliance IoT GEs: Smart sensors? IoT GEs: Smart sensors? Environmental SEs Meaning Data Fusion, Forecasting Harvestors, Connectors Observations Big data GEs: Annotation & processing Big data GEs: Annotation & processing Cloud mgm. GEs: automated deployment, scaling Cloud mgm. GEs: automated deployment, scaling I2ND GEs: Network reliability, Hardware abstraction, I2ND GEs: Network reliability, Hardware abstraction, Mashup GE: Ad-hoc applications Mashup GE: Ad-hoc applications Copyright © 2013 Denis Havlik, AIT Austrian Institute of Technology GmbH.
  • 12. Challenge: adding features 12 ENVIROFI web-mashup PoC: http://youtu.be/yEXlLQYq7s4
  • 13. Challenges: user interaction View existing knowledge •Map view •Table view •Detailed View •Areas of Interest View existing knowledge •Map view •Table view •Detailed View •Areas of Interest Receive information (events!) •Requests for more observations, •Warnings, e.g. “pollen warning” •Interests, e.g. “monumental tree in vicinity” Receive information (events!) •Requests for more observations, •Warnings, e.g. “pollen warning” •Interests, e.g. “monumental tree in vicinity” Report observations •“New” things, e.g. “here and now I see a tree” •Personal, e.g. “I have a headache” •Obs. on existing thing, e.g. “this tree currently blossoms Report observations •“New” things, e.g. “here and now I see a tree” •Personal, e.g. “I have a headache” •Obs. on existing thing, e.g. “this tree currently blossoms Inform Server Backend (or proxy) Alert! Request Action! Copyright © 2013 Denis Havlik, AIT Austrian Institute of Technology GmbH. 13
  • 14. Crowd tasking workflow 14 Mobile Users Sensors Automated Tasking External Data Manual Tasking Decision maker Experts Algorithms Copyright © 2013 Denis Havlik, AIT Austrian Institute of Technology GmbH. Mobile Users
  • 15. Challenges: tracking of users? Work with „Areas of Interest“: •defined by users •Could be automatically generated when user significantly moves Why AOIs? 1.pre-fetching of data => allows offline use 2.Server-side filtering of events No tracking! Current users position is taken into account by the Mobile app logic! Copyright © 2013 Denis Havlik, AIT Austrian Institute of Technology GmbH. 15 1 2 3 4 5 6 7 8 AOIAOI AOIAOI AOIAOI
  • 16. Do it on the phone! Copyright © 2013 Denis Havlik, AIT Austrian Institute of Technology GmbH. 16 Do you really need to process users sensitive data (e.g. health- related) on the backend server?
  • 17. Environmental Georeferenced Observation App Environmental Georeferenced Observation App Environmental Georeferenced Observation Service Environmental Georeferenced Observation Service Challenges: unreliable networks Copyright © 2013 Maria Egly, AIT Austrian Institute of Technology GmbH. 17 ServerClient CouchDB/ GeoCouch CouchDB/ GeoCouch continuous replication standardized technologies  GeoJSON  HTTP  Storage/Retrieval via http RESTful Interface  Changes Notification API used for app GUI updates  Created on user‘s first login  Filtered replication to Environmental Georeferenced Observation Service
  • 18. Environmental Georeferenced Observation Service Environmental Georeferenced Observation Service Environmental Georeferenced Observation Proxy Service Environmental Georeferenced Observation Proxy Service ClientClient Challenges: platform dependence Copyright © 2013 Maria Egly, AIT Austrian Institute of Technology GmbH. 18 ServerServer Sencha Touch Android Blackberry OS Apple IOS Windows Phone PhoneGap = Apache Cordova Presentation LayerPresentation Layer Application LogicApplication Logic VisualizationVisualization standardized technologies  Javascript  HTML 5  CSS Platform independent* *to a large extent; minor porting effort necessary HTTP Interfaces: OpenStreetMap Google Service API FI-Ware Object Storage FI-Ware Identity Management
  • 19. Copyright © 2013 Denis Havlik, AIT Austrian Institute of Technology GmbH. 19 Lessons learned?
  • 20. Ultimate challenge: industrialization We are transforming the ENVIROFI experiments into a reliable and scalable FOSS product.
  • 21. Ultimate challenge: industrialization First public presentation of “UBICITY” by Jan von Oort is tomorrow! (11:20-11:40 - during coffee break) hint: http://xkcd.com/1110/
  • 22. 1. The ideas presented today were developed and partially realized as Mobile Data Acquisition System (MDAF) in the scope of the European Community's Seventh Framework Programme (FP7/2007-2013) under Grant Agreement Number 284898 (ENVIROFI) 2. The importance of microlearning really became clear to me very recently, thanks to Dr. Christian Voigt and the microlearning 7.0 conference. 3. MDAF contributors: Eun Yu, Clemens Bernhard Geyer, Peter Kutschera, Markus Falgenhauer, Markus Cizek, Ralf Vamosi, Maria Egly, Hermann Huber and most recently Jan von Oort. • Currently active developers are underlined. Acknowledgements 22
  • 23. • All slides which are marked as © 2013 Denis Havlik or © 2013 Maria Egly can be re-used under the terms of the Creative Commons ”Attribution- ShareAlike 3.0“ license. • Illustration on the pages 2 and 6 have been marked for free re-use by their authors. To the best of my knowledge the licenses are compatible with CC. • Disclaimer: I am not a lawyer. Please follow the links for more info. • The logos on slides 17 and 23 are of course IPR of the respective companies. To the best of my knowledge this falls into “fair use” IPR and fair re-use 23
  • 24. Thank you for your attention Dr. Denis Havlik denis.havlik@ait.ac.at The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under Grant Agreement Number 284898 www.envirofi.eu