Michael Murphy, Huthasana Kalyanam, John Hess, Vance Faber, Boris Khattatov

Fusion Numerics Inc.

Overview of Current Research inSensor Networks and Weather Modeling

Fusion Numerics Inc. is an innovative software engineering company focused on building predictive simulation models and tools.

Relevant Activities

• Ionospheric ForecastingDASI – Distributed Array of Small

Instruments• Source estimation of pollutants in the

troposphere• Wireless Sensor Networks

Long-term Ionospheric Forecast System

Atmospheric Modeling and Source Identification

We have worked on a NASA-sponsored project related to modeling and assimilation of air pollution.

We used a 3-D tropospheric chemistry-transport model.

Dr Khattatov has led projects on Inverse modeling and source

identification of tropospheric pollutants.

Atmospheric Modeling and Source Identification Examples

The derived strengths of surface sources of carbon monoxide (top)

A regional 3-D distribution of modeled carbon monoxide (bottom left)

A global model (bottom right)

Sensor Business Unit

Mission: To create and market the leading sensor network

design and simulation platform.

Core Objectives: Develop key solutions for the design and

management of sensor networks Enhance our technical skills through partnerships Expand research funding from government agencies Commercialize solutions

Solution GoalsCreate a simulation and design platform for wireless sensor networks

that takes into account: Communications methods and constraints Sensor deployment strategies Power management Collaborative signal processing Hardware costs and cost tradeoffs

To provide a Hardware-Software agnostic simulator.To provide solutions of two types

General Designing a wireless sensor network optimal for a particular application

under a given set of operational constraints To support design trade-off engineering decisions

Is it more cost effective to deploy a few expensive sensors or many inexpensive sensors?

Is localized processing more efficient than centralized processing?

Navy ApplicationNavy Application

Communication Issues

Communication range Multi-hop routing protocols Directional Vs Omni-

directional antennae issues

(attenuation vs. antenna positioning)

Design of optimal data compression algorithms (Lossy and Lossless)

Lossless or near-lossless data compression

• MICA researchers have been studying traditional compression methods such as gzip

• We are considering: Wavelet-based compression techniquesBayesian compression methods

JPEG 2000 Wavelet transform

An example:

Wavelet Analysis• The advantage of using wavelets is that a large number of detail

coefficients are very small in magnitude.

• Truncating these coefficients introduces very small errors in the signal. Especially useful when combined with arithmetic coding. (JPEG2000 image compression works this way.) (typically 3:1 lossless, 25:1 “visually lossless.” YMMV).

• We can approximate the original data distribution efficiently by keeping only the most significant coefficients.

• Together with a consultant, Dr. Vance Faber, we have developed an algorithm for computing wavelet transforms having desirable (user-specified) properties. For example, this method could be used to create a reversible wavelet using integer coefficients. This method allows us to extend almost any convolution-based filter into a reversible wavelet pair. The filter can then be applied to the sequence and the high frequency coefficients can be removed. The inverse wavelet can then be applied to produce a much smoother version of the original sequence.

Power Management

• Algorithms to maximize Sensor Network lifetime

• Efficient energy-aware data-routing algorithms

• Methods to adapt query schedule based on event detection

• Power modeling of transmission and reception costs

Programming

• When should samples for a particular query be taken?

• What sensor nodes have data relevant to a particular query?

• In what order should samples for this query be taken, and how should sampling be interleaved with other operations?

• Is it worth expending computational power or bandwidth to process and relay a particular sample?

Collaborative Signal Processing

We are developing proprietary algorithms forTarget Detection, Tracking & Classification,

which minimize false positives and negativesSensor calibration, using advanced statistical

methodsPlume tracking using mobile sensors.

Sensor Deployment and Initialization

• Simulation of Deployment Techniques• Sensor Localization• Tracking of Sensors• Sensor Density• Sensing Range• Sensor synchronization

Example Achieved Results

• We have developed optimal algorithms for establishing communication routing trees when transmission ranges are limited by attenuation or hardware constraints.

• We have developed simple and energy-efficient methods for detection and tracking.

Energy based Target detection

Time delay of Arrival detection

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Communication Protocol

Our Strengths

Analyzing the rich design space of wireless sensor networks

CommunicationPower managementEnergy-efficient signal processing algorithmsBandwidth conservation techniques (e.g.

compression and onboard filtering)

Ionospheric modeling

What We Seek

Strong Partners like ENSCO for joint efforts in pursuing government funding through the SBIR and BAA programs.

Additional resources and capabilitiesHardware validation of our algorithms and

protocolsHardware solutions and vendorsAccess to expertise in R/F MEMS and MEMS

sensors

Next Steps

Our Work

Our present work – TransSensorIt’s being developed for the US NAVYIt’s aimed towards developing a simulator to

aid sensor deployment, protocol management, sensor connectivity, power management, submarine detection.

Contd…

We have designed protocols for efficient communication which would ensure sensor connectivity and coverage.

We have also efficient algorithms for target detection and location. Energy Based Time delay of Arrival

We have established theorems for connectivity and coverage

We also have simulated power consumption requirement.

Algorithms – A brief Overview

Energy BasedTime Delay of Arrival

Our Results

MEMS

What do we bring to ENSCO

Fusion could offer the following to ENSCOWe can tune our simulator to carry out the

following operationsSimulate sensor deploymentCommunication protocolsSensor coverage and connectivityIdentify power requirementsIdentify problems and provide solutions once

sensor is deployed

Together We Can

ENSCO makes the sensors

Fusionnumerics provide entire

Software support

Output