1. Michael Murphy, Huthasana Kalyanam, John Hess, Vance Faber, Boris Khattatov Fusion Numerics Inc. Overview of Current Research in Sensor Networks and Weather Modeling

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

3. Relevant Activities Ionospheric Forecasting DASI – Distributed Array of Small Instruments Source estimation of pollutants in the troposphere Wireless Sensor Networks

4. Long-term Ionospheric Forecast System

5. 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.

6. 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)

7. 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

8. Solution Goals Create 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?

9. Navy Application

10. 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)

11. Lossless or near-lossless data compression MICA researchers have been studying traditional compression methods such as gzip We are considering: Wavelet-based compression techniques Bayesian compression methods

12. JPEG 2000 Wavelet transform An example:

13. 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.

14. 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

15. 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?

16. Collaborative Signal Processing We are developing proprietary algorithms for Target Detection, Tracking & Classification, which minimize false positives and negatives Sensor calibration, using advanced statistical methods Plume tracking using mobile sensors.

17. Sensor Deployment and Initialization Simulation of Deployment Techniques Sensor Localization Tracking of Sensors Sensor Density Sensing Range Sensor synchronization

18. 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.

19. Energy based Target detection

20. Time delay of Arrival detection

21. 16 105 12 10 8 9 15 10 5 9 15 17 8 35 Communication Protocol

22. Our Strengths Analyzing the rich design space of wireless sensor networks Communication Power management Energy-efficient signal processing algorithms Bandwidth conservation techniques (e.g. compression and onboard filtering) Ionospheric modeling

23. What We Seek Strong Partners like ENSCO for joint efforts in pursuing government funding through the SBIR and BAA programs. Additional resources and capabilities Hardware validation of our algorithms and protocols Hardware solutions and vendors Access to expertise in R/F MEMS and MEMS sensors

24. Next Steps

28. Our Work Our present work – TransSensor It’s being developed for the US NAVY It’s aimed towards developing a simulator to aid sensor deployment, protocol management, sensor connectivity, power management, submarine detection.

29. 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.

30. Algorithms – A brief Overview Energy Based Time Delay of Arrival

31. Our Results

32. MEMS

33. What do we bring to ENSCO Fusion could offer the following to ENSCO We can tune our simulator to carry out the following operations Simulate sensor deployment Communication protocols Sensor coverage and connectivity Identify power requirements Identify problems and provide solutions once sensor is deployed

34. Together We Can ENSCO makes the sensors Fusionnumerics provide entire Software support Output