cs honors undergraduate research program - project proposal tingyu thomas lin advisor: professor...
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CS HONORS UNDERGRADUATE RESEARCH PROGRAM - PROJECT PROPOSALTingyu 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
802.11 wireless Digigram VXPocket440 four-channel sampling card
Runs Linux 2.6.10 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 MOTESWithout motes With 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