This article deals with the modeling and management of spatiotemporal systems using machine learning and self-organization algorithms. Two application examples are the localization of objects from radio measurements using spatiotemporal models learned from data, and the self-organizing management of wireless multi-hop sensor networks. For both examples we show how machine learning and self-organization significantly increases accuracy and efficiency.
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Bahl P, Padmanabhan VN (2000) RADAR: an in-building RF-based user location and tracking system. In: Proc IEEE joint conf IEEE computer communications societies (INFOCOM), pp 775–784
Bak P (1996) How nature works: the science of self-organized criticality. Copernicus Books, New York
Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, London
Bishop CM (2007) Pattern recognition and machine learning. Springer, Berlin
Cheung KW, So HC, Ma W-K, Chan YT (2004) Least squares algorithms for time-of-arrival based mobile location. IEEE Trans Signal Process 53(4):1121–1128
Durrant-Whyte H, Bailey T (2006) Simultaneous localization and mapping (slam). Part I. The essential algorithms. IEEE Robot Autom Mag 13:99–110
Gardner M (1970) Mathematical games: the fantastic combinations of John Conway’s new solitaire game “life”. Sci Am 223:120–123
Höppner F, Klawonn F, Kruse R, Runkler TA (1999) Fuzzy cluster analysis—methods for image recognition, classification, and data analysis. Wiley, New York
Olfati-Saber R, Franco E, Frazzoli E, Shamma JS (2006) Belief consensus and distributed hypothesis testing in sensor networks. In: Networked embedded sensing and control: workshop (NESC’05), University of Notre Dame, USA, October 2005. Springer, New York. Proceedings
Olfati-Saber R (2003) Consensus protocols for networks of dynamic agents. In: Proceeding of the 2003 American control conference, June 2003
Parodi BB, Lenz H, Szabo A, Wang H, Horn J, Bamberger J, Obradovic D (2006) Initialization and online-learning of RSS maps for indoor/campus localization. In: Proceedings of IEEE/ION PLANS 2006
Parodi BB, Szabo A, Horn J, Bamberger J (2008) Spll: simultaneous probabilistic localization and learning. In: Proceedings of IFAC world congress, vol 17, part 1
Reynolds CW (1987) Flocks, herds and schools: A distributed behavioral model. Comput Graph 21:25–34
Roos T, Myllymäki P, Tirri H, Misikangas P, Sievänen J (2002) A probabilistic approach to WLAN user location estimation. Int J Wirel Inf Netw 9(3):155–164
Tahbaz Salehi A, Jadbabaie A (2008) A necessary and sufficient condition for consensus over random networks. IEEE Trans Autom Control 53(3):791–796
Schölkopf B, Smola AJ (2002) Learning with kernels. MIT Press, Cambridge
Seow CK, Tan SY (2008) Non-line-of-sight localization in multipath environments. IEEE Trans Mob Comput 7(5):647–660
Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. MIT Press, Cambridge
Szabo A, Weiherer T, Bamberger J (2011) Unsupervised learning of propagation time for indoor localization. In: VTC spring, pp. 1–5. IEEE
Torrieri DJ (2007) Statistical theory of passive location systems. IEEE Trans Aerosp Electron Syst 20(2):183–198
Yang L, Ho KC (2009) An approximately efficient TDOA localization algorithm in closed-form for locating multiple disjoint sources with erroneous sensor positions. IEEE Trans Signal Process 57(12):4598–4615
Part of this work was done in the project ZESAN on reliable and energy efficient wireless sensor and actuator networks and was partially funded by German Ministry of Education and Research (BMBF) grant 01BN0712A.
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Runkler, T.A., Sollacher, R. & Szabo, A. Learning and Self-organization for Spatiotemporal Systems. Künstl Intell 26, 269–274 (2012). https://doi.org/10.1007/s13218-012-0171-x
- Sensor Node
- Time Slot
- Receive Signal Strength
- Consensus Protocol
- Receive Signal Strength Measurement