The Architecture

1. The Architecture • A wireless ad-hoc distributed computing environment • Harnesses and aggregates low computing power of geographically-concentrated mobile devices – even sensors in sensor networks • Suitable for execution of Cellular Automata - based applications/ simulations • Provides a bounded region of euclidean space to the application – a virtual lattice V Mobile phones Berkeley Mote sensors

2. A fixed immobile node I forms the origin of the lattice • Nodes calculate their location relative to I (using algorithms in [1] ) • 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 Lattice origin I Participant nodes • 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…

3. The communication sub-system ensures all messages are processed by nodes only afterthe maximum possible propagation time – resolving the timing issue • Upon completion of lattice formation, the application execution is initiated • Mobility of participating devices and device failure can lead to the development of holes in the lattice • Formation of lattice(s) in WAdL • Unorganized mobile nodes • A physical lattice - L is formed • L is logically re-mappedto form a 3-D virtual lattice - V

4. 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.

5. 0 1 0 Time + 1 0 0 0 0 1 1 1 0 1 1 0 0 1 0 0 1 1 1 1 0 0 1 0 0 1 0 0 0 0 1 0 Related Work • Many physical phenomenahave complex analytical solutions - Analog modelscan be used to predict their behavior 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 localrelations • Lattice Computercan execute CA-based simulations • Low computational demand processing elements • Represents euclidean space where phenomenon unfolds CA used in modeling a snowflake

6. W reless Lattice Vishakha Gupta and Current affiliations : (MSIN, CMU)

7. Ad-hoc Computer Gaurav Mathur, BITS-Pilani, India (Intel, India) Mentor – Dr. Anil M. Shende (Roanoke College)

8. Usage Scenarios • 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

9. The Application • We demonstrate an application based on simplified CFD model • Computes the ideallift 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

10. Simulation Results • Obtained simulation results areidentical to analyticalresults • 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

11. Future Work • 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

12. References [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/.