Phase transitions in self-organising sensor networks

Mark Foreman; Mikhail Prokopenko; Peter Wang

doi:10.1007/978-3-540-39432-7_84

Phase transitions in self-organising sensor networks

Mark Foreman, Mikhail Prokopenko, Peter Wang

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

17 Citations (Scopus)

Abstract

In this paper we consider a multi-cellular sensing and communication network, embedded in an ageless aerospace vehicle, that is expected to detect and react to impact location, intensity and damage over a wide range of impact energies. In particular, we investigate self-organisation of impact boundaries enclosing critically damaged areas, and measure their spatiotemporal robustness. The presented quantitative information-theoretic techniques clearly identify phase transitions, separating chaotic dynamics from ordered and robust patterns.

Original language	English
Title of host publication	Advances in Artificial Life
Editors	D. Hutchison, T. Kanade, J. Kittler, J.M. Kleinberg, F. Mattern, J.C. Mitchell, M. Naor, C. Pandu Rangan, B. Steffen, D. Terzopoulos, D. Tygar, G. Weikum
Place of Publication	Berlin; Heidelberg
Publisher	Springer, Springer Nature
Pages	781-791
Number of pages	11
Volume	2801
ISBN (Electronic)	9783540394327
ISBN (Print)	9783540200574
DOIs	https://doi.org/10.1007/978-3-540-39432-7_84
Publication status	Published - 2003
Externally published	Yes
Event	7th European Conference on Artificial Life, ECAL - 2003 - Dortmund, Germany Duration: 14 Sept 2003 → 17 Sept 2003

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Nature
Volume	2801
ISSN (Print)	0302-9743

Other

Other	7th European Conference on Artificial Life, ECAL - 2003
Country/Territory	Germany
City	Dortmund
Period	14/09/03 → 17/09/03

Access to Document

10.1007/978-3-540-39432-7_84

Cite this

Foreman, M., Prokopenko, M., & Wang, P. (2003). Phase transitions in self-organising sensor networks. In D. Hutchison, T. Kanade, J. Kittler, J. M. Kleinberg, F. Mattern, J. C. Mitchell, M. Naor, C. Pandu Rangan, B. Steffen, D. Terzopoulos, D. Tygar, & G. Weikum (Eds.), Advances in Artificial Life (Vol. 2801, pp. 781-791). (Lecture Notes in Computer Science; Vol. 2801). Springer, Springer Nature. https://doi.org/10.1007/978-3-540-39432-7_84

Foreman, Mark ; Prokopenko, Mikhail ; Wang, Peter. / Phase transitions in self-organising sensor networks. Advances in Artificial Life. editor / D. Hutchison ; T. Kanade ; J. Kittler ; J.M. Kleinberg ; F. Mattern ; J.C. Mitchell ; M. Naor ; C. Pandu Rangan ; B. Steffen ; D. Terzopoulos ; D. Tygar ; G. Weikum. Vol. 2801 Berlin; Heidelberg : Springer, Springer Nature, 2003. pp. 781-791 (Lecture Notes in Computer Science).

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editor = "D. Hutchison and T. Kanade and J. Kittler and J.M. Kleinberg and F. Mattern and J.C. Mitchell and M. Naor and {Pandu Rangan}, C. and B. Steffen and D. Terzopoulos and D. Tygar and G. Weikum",

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Foreman, M, Prokopenko, M & Wang, P 2003, Phase transitions in self-organising sensor networks. in D Hutchison, T Kanade, J Kittler, JM Kleinberg, F Mattern, JC Mitchell, M Naor, C Pandu Rangan, B Steffen, D Terzopoulos, D Tygar & G Weikum (eds), Advances in Artificial Life. vol. 2801, Lecture Notes in Computer Science, vol. 2801, Springer, Springer Nature, Berlin; Heidelberg, pp. 781-791, 7th European Conference on Artificial Life, ECAL - 2003, Dortmund, Germany, 14/09/03. https://doi.org/10.1007/978-3-540-39432-7_84

Phase transitions in self-organising sensor networks. / Foreman, Mark; Prokopenko, Mikhail; Wang, Peter.
Advances in Artificial Life. ed. / D. Hutchison; T. Kanade; J. Kittler; J.M. Kleinberg; F. Mattern; J.C. Mitchell; M. Naor; C. Pandu Rangan; B. Steffen; D. Terzopoulos; D. Tygar; G. Weikum. Vol. 2801 Berlin; Heidelberg: Springer, Springer Nature, 2003. p. 781-791 (Lecture Notes in Computer Science; Vol. 2801).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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AB - In this paper we consider a multi-cellular sensing and communication network, embedded in an ageless aerospace vehicle, that is expected to detect and react to impact location, intensity and damage over a wide range of impact energies. In particular, we investigate self-organisation of impact boundaries enclosing critically damaged areas, and measure their spatiotemporal robustness. The presented quantitative information-theoretic techniques clearly identify phase transitions, separating chaotic dynamics from ordered and robust patterns.

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Foreman M, Prokopenko M, Wang P. Phase transitions in self-organising sensor networks. In Hutchison D, Kanade T, Kittler J, Kleinberg JM, Mattern F, Mitchell JC, Naor M, Pandu Rangan C, Steffen B, Terzopoulos D, Tygar D, Weikum G, editors, Advances in Artificial Life. Vol. 2801. Berlin; Heidelberg: Springer, Springer Nature. 2003. p. 781-791. (Lecture Notes in Computer Science). doi: 10.1007/978-3-540-39432-7_84

Phase transitions in self-organising sensor networks

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