Effective Use of Smart Sensors in Digital Governance

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Effective Use of Smart Sensors in Digital Governance

January 14-16, 2008 / Bangkok Thailand

Rajan Zambre, CEO/CTO

CEO/CTO, Erallo Technologies, Inc., USA

Dr. Ganapati P. Patil

Distinguished Professor, Pennsylvania State University, USA

Mr.Vijay Singhal

,

District Collector, Jalgaon, Maharashtra, India

Dr. Sharad Joshi

Professor, Slippery Rock University, Pennsylvania, USA

Contributors

Rajan Zambre

CEO/CTO, Erallo Technologies, Inc.

20 Taylor Street, Littleton, MA 01460 www.erallo.com; [email protected]

Dr. G.P. Patil

Distinguished Professor of Mathematical Statistics

Professor of Mathematical and Environmental Statistics,

Pennsylvania State University, Pennsylvania, U.S.A. [email protected]

Mr. Vijay Singhal

District Collector

Jalgaon District, State of Maharashtra, India

Dr. Sharad W. Joshi

Professor, Slippery Rock University, Pennsylvania [email protected]

Overview

Presentation & Discussion:

• Data collection + data reporting for digital governance

• Sensors – Sensor Nodes – Sensor Networks

• Applications in digital governance

– Integrated water management

– Farm advisories

• Applications remote health monitoring

– ICDS

RF Radio Node

Digital Governance

Data Flow many, many, manual steps prone to errors, delays, tampering decision support systems administrators, policy makers

WEB access for public manual data transfer

(phone, paper, fax...)

more levels of data transfer,

supervisory levels, compilation

manual data entry manual data collection

Digital Governance

Data Flow with Sensors automated collection, transfer, reporting

automated

data measurement and recording

automated

data transfer, compilation and processing

automated

data entry decision support systems administrators, policy makers

WEB access for public

sensors

Reduces

tedious manpower tasks measurement and reporting errors scheduling problems, time delays data entry tasks, data tampering

Sensors

Devices to measure/detect real-world conditions

– Analog sensors

– Digital sensors

Sensor controls

CO2 Sensor

Soil Moisture Meter

Gypsum Blocks

Ethanol sensor to measure transdermal alcohol levels

Frequency Domain

Reflectometry (FDR) to measure water content of soil

Neutron sensor to measure soil moisture

Tipping bucket rain gauge

Smart Sensors

Smart Sensor Node = sensor device + connectivity, processor, memory, battery

Sensor

Wireless Radio

Processor,

Memory, Battery

Smart Node

Ad-hoc Sensor Network

Sensor Node

Sensor Node

Sensor Node

Router Node

Router Node

Sensor Node

Gateway Node to Wide Area Network connectivity

Sensor Node

Ad-hoc networks: self-configuring, self-healing, multi-sensor data fusion

Connectivity

Sensor Network

Sensor Network

Gateway

Transmit Network

Gateway

Database

DSS Applications

Central Unit data recording

Software Components

Analog

Digital

1.

Radio Node Software

2.

Base Station, Data Logger Software

3.

Management Software, GUI interface

Base Station

Radio Node

Sensor

Comm

Data

Processing

&

Commands

Radio

Comm

Radio

Comm

Data Base

&

Sensor

Node Mgt

Wide

Area

Network

Central

Unit

Low Cost Network Scenario

Ad-hoc Network of Radio Nodes

RF radio, processor, ADC, memory

communication between other nodes and

BaseStation

Data Logger/Base Station

RF radio, CPU, memory

communicates with Sensor Nodes and Bike Receiver

Bike Receiver

RF radio, CPU, memory

communicates with Data Logger/Base Stations

to Central Unit

Soil level

Water table

Range

100 feet to ½ mile, depending on radio type

Pressure Transducer Sensors

Jalgaon District, India

Jalgaon Projects

2.

3.

4.

Smart Sensor Applications Planned

1.

Integrated Child Development Scheme (IDCS)

Integrated Water Resource Management

Micro-weather Station Network

Farmer Advisory Network

1.

Integrated Child Development Scheme

UNICEF Program

• Promote childhood survival

• Provide integrated set of basic health services to children 0-6, pregnant women, and nursing mothers,

• Children’s weight and age are monitored to ensure they don’t fall below standards

• Administrated through communitybased childcare centers (in India, called Anganwadi Centers)

Improve Process

1. Weigh child -- weight, height, name

2. Anganwadi center

– village level – record on paper; duplicate/triplicates

3. Supervisor (ICDS worker) -- village level

adds date

4. Child Development Project Officer (CDPO) -- sub-district level

5. Deputy Chief Executive Officer

– district level

6. District Collector

– district level

7. State program level

8. National program level

Simple sensor technology

– could improve data accuracy and dates, reduce data tampering, reduce delays, save money, improve planning and allocation of funds/resources save lives

ICDS Sensor Network

PDA

(optional)

Digital weight scale

automated transmission using RF

Internet/

Web Services dialup or cellular

Base station at

Anganwadi center

Central Unit

Finger print scanner

Digital weight scale sensor node + integrated with finger print scanner = automated recording and wireless transmission of data

Child Mortality Data File

Typical Data File: Child Mortality Data

0 3138 111 1 3 5

1 2206 90 0 2 5 6

2 3558 159 1 6 7

3 2303 85 0 4 5 8

4 1455 48 3 5 8 9

5 2088 91 0 1 3 4 6 9 11 12

6 2762 92 1 2 5 7 12

7 2086 62 2 6

8 2774 117 3 4 9 10 13

9 1547 42 4 5 8 10 11

10 2411 91 8 9 11 13

11 2706 112 5 9 10 12 13

12 4679 170 5 6 11

13 3334 138 8 10 11

Data File Structure:

Column 1:

Cell (Tehsil) ID

Column 2:

Live births

Column 3:

Deaths 0 to 6 years

Column 4 onward:

IDs of cells adjacent to the given cell

Child Mortality Hotspot

2.

Integrated Water Resource Mgt

Components to Integrated Water Management

• Rainfall monitoring

• Dam/reservoir monitoring

• Inspection well monitoring

• Water quality monitoring

Primary Goals

• Effectively manage water usage for public and agro-industry

• Provide pre-emptive disaster management and services

Water Resource Problems

• Measurement, reporting, and entry of data –

all error prone

• No synchronization of dam overflow and rain fall

• Waste of growingly scarce resources

• Excessive use of water, lowering water table

• Need better data for more effective planning/managing

Integrated Sensor Network

weather sensors

for

Water Resource Management

Gateway

Gateway dam level sensors rain gauge sensors inspection well sensors

Gateway

Sensor Nodes

Transmit network

DSS Applications

Central Unit data recording

3.

Micro-weather Station Sensor Network

Currently

• Jalgaon district = 11,765 sq kilometers

• Over 780 villages on river banks

• Weather monitoring only done at district level

• Doesn’t provide granularity or precision for emergency warning services

• Flood of 2006 – emergency services were not delivered in time due to lack of data

Sensors for Early Warning

Need:

• Network of low cost, commercially available weather stations

• Integrated with micro-processors and communications devices

• Distribute throughout the district

Benefit:

• Provide timely warnings

before

damage to property or loss of life occurs

• Speed resources to areas for recovery

• Integrate with water management network

4. Farmer Advisory Network

Need up-to-date information:

• Water availability, irrigation forecasts, pumpage yeild

• Weather forecasts

• Current soil conditions (N,P,K)

• Water quality – organic & inorganic

• Disease forecasts

Sensor Data for Farming

Soil Moisture Sensor

Benefits:

• Aid in cropping decisions

• Increase crop yields

• Reduce disease

• Correctly plan for irrigation forecasts

• Accurately plan for fertilization

• Plan for seed availability

Next Steps

Discussion / Questions

Thank You !

Rajan Zambre, CEO/CTO, Erallo Technologies, Inc.

[email protected]

Dr. G.P. Patil, Distinguished Professor, Pennsylvania State University [email protected]

Mr. Vijay Singhal, District Collector

Jalgaon District, State of Maharashtra, India

Dr. Sharad W. Joshi, Professor, Slippery Rock University, Pennsylvania [email protected]

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