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Explore acoustic source localization using a heterogeneous network with ENSBox and Mica2 Mote technology. Investigate system design, simulation, and experimental results.
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CS Honors Undergraduate Research Program - Final Project Talk Tingyu Thomas Lin Advisor: Professor Deborah Estrin Date of Presentation: Thursday, June 7, 2007
An Investigation of Acoustic Source Localization in a Heterogeneous Network
Outline • Overview of system • Design • Simulation • Experimentation and Results
Outline • Overview of system • Design • Simulation • Experimentation and Results
Overview of System • Two tiered distributed sensing network: • ENSBox • + Lots of resources, precise • - Expensive in cost, resources, to deploy in large numbers • Mica2 Mote • + Cheap to deploy in large numbers • - Resource constraints, poor resolution in measurements • Why? • To leverage the advantages respective advantages • General context of acoustic source localization
Outline • Overview of system • Design • Simulation • Experimentation and Results
System Goals • Functionality to support acoustic localization: • Wireless Communication • Time synchronization • Self-calibration • ENSBox • Already have functionality • Mica2 Mote • Extend functionality to motes
Wireless Communication & Time Sync • Mote-Mote communication • Mica2 motes - onboard 433 MHz radio • BMAC • Transport/Routing Protocols • Mote-ENSBox communication • ENSBox – tethered mote • Time Synchronization • Flooding Time Synchronization Protocol (FTSP) • Time translation between ENSBox & Motes
Self-Calibration • How to determine positions of motes w/o any prior knowledge of location? • ENSBox acoustic source localization facilities • Equip motes with speakers • Process: • Schedule the motes to emit a signal • ENSBoxes localize signal • Localization results => mote locations
DOA-Based Localization • Determine Direction of Arrival (DOA) • Combine DOAs
LOCALIZATION, CONT’D • The Mica2 Motes now support: • Wireless Communication • Time synchronization • Self-calibration
Outline • Overview of system • Design • Simulation • Experimentation and Results
Goals of Simulation • Modeling System • Rapid Simulation • Controllable • Self-Calibration • Accuracy of localizing Motes w/ ENSBoxes
Simulating Localization • Field • 60x60 m, no obstructions • ENSBox placement and self-calibration • Errors in self-calibration • Gaussian errors: 4 cm for position, .96 degrees in orientation • Mote Localization • Sound wave • DOA and pseudo-likelihood maps
Outline • Overview of system • Design • Simulation • Experimentation and Results
Experiment #1 • Questions: • Frequency of call? • More ENSBoxes = better? • Scenario: • 20 motes randomly but uniformly generated in field • Initially: 5 ENSBoxes • Localize motes using different frequency calls • 1 KHz, 4 KHz, 10 KHz • Increase ENSBox count up to 8
Squares = ENSBoxes Blue = Motes Red = Estimated mote positions Call: 1 KHz EXPERIMENT #1, CONT’D
Experiment #1 Results • Table of mean errors ± standard deviation, units in cm • In this particular simulation setting: • Frequency of call, no effect • Increasing ENSBox count, no effect
Experiment #1 Extension • For 1 KHz call • localization results for 9 and 10 ENSBoxes • Indicates > 5 ENSBoxes != >accuracy
Experiment #2 • Question: • Least amount of ENSBoxes w/o losing accuracy? • Scenario: • Same 20 motes • 5 ENSBoxes to start from, 1 KHz calls from motes • ENSBoxes removed, motes localized with remaining boxes
Same 5 ENSBoxes, 20 motes. EXPERIMENT #2, CONT’D
EXPERIMENT #2 RESULTS, CONT’D • In simulation framework: • < 5 ENSBoxes = < accuracy • Suggests: • At least 5 ENSBoxes in the 60x60 m yields most accuracy w/ errors about 40±25 cm • Comparable to other mote acoustic localization schemes