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The Computational Power of Beeps

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Distributed Computing (DISC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9363))

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Abstract

We study the quantity of computational resources (state machine states and/or probabilistic transition precision) needed to solve specific problems in a single hop network where nodes communicate using only beeps. We begin by focusing on randomized leader election. We prove a lower bound on the states required to solve this problem with a given error bound, probability precision, and (when relevant) network size lower bound. We then show the bound tight with a matching upper bound. Noting that our optimal upper bound is slow, we describe two faster algorithms that trade some state optimality to gain efficiency. We then turn our attention to more general classes of problems by proving that once you have enough states to solve leader election with a given error bound, you have (within constant factors) enough states to simulate correctly, with this same error bound, a logspace TM with a constant number of unary input tapes: allowing you to solve a large and expressive set of problems. These results identify a key simplicity threshold beyond which useful distributed computation is possible in the beeping model.

S. Gilbert—Supported in part by NUS FRC T1-251RES1404.

C. Newport—Supported in part by NSF grant CCF 1320279.

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References

  1. Afek, Y., Alon, N., Bar-Joseph, Z., Cornejo, A., Haeupler, B., Kuhn, F.: Beeping a maximal independent set. In: Peleg, D. (ed.) Distributed Computing. LNCS, vol. 6950, pp. 32–50. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  2. Afek, Y., Alon, N., Bar-Joseph, Z., Cornejo, A., Haeupler, B., Kuhn, F.: Beeping a maximal independent set. Distributed Computing 26(4), 195–208 (2013)

    Article  MATH  Google Scholar 

  3. Afek, Y., Alon, N., Barad, O., Hornstein, E., Barkai, N., Bar-Joseph, Z.: A biological solution to a fundamental distributed computing problem. Science 331(6014), 183–185 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  4. Angluin, D., Aspnes, J., Diamadi, Z., Fischer, M.J., Peralta, R.: Computation in networks of passively mobile finite-state sensors. Distributed Computing 18(4), 235–253 (2006)

    Article  MATH  Google Scholar 

  5. Angluin, D., Aspnes, J., Eisenstat, D.: Stably computable predicates are semilinear. In: Proceedings of the Symposium on Principles of Distributed Computing (PODC), pp. 292–299 (2006)

    Google Scholar 

  6. Angluin, D., Aspnes, J., Eisenstat, D.: Fast computation by population protocols with a leader. Distributed Computing 21(3), 183–199 (2008)

    Article  MATH  Google Scholar 

  7. Angluin, D., Aspnes, J., Eisenstat, D.: A simple population protocol for fast robust approximate majority. Distributed Computing 21(2), 87–102 (2008)

    Article  MATH  Google Scholar 

  8. Angluin, D., Aspnes, J., Eisenstat, D., Ruppert, E.: The computational power of population protocols. Distributed Computing 20(4), 279–304 (2007)

    Article  MATH  Google Scholar 

  9. Chatzigiannakis, I., Spirakis, P.G.: The dynamics of probabilistic population protocols. In: Taubenfeld, G. (ed.) DISC 2008. LNCS, vol. 5218, pp. 498–499. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  10. Cornejo, A., Kuhn, F.: Deploying wireless networks with beeps. In: Lynch, N.A., Shvartsman, A.A. (eds.) DISC 2010. LNCS, vol. 6343, pp. 148–162. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  11. Degesys, J., Nagpal, R.: Towards desynchronization of multi-hop topologies. In: Proceedings of the International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2008) (2008)

    Google Scholar 

  12. Degesys, J., Rose, I., Patel, A., Nagpal, R.: Desync: self-organizing desynchronization and tdma on wireless sensor networks. In: Proceedings of the International Conference on Information Processing in Sensor Networks (2007)

    Google Scholar 

  13. Emek, Y., Wattenhofer, R.: Stone age distributed computing. In: Proceedings of the Symposium on Principles of Distributed Computing (PODC) (2013)

    Google Scholar 

  14. Förster, K.-T., Seidel, J., Wattenhofer, R.: Deterministic leader election in multi-hop beeping networks - (extended abstract). In: Kuhn, F. (ed.) DISC 2014. LNCS, vol. 8784, pp. 212–226. Springer, Heidelberg (2014)

    Google Scholar 

  15. Gilbert, S., Newport, C.: The computational power of beeps. Full version available online at. http://people.cs.georgetown.edu/~cnewport/pubs/Beeps-Full.pdf (arXiv)

  16. Lenzen, C., Lynch, N., Newport, C., Radeva, T.: Trade-offs between selection complexity and performance when searching the plane without communication. In: Proceedings of the Symposium on Principles of Distributed Computing (PODC) (2014)

    Google Scholar 

  17. Minsky, M.L.: Computation: finite and infinite machines. Prentice-Hall (1967)

    Google Scholar 

  18. Motskin, A., Roughgarden, T., Skraba, P., Guibas, L.J.: Lightweight coloring and desynchronization for networks. In: Proceedings of the of the Conference on Computer Communication (INFOCOM) (2009)

    Google Scholar 

  19. Navlakha, S., Bar-Joseph, Z.: Distributed information processing in biological and computational systems. Communications of the ACM 58(1), 94–102 (2014)

    Article  Google Scholar 

  20. Scott, A., Jeavons, P., Xu, L.: Feedback from nature: an optimal distributed algorithm for maximal independent set selection. In: Proceedings of the Symposium on Principles of Distributed Computing (PODC) (2013)

    Google Scholar 

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Correspondence to Calvin Newport .

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Gilbert, S., Newport, C. (2015). The Computational Power of Beeps. In: Moses, Y. (eds) Distributed Computing. DISC 2015. Lecture Notes in Computer Science(), vol 9363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48653-5_3

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  • DOI: https://doi.org/10.1007/978-3-662-48653-5_3

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-48652-8

  • Online ISBN: 978-3-662-48653-5

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