“ENVIROfying” the Future Internet

THE ENVIRONMENTAL OBSERVATION WEBFOR THE CROSS-DOMAIN FI-PPP APPLICATIONS

Future Internet enablers for VGI applications

ENVIROINFO 2013, Sept. 02-04 2013

Denis Havlik (AIT), Javier Soriano (UPM), Carlos Granell (JRC), Stuart E.

Middleton (IT-INNOV); Hylke van der Schaaf (IOSB), Arne J. Berre (SINTEF),

Jasmin Pielorz (UBIMET)

Overview

2

FI-PPP Programme

© 2013 CONCORD Project Consortium 3

FI-WARE: Technology Foundation

SME Innovation

3rd Call Use Case

Expansion Phase

Call 3

TF Continuation

FI-CONTENT 2

FITMAN

FI-STAR

FIspace

Call 2

FINESCE

XIFI: Capacity Building

INFINITY: Capacity Building & Infrastructure

ENVIROFI

Call 1

CONCORD: Programme Facilitation & Support

20112010 2012 2013 2014 2015

Phase 3Phase 1 Phase 2

FI-CONTENT

US

E C

AS

ES

OUTSMART

SafeCity

FINSENY

SMARTAGRIFOOD

Instant Mobility

FInest

3

ENVIROFI Vision

We envision …• an Environmental Observation Web in which all

environmental data, from sensors, citizens, and models, is available through the Internet in a standardized, usable format

• a system with dynamic understanding of the Earth’s atmospheric, marine and terrestrial spheres for the benefit of all European citizens

© 2013 ENVIROFI Project Consortium

Why FI for environment?

5

ENVIROFI Scenarios

1. Bringing Biodiversity into the Future Internet• Enabled biodiversity surveys with advanced ontologies• Analysis, quality assurance and dissemination of biodiversity data

2. 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”

3. 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

© 2013 ENVIROFI Project Consortium

Focus on Observations

ENVIROFI focuses on observations and re-use of the standardized geospatial services

Observations can originate from various sources• Web-enabled sensors and sensor networks

• Citizen observations / human sensors

• Models and data

fusion services

© 2013 ENVIROFI Project Consortium

Humans are sensors,but different

Illustration by Scoobay (http://www.flickr.com/photos/scoobay/224565711/) 8

OBSERVEPUBLISH

DISCOVERCOMPOSEANALYSE

ACTNOTIFY

MANAGE

EnvironmentalMonitoring andDecisionLifecycle

HumanSensorDataService

GeospatialSEs

EnvironmentalSEs

FI-WARE - Generic EnablersApps

Serv.Repository

MarketplaceSem Comp

EditorWireCloudMediator

Cloud-I2ND

Alloc VMsAlloc Obj St

SecurityID Mgmt

Data 2Query BrokLocations

Seman App

Seman Ann

Data 1Comp

Evt.ProcPub/Sub Broker

BigData An

IoTThings MgmtDevice Mgmt

GW Data Handling

Prot Adapter

TAGging

Uncertaintysemanticannotation

MEDiation

Discovery brokerDiscovery augm componentAccess brokerConnector – SOSConnector – WCSConnector – WFSConnector – WMSConnector – toolboxMediator – SOSMediator – Fusion toolboxTranscode sensor

Fusion

Data fusionImage sample classificationAsset geo-reference analysis Areas classificationPrediction serviceModel-based fusion

GEO

Observ collectionObserv catalogueObserv retrievalObserv identificationObserv visualizationObserv operatiingImage sample archiveSample Quality AssesGeoref observ proxGeoref Observ app

NOTification

Alert notification

Sensor Event

VGI

Mobile VGI enablerMDAF

Cloud storage andSynchronisation (MDAF)

Environmental (Biodiversity, Atmospheric and Marine) ApplicationsEnvironmental (Biodiversity, Atmospheric and Marine) Applications

Geospatial ServicesOGC Charting OGC SensorWeb OGC Processing OGC Storage

(OPeNDAP) THREDDS

ERDDAP

WPSSOS, SPSWMS, WCS WFS

FI-War

e

FI-War

e

© 2013 ENVIROFI Project Consortium

10

Main developments: (1)mobile crowd-sourcing and crowd-tasking;(2)fusion of heterogeneous data

Envisaged functionality

11

Mobile Users

SensorsAutomatedTasking

External Data

ManualTasking

Decision maker

Experts Algorithms

Mobile Data Acquisition Framework(MDAF) architecture

12

Basic user interaction patternsView 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!

Observation DB

Dealing with heterogeneity and conflicts

14

Plausibility/Confidence checks

Consensus buildingPrevious situation

knowledge

HabitatInformatio

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

Plausibility of data and QA

15

Image Classifier SEQuality Assessment SE

classify & check images

Image Archive SEmanage images

MDAF servermobile acquisition

General Userleaf images

metadata (e.g. geotag)

Expertleaf species

manual assessment

© 2013 ENVIROFI Project Consortium

Application architecture

16

„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

16

Observations & Situation

Awareness

Cloud StorageStoring of BLOBS (photos, videos)

Cloud StorageStoring 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

Role of web Mashups

17

ENVIROFI web-mashup PoC: http://youtu.be/yEXlLQYq7s4

Lessons learned (VGI)

• Humans are good at occasional subjective observations and judgments; bad at repeating and quantization

• Users need to be motivated, e.g. by emphasizing their contributions and profile/context-aware “tasking”

• We can selectively task the users without continuously tracing their location

• Narrowing down the choices by automated processing services is a GoodThing™ (tested with e.g. leaf recognition, eHabitat).

Cloud hosting and citizens enablement are game-changers for environmental UA

Lessons Learned (FI-Ware)

Most FI-Ware GEs from the Cloud Hosting, Data/Context management, and Security chapters are truly generic and should be usable in environmental applications

•Event-related GEs are convenient for integration of GEs, SEs and third party services in FI application

•Application mashup platform can speed up application prototyping

•Marketplace & co. could be a way to bring environmental data and services to businesses that need them.

•GEs from the Internet of Things (IoT) chapter may be interesting for future applications

Main issues with FI-Ware: Lack of maturity (2-nd release already available!); Weak support for Geospatial data and processing, weak semantic stack; IoT partially competing with OGC SWE.

20

Unless stated differently, the slides are © 2013 Denis Havlik and licensed under the terms of the Creative Commons ”Attribution-ShareAlike 3.0“ license.

Re-use of resuls

21

MDAF development continues as FOSS. Code will be online next month; new

users and partners are welcome!

1. 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

2. The ENVIROFI produced significant input to standardisation through CEN/TC287, TR 15449-2/3, Geographic information — Spatial Data Infrastructures – Best practices, ISO 19119, OGC Topic 12, …

3. ENVIROFI is a collaborative effort of 14 partners (see next slide). Austrian part of the team includes AIT, UBIMET and the Austrian Environmental Agency

4. The Generic Enablers are developed and maintained by the „FI-Ware“ – the core project of the FI-PPP Programme.

Acknowledgements

22

Thank you for your attentionDr. 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