Thesis Slides

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Apr 24, 2022 1 COMMON-Sense Net Wireless Sensor Networks for Marginal Farming in India Jacques Panchard PhD Private Defense Advisor: Prof. Jean-Pierre Hubaux

Transcript of Thesis Slides

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COMMON-Sense NetWireless Sensor

Networks for Marginal Farming in India

Jacques Panchard PhD Private Defense

Advisor: Prof. Jean-Pierre Hubaux

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COMMON-Sense Net Motivation: Help define and implement farming strategies for

poor farmers in a situation of water scarcity.

Outcome: decision support system based on environmental monitoring via sensor networks

Participatory design Data collected: soil moisture,

temperature, humidity Web-based data access with GIS

http://commonsense.epfl.ch

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Outline of the presentation Field State-of-the-Art Project description Summary of Results Sensor-Tunes Conclusion

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Environment and Development? There is a growing tension between economic

development and environmental sustainability. Millennium Development Goals and the

Environment: Ensure environmental sustainability Eradicate extreme poverty and hunger Reduce child mortality

Enhanced environmental monitoring: Explore Environment-to-Person Information Systems. New technological window: wireless sensor networks

FieldField State of the ArtState of the Art CSNCSN ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

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Wireless Sensor Network

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ICT4D ICT4D: Information and Communication Technologies for

Development

ICT as an enabler for development [Heeks 2002, 2008], [Walsham 2001], [Negroponte 1998] Empowerment Fight against exclusion Prevention of corruption

Common pitfalls of ICT4D projects [Heeks 2002, 2008] Design/actuality gaps Sustainability Scalability

Design guidelines [Surana et al. 2008], [Heeks 2002, 2008] Use Hybrids Optimization of existing systems Financial self-sufficiency Operational self-sufficiency

FieldField State of the ArtState of the Art CSNCSN ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

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Test-Case: India

India is a country with two faces Extreme Poverty Cutting Edge Technology and Excellent

higher education system

FieldField State of the ArtState of the Art CSNCSN ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

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India Agriculture Agriculture in India : 67% of employment

Small land-holding: More than 80% farms are less than 4 ha 39% farms are less than 1 ha (Karnataka)

Green revolution: from subsistence to market agriculture

Difficulties for small farmers: Cannot invest in intensive agriculture practices Institutional crisis: From collectively managed infrastructure to

individual management Early impacts of climate change Semi-arid areas characterized by low-rain fall and high climatic

variability

FieldField State of the ArtState of the Art CSNCSN ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

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WSN Projects in Developing Countries

Arsenic detection in Bangladesh: Bangladesh University of Engineering, UCLA, MIT

SenSlide: Landslide detection in India University of Colorado, Boulder, IIT Mumbai, Microsoft

Research

Water-quality management in Malawi Royal Institute of Technology, Stockholm

Flood detection in Honduras MIT

Road Quality Monitoring in Sri Lanka University of Colombo, Sri Lanka

FieldField State of the ArtState of the Art CSNCSN ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

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WSN Projects in Developing Countries

FieldField State of the ArtState of the Art CSNCSN ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

Arsenic detection

Landslide prediction

Water quality mgt

Flood detection

Road Monitoring

Location Bangladesh India Malawi Honduras Sri Lanka

Status Full deployment

Simulation, laboratory

LaboratoryPreliminary deployment

Laboratory

Architecture3-Tier Multi-cluster WSN

2-Tier Multi-cluster WSN Multi-hop WSN

2-Tier Multi-cluster WSN

Delay-Tolerant Network with Data Mules

Size48 sensors

3 APs65 (deployed)600 (projected)

4 (projected) Unknown Unknown

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WSN Projects in Developing Countries

FieldField State of the ArtState of the Art CSNCSN ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

Arsenic detection

Landslide prediction

Water quality

mgt

Flood detection

Road Monitoring

Platform Unknown TelosB SPOT Proprietary Proprietary

Radio range

< 100m 30 – 40m 200-300m

8 km (cluster)

25 km (inter-cluster)

30 – 40m

Data

Soil moistureTemperature

pHNitrates

Rock strainspH

RedoxTurbidity

Water pressure

Acceleration

Energy Solar panel Battery Solar panel Solar panel Car battery

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System Description

FieldField State of the ArtState of the Art CSNCSN ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

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COMMON-Sense Net

IISc

CEDT Prof. Jamadagni

EPFL/SDC Cooperation Fund

EPFL

SDC

HYDRAM Prof. Mermoud

MICSCluster 2

ChennakeshavaTrust (NGO) S. Rao

LCA Prof. Hubaux, J. Panchard

FieldField State of the ArtState of the Art CSNCSN ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

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Chennakeshavapura

FieldField State of the ArtState of the Art CSNCSN ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

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Test-beds

FieldField State of the ArtState of the Art CSNCSN ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

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Platform selection: mica2 motes

• Crossbow Technology IncCrossbow Technology Inc• Atmel ATMega128 processorAtmel ATMega128 processor• Chipcon CC1000 radio (915 MHz)Chipcon CC1000 radio (915 MHz)• Runs tinyOSRuns tinyOS

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Platform Choice: TinyNode

• ShockfishShockfish• MSP 430 processorMSP 430 processor• XEMICS 1205 radio (868MHz)XEMICS 1205 radio (868MHz)• Runs tinyOSRuns tinyOS

FieldField State of the ArtState of the Art CSNCSN ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

Tinynode has: Better useful radio range : more than 500 m was achieved at

15 dBm In theory longer lifetime (tests on-going)

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Lessons Learned

• Environmental monitoring is useful for small farmers (User survey 2004)

• Impossibility to identify precise use cases for marginal farmers at this point

• Specialists are needed to deploy and maintain the network

• Relationship with farmers : estrangement towards technology

Consequence:• Necessity to develop tools to put wireless sensors in the

hands of non-specialists• Target scientists rather than farmers. • Find precise use cases, design experiments, carry them

out in a controlled environment

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Survey of agriculture scientists

Qualitative and quantitative 20 participants: Indian agriculture

scientists Briefing meeting 2 weeks

Logging of web application queries Scenario-based detailed questionnaire

Debriefing interviews

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Environmental Data for marginal agriculture

FieldField State of the ArtState of the Art ContributionContribution ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

Rain Fall16%

Soil Type17%

Temperature22%

Soil Moisture

29%

Humidity16%

Spatial variability: High: soil moisture Low: temperature,

rain-fall, atm. pressure

Mixed: Soil physics Time variability:

No consensus Day is lower limit

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Debriefing Meetings Usefulness

General agreement Gap between discourse and behavior Debriefing allows optimism

Usability Use cases Space/time variability still an open question

Usage Disinterest? Computer literacy issues No one-size-fits-all scenario

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New method for agriculture scientists Based on data collected by the test-beds 3 use cases and potential customers found Experiments could take place in 2009 (S. Rao)

Entomology Explore links between soil moisture recent

history and emergence of white grub and red hairy caterpillar

Crop physiology Asses water efficiency of different crops

Drip irrigation Assess efficiency of underground

drip irrigation

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Sensor-Tune

FieldField State of the ArtState of the Art ContributionContribution ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

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Deployment Technical Issues

• Challenging physical environment• Connectivity depends on fluctuating radio

channel• Network topology is highly unpredictable• Difficulty to know link-quality over time

• Ergonomic problem• LCD displays are ill-adapted to outdoor

conditions• Wireless sensors have very limited interfacing

capabilities• Task at hand requires attention and

disponibility

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An Alternative: Sonification• Hearing is a passive sense

• Does not require specific attention

• Happens continuously in the background

• The human brain can analyze complex sounds

• Physical ability is not impaired by hearing

• Hands are free for the work at hand

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Sensor-Tune

FieldField State of the ArtState of the Art ContributionContribution ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

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Use Cases

FieldField State of the ArtState of the Art ContributionContribution ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

Non-expert users

WSN deployment/maintenance staff

Live connectivity to the Base Station As deployment goes on Monitor radio link Monitor distance to one base station

Connectivity history over the last 24 hours Nodes with a PER below a threshold replay their history User can select and tag nodes

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Parameters and Mapping• Monitor

• Packet delivery rate• Distance to the base station

• Model ideal state with a repetitive pleasant sound: • Music• Voice• Natural sound (bird songs)

• The noise metaphor: Degrade the sound when the quality drops.

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Prototype

FieldField State of the ArtState of the Art ContributionContribution ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

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User experiment

FieldField State of the ArtState of the Art ContributionContribution ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

• Performed indoor• Simple deployment task • Non-specialists• Comparison between audio and visual

• Indoor, audio interface performs as well as visual

• Audio advantages in a challenging environment (hands free, vision free), come at no performance price

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Contributions Participatory design the first WSN toolkit

for resource-poor agriculture First reported WSN deployment in an

ICT4D project Identified precise applications with

agriculture scientists Developed a WSN interface targeted at

non-specialists, using sonification

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Future Work

Extended laboratory deployments with identified scientific partners

Extensive tests of sonification interface in the field

FieldField State of the ArtState of the Art ContributionContribution ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

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PublicationsJournal J. Panchard, S. Rao, T. V. Prabhakar, J.-P. Hubaux, and H. S. Jamadagni. COMMONSense Net: A

Wireless Sensor Network for Resource-Poor Agriculture in the Semiarid Areas of Developing Countries. Information Technologies and International Development, 4(1):51-67, 2007.

Conference/Workshop J. Panchard, P. S. Rao, M. Sheshshayee, P. P. Papadimitratos, and J.-P. Hubaux. Wireless Sensor

Networking for Rain-fed Farming Decision Support. In ACM SIGCOMM Workshop on Networked Systems for Developing Regions, 2008.

Prabhakar T V , N V Chalapathi Rao, Sujay M S, J. Panchard, H S Jamadagni, A. Pittet, Sensor Network Deployment For Agronomical Data Gathering in Semi-Arid Regions, WISARD 2007

J. Panchard, S. Rao, T.V. Prabhakar, H.S. Jamadagni and J.-P. Hubaux, Improved Water Management for Resource Poor Farmers via Sensor Networks, ICTD06, Berkeley

J. Luo, J. Panchard, M. Piorkowski, M. Grossglauser and Jean-Pierre Hubaux, MobiRoute: Routing towards a Mobile Sink for Improving Lifetime in Sensor Networks, INSS2006

J. Panchard, A. Osterwalder, ICTs and Capacity Building through Apprenticeship and Participatory Methods, Applied to an ICT-based agricultural water management system, IFIP WG 9.4, Abuja 2005

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Thank you.

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System Description

FieldField State of the ArtState of the Art ContributionContribution ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

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System Description

FieldField State of the ArtState of the Art ContributionContribution ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

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Design Science and participative Method

3 Development

2 Suggestion

1 Awareness of Problem

4 Evaluation

5 Conclusion

Community meetings led

by a local faclitatorTranslation into

a technical language: local facilitator and

scientists

Multidisciplinary development

Participatory approach and

apprenticeshipDiscussion and refinement of the artifact. (back to 1)

FieldField State of the ArtState of the Art ContributionContribution ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

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Protocol Description (Live use case)

FieldField State of the ArtState of the Art ContributionContribution ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

PDA MasterWireless Monitor Slave iWait Init

Start_Probing

Info_request(seq_num, g_info*)

Computeg_info(i,t)

if (g_info(i,t) < g_info*)Info_response(l_info, g_info)

Info(l_info, g_info*)Probing(l_info, g_info)

Delay D

INIT

ACTIVE

CLOSE

Info_request(seq_num, g_info*)

Update g_info*

Stop Stop_ProbingStop_Info

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Clustering

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Use Case 1: Entomology Description

Explore links between soil moisture recent history and emergence of white grub and red hairy caterpillar

Benefit for farmers Predict occurrences Buy pesticides accordingly

Methodology Milieu: Field Sensors: Soil moisture, temperature Number: 10 (?) Duration: 1 year Frequency: daily (?)

FieldField State of the ArtState of the Art ContributionContribution ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

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Use Case 2: crop physiology Description

Asses water efficiency of different crops Benefit for farmers

Appropriate crop selection Methodology

Milieu: Pots in the lab Sensors: Soil moisture Number: 100+ Duration: 80 days Frequency: as high as possible

FieldField State of the ArtState of the Art ContributionContribution ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion

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Use case 3 : Irrigation Description

BAIF applied research Assess efficiency of underground drip irrigation

Benefit for farmers Assess benefit/cost ratio

Methodology Milieu: Field Sensors: Soil moisture Number: 100+ Duration: ? Frequency: daily

FieldField State of the ArtState of the Art ContributionContribution ResultsResults Sensor-TuneSensor-Tune ConclusionConclusion