A wireless ad-hoc distributed computing environment
Harnesses and aggregates low computing power of
geographically-concentrated mobile devices – even
sensors in sensor networks
Mobile
phones
Berkeley Mote
sensors
Suitable for execution of Cellular Automata - based
applications/ simulations
Provides a bounded region of euclidean space to the
application – a virtual lattice V
A fixed immobile node I forms the origin of the lattice
Nodes calculate their location relative to I (using
algorithms in [1] )
Lattice
origin I
Participant
nodes
Based on location, they now form
a 2-dimensional, physical lattice P
P is logically re-arranged to form
a virtual lattice V with dimension,
size, etc. based on application
requirements
The application is aware only of V; P is transparent
Accurate timing of communication is often critical to
the simulation
A neighbor in V is not necessarily a neighbor in P – thus
messages to neighbors in V may not reach them
simultaneously, causing erroneous simulation results…
The communication sub-system ensures all messages
are processed by nodes only after the maximum
possible propagation time – resolving the timing issue
Upon completion of
lattice formation, the
application execution is
initiated
Formation of lattice(s) in WAdL
a. Unorganized mobile nodes
b. A physical lattice - L is
formed
c. L is logically re-mapped to
form a 3-D virtual lattice - V
Mobility of participating
devices and device
failure can lead to the
development of holes in
the lattice
Strategies helpful in tackling node mobility / failure
Neighbors working for failed / moving devices
Multiple devices responsible for a lattice vertex –
performing tasks in parallel so that one of the backup
devices take over when the primary device fails
Physical obstructions might prevent
direct communication between
neighbors in P
Use of a simple routing
mechanism - utilizing devices
adjacent to the obstruction, can
help resolve this issue.
Many physical phenomena
have complex analytical
solutions - Analog models
can be used to predict their
behavior
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Operation of Cellular Automata
Some analog simulations can be modeled using
Cellular Automata (CA)
CA are dynamic - discrete in space and time
Behavior completely specified in terms of local
relations
Lattice Computer can execute CA-based
simulations
Low computational demand processing elements
Represents euclidean space where phenomenon
unfolds
CA used in
modeling a
snowflake
Vishakha Gupta and
Current affiliations : (MSIN, CMU)
Gaurav Mathur, BITS-Pilani, India
(Intel, India)
Mentor – Dr. Anil M. Shende
(Roanoke College)
Extremely cheap computing grids
can be formed using clusters of
cheap Mote-like devices / sensors
Message routing in a wireless
network
Providing load-balancing and/or
fault tolerance in a wireless
network
Some applications might need a
structured network – WAdL can
help provide structure to an
otherwise unstructured network
We demonstrate an application
based on simplified CFD model
Computes the ideal lift and
drag on an airplane wing
Virtual wing “flies” in the
virtual lattice generated by
WAdL
Aerofoil and direction of
lift and drag
Virtual ‘flight’ of the
simulated wing
Obtained simulation
results are identical to
analytical results
Uses minimal network
bandwidth – causing
negligible disruption to
existing network traffic
Change in Lift generated by the
Virtual Wing due to
Decreasing Density in V
(plotted from simulation data)
Bandwidth Utilization in
WAdL with 1000 nodes
Linking multiple, geographically remote
WAdLs together to form a single
WAdL – providing more euclidean
space for simulation
Routing messages around physical
obstructions in a WAdL
Using a WAdL for routing and
addressing network congestion in a
wireless setting
Distributed clock synchronization
[1] Anil M. Shende, Vishakha Gupta, Gaurav Mathur. “Lattice formation in a
Wireless Ad-hoc Lattice computer (WAdL)”. AlgorithmS for Wireless and
mobile Networks (A-SWAN), August 2004.
[2] D. S. Rajan, J. Case, A. M. Shende. “Optimally representing euclidean
space discretely for analogically simulating physical phenomena”. In
Foundations of Software Technology and Theoretical Computer Science,
December 1990. (Lecture Notes in Computer Science)
[3] Donald Greenspan. “Deterministic Computer Physics”. International Jounal
of Theoretical Physics, 1982.
[4] L. Wilson A. Wadaa, S. Olariu. “On training a sensor network”. In
Proceedings of the International Parallel & Distributed Processing
Symposium, page 220, 2003. (Workshop on Mobile Adhoc Networks)
[5] C. L. Barrett, S. J. Eidenbenz, L. Kroc, M. Marathe, J. P. Smith.
“Parametric probabilistic sensor network routing”. Proceedings of the 2nd
ACM international conference on Wireless sensor networks and
applications, page 122-131, 2003.
[6] Factual data for lift and drag on an aerofoil.http://www.centennialoight.gov.
[7] Network simulator 2 (ns-2). http://www.isi.edu/nsnam/ns/.