Providing Locality Information to Smart Sensor Networks Tim Mead Supervisor: Charles Greif.
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Transcript of Providing Locality Information to Smart Sensor Networks Tim Mead Supervisor: Charles Greif.
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Providing Locality Information to Smart Sensor Networks
Tim Mead
Supervisor: Charles Greif
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Overview Intro / Aim Smart Sensor
Networks Ptolemy Smart Sensor
Hardware The Problem
Multidimensional Scaling
Findings Outcomes Future Work Conclusions
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Introduction and Aim
Project involves the task of calculating the relative locations of nodes in a Smart Sensor network, based on detected inter-node distances
A full simulation is devised, not just a specific implementation
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Smart Sensor Networks What is a Smart Sensor network?
An array of small, self-powered, processors with the ability to acquire data from a number of sources, as well as communicate with other nodes
Roots lie with the early ’90s Intel/Berkeley “Smart Dust” project
Recent research focused on efficiency and miniaturization
Research involves very recent technologies such as MEMS
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Deputy Dust!
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Smart Sensor Networks (con’t)
What can they be used for? The monitoring of data over a
distributed space: inside a home, over a factory, crops in a field
29-Palms Experiment. Six nodes dropped from a UAV, which were used to detect ground vehicles. UAV then flew past the nodes and queried them for their findings.
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Ptolemy
What is Ptolemy? A modeling and simulation suite, covering a number
of domains, including wireless
Why Model? Modeling allows for retargeting, reuse and formal
validation and verification
Why Simulate? Simulation reduces development time, by allowing
developers to simulate the entire system, without having to construct prototypes
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The need for Verified Software Why V&V?
Systems are ‘turn key’ – once they’re out in the field, they can’t easily be collected and reprogrammed.
Design and Verification of Embedded Systems
Thomas A. HenzingerUniversity of California, Berkeley
Collaboration between two research groups at Berkeley
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Ptolemy in Use
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Ptolemy and TinyOS Ptolemy was designed to support TinyOS What is TinyOS?
A lightweight, event-driven real-time OS Manages ad-hoc wireless communication Designed for smart sensors
TinyOS is implemented on the sensors using gcc/Atmel cross-compiler with nesC language (extension of C)
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Smart Sensor Hardware Processor:
Atmel processor with Flash memory Wireless RF interface:
310 / 916 MHz Sensor acquisition hardware:
Light, temperature, pressure, acceleration sensors
Real-time operating system: TinyOS
For location detection: Audio receiver / transmitter
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Commercial Smart Sensor Hardware: Crossbow
Crossbow ‘mote’
Audio sensor board
PC interface and programmer board
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Local Developments in SSN’s
Dr Peter Corke, with Qld CSIRO, has developed the ‘Flecks’
Similar to overseas units Runs TinyOS Uses Atmel processor
Cheaper, more readilyavailable
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The Problem: Positional Determination
Why do nodes want to know their position?
Much more powerful processing of acquired data by being able to correlate against position
Opens the door to smarter network routing algorithms, saving power and reducing errors
How can the position be determined? Systems such as GPS are too cost prohibitive Using a combination of high- and low-speed propagating
signals allows the inter-node distances to be determined
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Positional Determination (con’t)
What to with do with distances? Inter-node distances can be transformed into relative
positions
How to transform? Conventional methods utilise triangulation-like
systems, but limit themselves to 3 pieces of information
Multidimensional scaling utilises a greater body of information, to provide more reliable results, particularly when data is missing or corrupt
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Multidimensional Scaling
Began in the area of psychology, for grouping and correlation
Later adapted to statistics, for reducing dimensionality
Iteratively, works on minimising a loss function:
2/1
1
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1
2
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i
n
ijij
n
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ijijij
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S
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Multidimensional Scaling (con’t)
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Findings Ptolemy’s “building block” system
inadequate for complex decision-making and iterative logic
The wireless building blocks are well designed and extensible Allows for basic terrain simulation to be
easily added Proper simulation of audio effects, such as
reflection and diffraction requires complex FE methods
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Findings (con’t)
Multidimensional scaling: Requires a minimum of 3 known positions to
determine 2D positions Holds up well with missing distance data Handles spurious data
with appropriate weights
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Outcomes
Ptolemy simulation and model of nodes in an environment Simulates network communication Produces a matrix
of inter-nodedistances andweights
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Outcomes (con’t)
javaMDS Basic metric,
weighted MDScalculator with asimple graphicaloutput
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Future Work Immediate future:
Improvement of the Ptolemy model and synthesis of downloadable code (for Atmel)
Improved Data Acquisition More Efficient Communication
If we know where surround nodes are, we know how far away they are, so we can attenuate the power output accordingly.
More Efficient Packet Sending If we know where nodes are, any how far they can
communicate, we can determine the optimal communication pathway between two nodes.
Long-term future: Integration of T. Henzinger’s Verification and
Validation tools
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Conclusions A low-cost system for providing locality information
to Smart Sensor networks was devised Ptolemy: A visual programming environment in
which solutions for Smart Sensor networks can be developed
Simulation can be performed prior to production of code
V&V will be able to be performed in the same suite Extensions to all sorts of areas, such as Peter
Corke’s Fleck nodes
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Positional Determination (con’t)
Calculation of node position using triangulation utilises only three distances
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Positional Determination (con’t)
Calculation of node position using multidimensional scaling utilises all available data