Problem: Flooding Without Warning

How to predict river flooding 24 hours in advance given:• Large geographic areas of 10000 km2

• Limited sensing infrastructure• Difficult environmental conditions• No existing infrastructure• Numerous contributing variables and no historical data• Computationally extensive and centralized prediction

models

Solution: Regression Prediction Model

Solution: Sensor Network for Autonomous Early Warning

System Architecture• 900 MHz mini-networks of sensing nodes measuring precipitation, temperature, and river

pressure with support for additional sensors if needed• 144 MHz radio backbone running the distributed computation model to determine flood

risk• Office nodes to provide flood alerts, to assist in maintaining system, and to obtain Internet

satellite and weather data• Community nodes to alert of incipient flooding

Field Experiments: Honduras and Dover, Massachusetts

Rio Aguán BasinTocoa, Honduras

Tocoa

Field experiment of system in Honduras:• Installed:

- 4 sensing nodes: 2 precipitation and 2temperature- 2 computation nodes with river levelsensors- 1 office node

• Computed distributed calibration andprediction model

Field experiments in Charles River at Dover, Massachusetts:• Fall 2007

- Installed 3 sensing nodes; one of each type- Gathered 4+ weeks data

• Fall 2008- Installed 5 sensing nodes: 1 pressure, 2 temperature, and 2precipitation- Ran distributed prediction with predetermined coefficients- Operational for approximately 2 months

Installed infrastructure in Honduras:• Radio antenna towers:

- 5 meter for river locations- 10 meter for offices

• Solar power backup system for offices with automatic switching from grid• Pressure sensor installation (bridge design only)

Model‐Based Monitoring For Early Warning Flood DetectionElizabeth Basha and Daniela Rus

Distributed Robotics Lab

• Calibrate model using novel 3-step distributed pseudoinverse algorithm whereA is mxn and m≥n:

1. QR Decomposition: [Q, R] = qr(A)2. Singular Value Decomposition: [U, R, V] = svd(R)3. Pseudoinverse Combination Step: xopt=VR-1(QU)Tb

• Verified in Matlab using randomly generated matrices• Implemented on sensor network and tested using internal temperature

sensors; system ran error-free for 8 hours with calibrating the model every 10minutes

Other Applications• Predict congestion in networks of multi-function devices that can provide print,

scan, fax, and email service along with other functionality in officeenvironments (with Xerox)

• Predict water usage across multiple agriculture fields in order to provideefficient and dynamic control of water irrigation systems (with CSIROBrisbane)

• Need simple prediction computation thatcan run distributed on sensor network

• Developed multiple linear regressionmodel operating on locally sensed riverlevel, precipitation, and air temperaturedata

• Verified using 7 years of data from theBlue River in Oklahoma:- Sensors consist of 6 precipitation, 1 airtemperature and 1 river flow- Model uses 1 year of data for training

Precipitation Sensing Node Computation Node

Pressure Sensor