Advertisement

Neuron Inspired Collaborative Transmission in Wireless Sensor Networks

  • Stephan Sigg
  • Predrag Jakimovski
  • Florian Becker
  • Hedda R. Schmidtke
  • Alexander Neumann
  • Yusheng Ji
  • Michael Beigl
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 104)

Abstract

We establish a wireless sensor network that emulates biological neuronal structures for the purpose of creating smart spaces. Two different types of wireless nodes working together are used to mimic the behaviour of a neuron consisting of dendrites, soma and synapses. The transmission among nodes that establish such a neuron structure is established by distributed beamforming techniques to enable simultaneous information transmission among neurons. Through superposition of transmission signals, data from neighbouring nodes is perceived as background noise and does not interfere. In this way we show that beamforming and computation on the channel can be powerful tools to establish intelligent sensing systems even with minimal computational power.

Keywords

computational neuroscience neuronal networks (NN) distributed adaptive beamforming artificial intelligence (AI) collaborative communication superimposed signals context recognition 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    3GPP. 3rd generation partnership project; technical specification group radio access networks; 3g home nodeb study item technical report (release 8). Technical Report 3GPP TR 25.820 V8.0.0 (March 2008)Google Scholar
  2. 2.
    Bucklew, J.A., Sethares, W.A.: Convergence of a class of decentralised beamforming algorithms. IEEE Transactions on Signal Processing 56(6), 2280–2288 (2008)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Jakimovski, P., Becker, F., Sigg, S., Schmidtke, H.R., Beigl, M.: Collective communication in dense sensing environments. In: The 7th International Conference on Intelligent Environments - IE 2011, Nottingham Trent University, United Kingdom (July 2011)Google Scholar
  4. 4.
    Maass, W.: Networks of spiking neurons: the third generation of neural network models. Neural Networks 10, 1659–1671 (1997)CrossRefGoogle Scholar
  5. 5.
    Mudumbai, R., Barriac, G., Madhow, U.: On the feasibility of distributed beamforming in wireless networks. IEEE Transactions on Wireless communications 6, 1754–1763 (2007)CrossRefGoogle Scholar
  6. 6.
    Mudumbai, R., Brown, D.R., Madhow, U., Poor, H.V.: Distributed transmit beamforming: Challenges and recent progress. IEEE Communications Magazine, 102–110 (February 2009)Google Scholar
  7. 7.
    Mudumbai, R., Hespanha, J., Madhow, U., Barriac, G.: Scalable feedback control for distributed beamforming in sensor networks. In: Proceedings of the IEEE International Symposium on Information Theory, pp. 137–141 (2005)Google Scholar
  8. 8.
    Mudumbai, R., Hespanha, J., Madhow, U., Barriac, G.: Distributed transmit beamforming using feedback control. IEEE Transactions on Information Theory 56(1) (January 2010)Google Scholar
  9. 9.
    Mudumbai, R., Wild, B., Madhow, U., Ramchandran, K.: Distributed beamforming using 1 bit feedback: from concept to realization. In: Proceedings of the 44th Allerton Conference on Communication, Control and Computation, pp. 1020–1027 (2006)Google Scholar
  10. 10.
    Rappaport, T.: Wireless Communications: Principles and Practice. Prentice Hall (2002)Google Scholar
  11. 11.
    Seo, M., Rodwell, M., Madhow, U.: A feedback-based distributed phased array technique and its application to 60-ghz wireless sensor network. In: IEEE MTT-S International Microwave Symposium Digest, pp. 683–686 (2008)Google Scholar
  12. 12.
    Sigg, S., Beigl, M.: Collaborative transmission in WSNs by a (1+1)-ea. In: Proceedings of the 8th International Workshop on Applications and Services in Wireless Networks, ASWN 2008 (2008)Google Scholar
  13. 13.
    Sigg, S., Beigl, M., Banitalebi, B.: Organic Computing - A Paradigm Shift for Complex Systems. In: Efficient Adaptive Communication From Multiple Resource Restricted Transmitters. Autonomic Systems Series, ch. 5.4. Springer (2011)Google Scholar
  14. 14.
    Sigg, S., El Masri, R.M., Beigl, M.: A sharp asymptotic bound for feedback based closed-loop distributed adaptive beamforming in wireless sensor networks. Transactions on Mobile Computing (2011)Google Scholar
  15. 15.
    Tu, Y., Pottie, G.: Coherent cooperative transmission from multiple adjacent antennas to a distant stationary antenna through awgn channels. In: Proceedings of the IEEE Vehicular Technology Conference, pp. 130–134 (2002)Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2012

Authors and Affiliations

  • Stephan Sigg
    • 1
  • Predrag Jakimovski
    • 2
  • Florian Becker
    • 2
  • Hedda R. Schmidtke
    • 2
  • Alexander Neumann
    • 2
  • Yusheng Ji
    • 1
  • Michael Beigl
    • 2
  1. 1.National Institute of Informatics (NII)TokyoJapan
  2. 2.Pervasive Computing Systems, TecOKarlsruhe Institute of Technology (KIT)Germany

Personalised recommendations