CS HONORS UNDERGRADUATE RESEARCH PROGRAM - PROJECT PROPOSAL Tingyu Thomas Lin Advisor: Professor Deborah Estrin January 25, 2007

PROJECT PROPOSAL Acoustic Localization Acoustic Embedded Networked Sensing Box (ENSBox) Expanding the capabilities of the ENSBox Using motes

OUTLINE Acoustic Localization and the ENSBox Expanding the ENSBox – adding motes Methodology and milestones Summary

OUTLINE Acoustic Localization and the ENSBox Expanding the ENSBox – adding motes Methodology and milestones Summary

ACOUSTIC LOCALIZATION Why acoustic sensing platform? Scientific Tracking calls of birds, wolves, other animals Military Tracking vehicle and personnel movements Commercial Smart spaces Distributed Sensing Networks Low-cost nodes Scalability May need to cover large area

TYPICAL IMPLEMENTATION OF ACOUSTIC PLATFORM Source: L. Girod et al. The Design and Implementation of a Self-Calibrating Distributed Acoustic Sensing Platform. SenSys’06, November 1-3, 2006, Boulder, Colarado, USA.

EXISTING DISTRIBUTED ACOUSTIC SENSING PLATFORMS Heavily Optimized e.g. Countersniper system, troop tracking sensing platforms Not ideal as a prototyping platform General purpose acoustic sensing platforms Off-the-shelf solutions Doesn’t scale easily WINS NG, VanGo, and other Berkeley/Telos Mote based systems Generally, doesn’t provide tight time synchronization Tight constraints on resources ENSBox

ENSBOX Source: L. Girod et al. The Design and Implementation of a Self-Calibrating Distributed Acoustic Sensing Platform. SenSys’06, November 1-3, 2006, Boulder, Colarado, USA.

ENSBOX Acoustic Source Localization At node If source is “far field,” sound waves are planar If not, discard information Approximate bearing of source Using difference in time of arrivals at the microphones Relative positions of microphones known and fixed In the network Approximate location of source Using bearing estimates of several nodes Using difference in time of arrivals at nodes Possible through tight time synchronization Possible only if nodes know their relative locations How do they know? Through Self Localization

ENSBOX Acoustic Self Localization At node Onboard speaker, emits a calibration tone Other nodes: estimates bearing to the node Each node takes turns In the network Reconcile bearing estimates Determine relative positions of nodes

ENSBOX Internal workings 400 MHz Intel PXA255 w/ 64MB RAM On-board 32MB flash Dual slot PCMCIA interface wireless Digigram VXPocket440 four-channel sampling card Runs Linux Modifications to kernel and Digigram firmware Support accurate timestamping Custom circuit board Battery powered

ENSBOX Functional Performance Very accurate About 5 cm 2D positional error and 1.5 degree average orientation error partially obstructed 80x50m field About 5x better than the next best solution General purpose Enables rapid prototyping Self calibrating system

OUTLINE Acoustic Localization and the ENSBox Expanding the ENSBox – adding motes Methodology and milestones Summary

MOTES Components Single microphone (vs. 4 for ENSBox) Speaker (for calibration tone) Severely limited resources Runs on TinyOS Radio for networking

MOTES Proposed Functionality Acoustic Self Localization Smaller and cheaper Can easily add motes around points of interests Additional nodes => Denser network Better detection of events More accuracy to estimates Increased robustness in face of obstructions Additional features to network Early warning for ENSBox nodes Highly unlikely doable in allotted time

COMPARISON: WITH VS. WITHOUT MOTES Without motesWith motes

OUTLINE Acoustic Localization and the ENSBox Expanding the ENSBox – adding motes Methodology and milestones Summary

INCREMENTAL DEVELOPMENT Phase 1: Self Localization of motes (4 weeks) Have ENSBox locate motes Initially constrain to single mote and 2D Determine best mote configuration find a robust calibration signal Find optimal mote placements Phase 2: Interface motes with ENSBoxes Have them talk so ENSBoxes knows where motes are Phase 3: Integrate motes into system Motes assist in estimating acoustic sources Experiment, test and analyze the impact of motes on the system

MILESTONES By Project Checkpoint: Phase 1 complete Self Localization of motes Deliverable: Analysis of optimal mote configuration Phase 2 under way By Project End: Phase 2 complete Motes and ENSBoxes talking Deliverable: Discussion on issues and solutions encountered Phase 3 complete Motes assist in source localization Deliverable: Quantitative analysis of impact motes have on the system

POTENTIAL DIFFICULTIES Phase 1 – finding optimal mote configurations Testing and analyzing data might take longer than expected, but still within the first quarter Push back Phase 2 and 3 if necessary Phase 2 – Integrating motes into network Coding intensive phase Depending on how swiftly the coding goes, may take shorter or longer (most likely longer) than expected If necessary, drop Phase 3 Phase 3 – Using motes to find sources Coding intensive and a lot of data analysis Drop Phase 3 if necessary

POTENTIAL DIFFICULTIES As progress is made, a better feel of what’s feasible will develop Project goals and scope will change

OUTLINE Acoustic Localization and the ENSBox Expanding the ENSBox – adding motes Methodology and milestones Summary

SUMMARY Motes have the potential of improving ENSBox Motes are cheap Easier to deploy and in greater numbers than the larger and more expensive ENSBoxes Denser network => More information in system => better estimates