ABSTRACT
We propose modeling environmental noise in order to efficiently and accurately simulate wireless packet delivery. We measure noise traces in many different environments and propose three algorithms to simulate noise from these traces. We evaluate applying these algorithms to signal-to-noise curves in comparison to existing simulation approaches used in EmStar, TOSSIM, and ns2. We measure simulation accuracy using the Kantorovich-Wasserstein distance on conditional packet delivery functions. We demonstrate that using a closest-fit pattern matching (CPM) noise model can capture complex temporal dynamics which existing approaches do not, increasing packet simulation fidelity by a factor of 2 for good links, a factor of 1.5 for bad links, and a factor of 5 for intermediate links. As our models are derived from real-world traces, they can be generated for many different environments.
- Sensor network emulator/simulator/debugger. http://www.cshcn.umd.edu/research/atemu/.Google Scholar
- The Network Simulator - ns-2. http://www.isi.edu/nsnam/ns/.Google Scholar
- TinyOS 2.0. http://www.tinyos.net/tinyos-2.x/.Google Scholar
- TOSSIM 2.x. http://www.tinyos.net/tinyos-2.x/.Google Scholar
- A. Cerpa, N. Busek, and D. Estrin. Scale: A tool for simple connectivity assessment in lossy environments. Technical Report 0021, Sept. 2003.Google Scholar
- A. Cerpa, J. L. Wong, M. Potkonjak, and D. Estrin. Temporal properties of low power wireless links: Modeling and implications on multi-hop routing. In Proceedings of the Sixth ACM International Symposium on Mobile Ad Hoc Networking and Computing (MOBIHOC'05), 2005. Google ScholarDigital Library
- D. Conner and J. Hammond. Modeling of stochastic system inputs having prescribed distribution and covariance functions. In Applied Mathematical Modeling, volume 3, 1979.Google Scholar
- R. Deutsch. Nonlinear Transformations of Random Processes. Prentice-Hall, 1962.Google Scholar
- D. Ganesan, B. Krishnamachari, A. Woo, D. Culler, D. Estrin, and S. Wicker. An empirical study of epidemic algorithms in large scale multihop wireless networks. UCLA Computer Science Technical Report UCLA/CSD-TR 02-0013, 2002.Google Scholar
- L. Girod, T. Stathopoulos, N. Ramanathan, J. Elson, D. Estrin, E. Osterweil, and T. Schoellhammer. A system for simulation, emulation, and deployment of heterogeneous sensor networks. In Proceedings of the 2nd international conference on Embedded networked sensor systems (SenSys), pages 201--213, New York, NY, USA, 2004. ACM Press. Google ScholarDigital Library
- C. Givens and R. Shortt. A class of wasserstein metrics for probability distributions. In Michigan Math. J., volume 31, pages 231--240, 1884.Google Scholar
- H. Hashemi. The Indoor Radio Propagation Channel. Proceedings of the IEEE., 81(7), July 1993.Google ScholarCross Ref
- G. Johnson. Constructions of particular random process. In Proceedings of the IEEE, volume 82, pages 270--285, 1994.Google Scholar
- J. Johnson. Thermal agitation of electricity in conductors. Physics Review, 32(97), 1928.Google Scholar
- P. Levis, N. Lee, M. Welsh, and D. Culler. TOSSIM: Simulating large wireless sensor networks of tinyos motes. In Proceedings of the First ACM Conference on Embedded Networked Sensor Systems (SenSys),2003. Google ScholarDigital Library
- S. Lin, T. He, J. Zhang, G. Zhou, L. Gu, and J. A. Stankovic. Atpc: Adaptive transmission power control for wireless sensor networks. 2006.Google Scholar
- H. Nyquist. Thermal agitation of electric charge in conductors. Physics Review, 32(110), 1928.Google Scholar
- Y. Rubner, C. Tomasi, and L. J. Guibas. A metric for distributions with applications to image databases. In Proceedings of the 1998 IEEE International Conference on Computer Vision, pages 59--66, 1998. Google ScholarDigital Library
- S. Y. Seidel and T. S. Rappaport. 914 MHz path loss prediction models for indoor wireless communications in multifloored buildings. IEEE Transactions on Antennas and Propagation., 40(2), Feb 1992.Google Scholar
- D. Son, B. Krishnamachari, and J. Heidemann. Experimental study of concurrent transmission in wireless sensor networks. In Proceedings of the Fourth ACM Conference on Embedded Networked Sensor Systems (SenSys), 2006. Google ScholarDigital Library
- K. Srinivasan, P. Dutta, A. Tavakoli, and P. Levis. Understanding the causes of packet delivery success and failure in dense wireless sensor networks. In Technical report SING-06-00, Stanford, CA, 2006.Google Scholar
- B. L. Titzer, D. K. Lee, and J. Palsberg. Avrora: scalable sensor network simulation with precise timing. In IPSN '05: Proceedings of the 4th international symposium on Information processing in sensor networks, page 67, Piscataway, NJ, USA, 2005. IEEE Press. Google ScholarDigital Library
- M. Tognarelli, J. Zhao, and A. Kareem. Equivalent statistical cubicization: A frequency domain approach for nonlinearities in both system and forcing function. In Journal of Engineering Mechanics, ASCE, volume 123, 1997.Google Scholar
- J. Zhao and R. Govindan. Understanding packet delivery performance in dense wireless sensor networks. In Proceedings of the First International Conference on Embedded Network Sensor Systems, 2003. Google ScholarDigital Library
- M. Zuniga and B. Krishnamachari. Analyzing the transitional region in low power wireless links. In First IEEE International Conference on Sensor and Ad hoc Communications and Networks (SECON), 2004.Google ScholarCross Ref
Index Terms
- Improving wireless simulation through noise modeling
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