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
– ICDS
RF Radio Node

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

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

• 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 Sensor Node = sensor device + connectivity, processor, memory, battery
Sensor
Wireless Radio
Processor,
Memory, Battery
Smart Node

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
Gateway
Transmit Network
Gateway
Database
DSS Applications
Central Unit data recording

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

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

Smart Sensor Applications Planned
1.
Integrated Child Development Scheme (IDCS)
2.
Integrated Water Resource Management
3.
Micro-weather Station Network
4.
Farmer Advisory Network

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)

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

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

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

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

• 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

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

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

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

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

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

Rajan Zambre, CEO/CTO, Erallo Technologies, Inc.
zambre@erallo.com
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