Effective Use of Smart Sensors in Digital Governance


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


Rajan Zambre

CEO/CTO, Erallo Technologies, Inc.

20 Taylor Street, Littleton, MA 01460 www.erallo.com; zambre@erallo.com

Dr. G.P. Patil

Distinguished Professor of Mathematical Statistics

Professor of Mathematical and Environmental Statistics,

Pennsylvania State University, Pennsylvania, U.S.A. gpp@stat.psu.edu

Mr. Vijay Singhal

District Collector

Jalgaon District, State of Maharashtra, India

Dr. Sharad W. Joshi

Professor, Slippery Rock University, Pennsylvania sharadchandra.joshi@sru.edu


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


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


• tedious manpower tasks

• measurement and reporting errors

• scheduling problems, time delays

• data entry tasks, data tampering


• 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


Wireless Radio


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


Sensor Network

Sensor Network


Transmit Network



DSS Applications

Central Unit data recording

Software Components




Radio Node Software


Base Station, Data Logger Software


Management Software, GUI interface

Base Station Radio Node











Data Base



Node Mgt






Low Cost Network Scenario

Ad-hoc Network of Radio Nodes

RF radio, processor, ADC, memory communication between other nodes and


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


100 feet to ½ mile, depending on radio type

Pressure Transducer Sensors

Jalgaon District, India

Jalgaon Projects

Smart Sensor Applications Planned


Integrated Child Development Scheme (IDCS)


Integrated Water Resource Management


Micro-weather Station Network


Farmer Advisory Network


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


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


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 dam level sensors rain gauge sensors inspection well sensors


Sensor Nodes

Transmit network

DSS Applications

Central Unit data recording


Micro-weather Station Sensor Network


• 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


• Network of low cost, commercially available weather stations

• Integrated with micro-processors and communications devices

• Distribute throughout the district


• 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


• 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.


Dr. G.P. Patil, Distinguished Professor, Pennsylvania State University gpp@stat.psu.edu

Mr. Vijay Singhal, District Collector

Jalgaon District, State of Maharashtra, India

Dr. Sharad W. Joshi, Professor, Slippery Rock University, Pennsylvania sharadchandra.joshi@sru.edu