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Bio-inspired Communications in Wireless Sensor Networks

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Abstract

Wireless-sensor networks (WSN) are expected to enable connection between physical world and the Internet to provide access to vast amount of information from anywhere and anytime through any kind of communication devices and services. However, this vision poses significant challenges for WSN. Due to the pervasion in its nature, centralized control of WSN is not a practical solution. Instead, WSN and its communication protocols must have the capabilities of scalability, self-organization, self-adaptation, and survivability. In nature, the biological systems intrinsically have these capabilities such that billions of blood cells, which constitute the immune system, can protect the organism from the pathogens without any central control of the brain. Similarly, in the insect colonies insects can collaboratively allocate certain tasks according to the sensed information from the environment without any central controller. Therefore, the natural biological systems may give great inspiration to develop the communication network models and techniques for WSN. In this chapter, we introduce potential solution avenues from the biological systems toward addressing the challenges of WSN such as scalability, self-organization, self-adaptation, and survivability. After the existing biological models are first investigated, biologically inspired communication approaches are introduced for WSN. The objective of these communication approaches is to serve as a roadmap for the development of efficient scalable, adaptive, and self-organizing bioinspired communication techniques for WSN.

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Notes

  1. 1.

     Here, we consider a source node as a sensor node which senses and samples the event signal and forwards to the sink node.

References

  1. Akyildiz I F, Su W, Sankarasubramaniam Y, Cayirci E (2002) A survey on sensor networks. IEEE Communications Magazine 40:102–114.

    Article  Google Scholar 

  2. Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm Intelligence, From Natural to Artificial System. Oxford University Press, Oxford.

    MATH  Google Scholar 

  3. Muraleedharan R, Osadciw L A (2003) Sensor communication network using swarm intelligence. IEEE Upstate New York Workshop, Syracuse, NY, USA.

    Google Scholar 

  4. Muraleedharan R, Osadciw L A (2003) Balancing the performance of a sensor network using an ant system. Annual Conference on Information Sciences and Systems, Baltimore, MD, USA.

    Google Scholar 

  5. Caro G D, Ducatelle F, Gambardella L M (2005) AntHocNet: an adaptive nature-inspired algorithm for routing in mobile ad hoc networks. European Transactions on Telecommunications 16:443–455.

    Article  Google Scholar 

  6. Hong Y W, Scaglione A (2005) A scalable synchronization protocol for large scale sensor networks and its applications. IEEE Journal on Selected Areas in Communications 23:1085–1099.

    Article  Google Scholar 

  7. Werner-Allen G, Tewari G, Patel A, Welsh M, Nagpal R (2005) FireflyInspired Sensor Network Synchronicity with Realistic Radio Effects. SenSys’05.

    Google Scholar 

  8. Carreras I, Chlamtac I, Woesner H, Kiraly C (2005) BIONETS: Bioinspired next generation networks. Lecture Notes in Computer Science 3457:245–252.

    Article  Google Scholar 

  9. Dressler F (2005) Efficient and Scalable Communication in autonomous networking using bio-inspired mechanisms – An overview. Informatica 29:183–188.

    Google Scholar 

  10. Dressler F, Krüger B, Fuchs G, German R (2005) Self-Organization in Sensor Networks Using Bio-Inspired Mechanism. ARCS’05.

    Google Scholar 

  11. Dressler F (2005) Locality Driven Congestion Control in Self-Organizing Wireless Sensor Networks. SASO-STEPS’05.

    Google Scholar 

  12. Wokoma T, Shum L L, Sacks L, Marshall I (2005) A biologically inspired clustering algorithm dependent on spatial data in sensor networks. Second European Workshop on Wireless Sensor Networks.

    Google Scholar 

  13. Atakan B, Akan O B (2007) Immune system based energy efficient and reliable communication in wireless sensor networks. In: Dressler F and Carreras I (eds.) Advances in Biologically Inspired Information Systems, Springer, New York, NY.

    Google Scholar 

  14. Timmis J, Neal M, Hunt J (2000) An artificial immune system for data analysis. Biosystems 55:143–150.

    Article  Google Scholar 

  15. Jerne N K (1984) Idiotypic network and other preconceived ideas. Immunological Review 79:5–24.

    Article  Google Scholar 

  16. Farmer J D, Packard N H, Perelson A S (1986) The immune system, adaptation, and machine learning. Physica 22:187–204.

    MathSciNet  Google Scholar 

  17. Vuran M C, Akan O B, Akyildiz I F (2004) Spatio-temporal correlation: theory and applications for wireless sensor networks. Computer Networks Journal (Elsevier) 45:245–261.

    Article  MATH  Google Scholar 

  18. Berger J O, Oliviera V, Sanso B (2001) Objective bayesian analysis of spatially correlated data. Journal of the American Statistical Association 96:1361–1374.

    Article  MATH  MathSciNet  Google Scholar 

  19. Neal M, Timmis J (2005) Once more unto the breach towards artificial homeostasis. Recent Advances in Biologically Inspired Computing, Idea Group, pp. 340–365.

    MATH  Google Scholar 

  20. Oppenheim A V, Schafer R W, Buck J R (1999) Discrete-Time Signal Processing, Prentice Hall, Upper Saddle River, NJ.

    Google Scholar 

  21. Hightower J, Borriello G (2001) Location systems for ubiquitous computing. IEEE Computer 34:57–66.

    Google Scholar 

  22. Welch P D (1967) The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short modified periodogram. IEEE Transaction on Audio and Electroacoustics 15:70–73.

    Article  MathSciNet  Google Scholar 

  23. Akyildiz I F, Kasimoglu I H (2004) Wireless sensor and actor networks: research challenges. Ad Hoc Networks 2:351–367.

    Article  Google Scholar 

  24. Heinzelman W, Chandrakasan A, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Transaction on Wireless Communications 1:660–667.

    Article  Google Scholar 

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© 2009 Springer-Verlag London Limited

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Atakan, B., Akan, Ö., Tuğcu, T. (2009). Bio-inspired Communications in Wireless Sensor Networks. In: Misra, S., Woungang, I., Misra, S. (eds) Guide to Wireless Sensor Networks. Computer Communications and Networks. Springer, London. https://doi.org/10.1007/978-1-84882-218-4_26

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  • DOI: https://doi.org/10.1007/978-1-84882-218-4_26

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84882-217-7

  • Online ISBN: 978-1-84882-218-4

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