Ye Wen
ABSTRACT
Software development for sensor network is made difficult by resource constrained sensor devices, distributed system complexity, communication unreliability, and high labor cost. Simulation, as a useful tool, provides an affordable way to study algorithmic problems with flexibility and controllability. However, in exchange for speed simulation often trades detail that ultimately limits its utility. In this paper, we propose a new development paradigm, simulation-based augmented reality, in which simulation is used to enhance development on physical hardware by seamlessly integrating a running simulated network with a physical deployment in a way that is transparent to each. The advantages of such an augmented network include the ability to study a large sensor network with limited hardware and the convenience of studying a part of the physical network with simulation's debugging, profiling and tracing capabilities. We implement the augmented reality system based on a sensor network simulator with high fidelity and high scalability. Key to the design are "super" sensor nodes which are half virtual and half physical that interconnect simulation and physical network with fine-grained traffic forwarding and accurate time synchronization. Our results detail the overhead associated with integrating live and simulated networks and the timing accuracy between virtual and physical parts of the network. We also discuss various application scenarios for our system.
- Philip Levis, Nelson Lee, Matt Welsh, and David Culler. TOSSIM: Accurate and Scalable Simulation of Entire TinyOS Applications. ACM Conference on Embedded Networked Sensor Systems, November 2003. Google Scholar
Digital Library
- Sung Park, Andreas Savvides, and Mani B. Srivastava. SensorSim: a simulation framework for sensor networks. ACM International workshop on Modeling, analysis and simulation of wireless and mobile systems, pages 104--111, 2000. Google ScholarDigital Library
- Sameer Sundresh, Wooyoung Kim, and Gul Agha. SENS: A Sensor, Environment and Network Simulator. The IEEE 37th Annual Simulation Symposium, 2004. Google ScholarDigital Library
- Ben L. Titzer, Daniel K. Lee, and Jens Palsberg. Avrora: Scalable Sensor Network Simulation with Precise Timing. The Fourth International Symposium on Information Processing in Sensor Networks, April 2005. Google ScholarDigital Library
- Jonathan Polley, Dionysys Blazakis, Jonathan McGee, Dan Rusk, and John S. Baras. ATEMU: A Fine-grained Sensor Network Simulator. IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004.Google Scholar
- Shih-Hsiang Lo, Jiun-Hung Ding, Sheng-Je Hung, Jin-Wei Tang, and Wei-Lun Tsai. SEMU: A Framework of Simulation Environment for Wireless Sensor Networks with co-simulation model. In the Proceedings of International Conference on Grid and Pervasive Computing (GPC), Lecture Notes in Computer Science (LNCS), May 2007. France.Google ScholarCross Ref
- Lewis Girod, Jeremy Elson, Alberto Cerpa, Thanos Stathopoulos, Nithya Ramanathan, and Deborah Estrin. EmStar: a Software Environment for Developing and Deploying Wireless Sensor Networks. USENIX Technical Conference, 2004. Google ScholarDigital Library
- Lewis Girod, Thanos Stathopoulos, Nithya Ramanathan, Jeremy Elson, Deborah Estrin, Eric Osterweil, and Tom Schoellhammer. A System for Simulation, Emulation, and Deployment of Heterogeneous Sensor Networks. ACM Conference on Embedded Networked Sensor Systems, November 2004. Google ScholarDigital Library
- Ye Wen, Rich Wolski, and Greg Moore. DiSenS: Scalable Distributed Sensor Network Simulation. In Proceedings of ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP 07), March 2007. San Jose, CA. Google ScholarDigital Library
- Alexander Kroeller, Dennis Pfisterer, Carsten Buschmann, Sandor P. Fekete, and Stefan Fischer. Shawn: A new approach to simulating wireless sensor networks. eprint arXiv:cs/0502003, February 2005.Google Scholar
- ElMoustapha Ould-Ahmed-Vall, George F. Riley, Bonnie S. Heck, and Dheeraj Reddy. Simulation of Large-Scale Sensor Networks Using GTSNetS. In Proceedings of the 13th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS'05), 2005. Google ScholarDigital Library
- J. Barbancho, F. J. Molina, C. Len, J. Ropero, and A. Barbancho. OLIMPO, An Ad-Hoc Wireless Sensor Network Simulator for Optimal SCADA-Applications. Communication Systems and Networks (CSN 2004), 450, September 2004.Google Scholar
- D. Watson and M. Nesterenko. Mule: Hybrid Simulator for Testing and Debugging Wireless Sensor Networks. In Workshop on Sensor and Actor Network Protocols and Applications, August 2004.Google Scholar
- Ohio State University, Kansei: Sensor Testbed for At-Scale Experiments. Poster, 2nd International TinyOS Technology Exchange, Berkeley, February 2005.Google Scholar
- Rolf R. Hainich. The End of Hardware: A Novel Approach to Augmented Reality. BookSurge Publishing, 2006. Google ScholarDigital Library
- Ye Wen and Rich Wolski. S2DB: A Novel Simulation-Based Debugger for Sensor Network Applications. In the Proceedings of 6th Annual ACM Conference on Embedded Software (EmSoft 06), October 2006. Seoul, South Korea. Google ScholarDigital Library
- Mote hardware platform. http://www.tinyos.net/scoop/special/hardware.Google Scholar
- Jason Hill, Robert Szewczyk, Alec Woo, Seth Hollar, David Culler, and Kristofer Pister. System architecture directions for network sensors. International Conference on Architectural Support for Programming Languages and Operating Systems, October 2000. Google ScholarDigital Library
- Kirk Schloegel, George Karypis, and Vipin Kumar. Graph Partitioning for High Performance Scientific Simulations. Draft to be included in CRPC Parallel Computing Handbook, Morgan Kaufmann, September 2000.Google Scholar
- Horst D. Simon. Partitioning of Unstructured Problems for Parallel Processing. Computing Systems in Engineering, 2:135--148, 1991.Google ScholarCross Ref
- Alex Pothen. Graph partitioning algorithms with applications to scientific computing. Parallel Numerical Algorithms, pages 323--368, 1997. Kluwer.Google ScholarCross Ref
- Bruce Hendrickson and Robert Leland. The Chaco User's Guide: Version 2.0. Technical Report SAND94--2692, Sandia National Lab, 1994.Google Scholar
- F. A. Tobagi and L. Kleinrock. Packet switching in radio channels: Part II-The hidden terminal problem in carrier sense multiple-access and the busy-tone solution. IEEE Transactions on Communications, COM-23:1417--1433, 1975.Google ScholarCross Ref
- Alberto Cerpa, Jennifer L. Wong, Louane Kuang, Miodrag Potkonjak, and Deborah Estrin. Statistical Model of Lossy Links in Wireless Sensor Networks. In the ACM/IEEE Fourth International Conference on Information Processing in Sensor Networks (IPSN'05), April 2005. Los Angeles, California. Google ScholarDigital Library
- Gang Zhou, Tian He, Sudha Krishnamurthy, and John A. Stankovic. Impact of radio irregularity on wireless sensor networks. In Proceedings of the 2nd international conference on Mobile systems, applications, and services (MobiSYS'04), 2004. Google ScholarDigital Library
- Jerry Zhao and Ramesh Govindan. Understanding packet delivery performance in dense wireless sensor networks. In Proceedings of the 1st international conference on Embedded networked sensor systems (SenSys'03), 2003. Google ScholarDigital Library
- Olaf Landsiedel, Klaus Wehrle, and Stefan Gotz. Accurate Prediction of Power Consumption in Sensor Networks. In Proceedings of The Second IEEE Workshop on Embedded Networked Sensors (EmNetS-II), May 2005. Sydney, Australia. Google ScholarDigital Library
- Victor Shnayder, Mark Hempstead, Bor-rong Chen, Geoff Werner-Allen, and Matt Welsh. Simulating the Power Consumption of Large-Scale Sensor Network Applications. In Proceedings of the Second ACM Conference on Embedded Networked Sensor Systems (SenSys'04), November 2004. Baltimore, MD. Google ScholarDigital Library
- Victor Shnayder, Mark Hempstead, Bor-rong Chen, and Matt Welsh. PowerTOSSIM: Efficient Power Simulation for TinyOS Applications. In Proceedings of the Second ACM Conference on Embedded Networked Sensor Systems (SenSys'04), November 2004. Baltimore, MD.Google Scholar
- K. C. Syracuse and W. Clark. A statistical approach to domain performance modeling for oxyhalide primary lithium batteries. In Proceedings of Annual Battery Conference on Applications and Advances, January 1997.Google ScholarCross Ref
- L. Benini, G. Castelli, A. Macii, E. Macii, M. Poncino, and R. Scarsi. A discrete-time battery model for high-level power estimation. In Proceedings of Design, Automation and Test in Europe, 2000. Google ScholarDigital Library
- D. Rakhmatov and S. Vrudhula. Time-to-failure estimation for batteries in portable electronic systems. In Proceedings of the International Symposium on Low Power Electronics and Design, August 2001. Google ScholarDigital Library
- D. Linden and T. B. Reddy. Handbook of Batteries(3rd edition). McGraw-Hill, 2002.Google Scholar
- Samuel T. King, George W. Dunlap, and Peter M. Chen. Debugging Operating Systems with Time-Traveling Virtual Machines. In the Proceedings of USENIX Annual Technical Conference 2005, April 2005. Anaheim, CA. Google ScholarDigital Library
- Sudarshan M. Srinivasan, Srikanth Kandula, Christopher R. Andrews, and Yuanyuan Zhou. Flashback: A Lightweight Extension for Rollback and Deterministic Replay for Software Debugging. In the Proceedings of USENIX Annual Technical Conference 2004, June 2004. Boston, MA. Google ScholarDigital Library
- Stargate: a platform X project. http://platformx.sourceforge.net/.Google Scholar
- Mark Carson and Darrin Santay. NIST Net -- A Linux-based Network Emulation Tool. In the Proceedings of ACM SIGCOMM special issue of Computer Communication Review, 2003. Google ScholarDigital Library
- Stephen Hemminger. Network Emulation with NetEm. In the Proceedings of Linux Conference AU, April. 2005.Google Scholar
- Luigi Rizzo. Dummynet: a simple approach to the evaluation of network protocols. In ACM Computer Communication Review, 27(1):31--41, 1997. Google ScholarDigital Library
- Network Emulation with the NS Simulator. http://www.isi.edu/nsnam/ns/ns-emulation.html.Google Scholar
- NS-2 network simulator. http://www.isi.edu/nsnam/ns/.Google Scholar
- QEMU: A Generic and Open Source Processor Emulator. http://fabrice.bellard.free.fr/qemu/.Google Scholar
- Richard M. Fujimoto. Time warp on a shared memory multiprocessor. Transactions of the Society for Computer Simulation International, 6(3):211--239, 1989. Google ScholarDigital Library
Index Terms
- Simulation-based augmented reality for sensor network development
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