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Modeling nano-communication networks using neurocomputing algorithm

Published: 26 October 2011 Publication History

Abstract

In this paper, a novel practical neurocomputing algorithm is introduced and elaborated in order to design and implement a nano-communication network for various applications such as medical and industrial signal processing. Firstly, the idea of artificial neural network (ANN) for data processing is explained and feasibility of modeling a nano-scale network by an optimized neurocomputing algorithm is discussed using binary neuro-modeling. Moreover, it is stressed how nano-scaling increases the complexity of the communication network considering the existing constraints on computation resources, and accuracy of the proposed networking algorithm, either for communication or computation. Furthermore, the developed nano-scale networking technique is more optimized in order to assist the so-called neural nano-machines, to conduct the simple nano-nodes working more effectively and collaboratively. To experiment the performance of the presented bio-inspired nano-network, a practical test scenario is implemented on Imote2 sensor nodes to compare the accuracy of data processing techniques, showing how a large-scale network is replaced by an efficient nano-scale networking algorithm. Finally, the obtained results are illustrated and more elaborated to provide a complete procedure for future developments of the bio-inspired networking in nano-scale.

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  • (2012)An ant colony biological inspired way for statistical shortest paths in complex brain networksProceedings of the 7th International Conference on Body Area Networks10.5555/2442691.2442704(48-51)Online publication date: 24-Feb-2012

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ISABEL '11: Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
October 2011
949 pages
ISBN:9781450309134
DOI:10.1145/2093698
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • Universitat Pompeu Fabra
  • IEEE
  • Technical University of Catalonia Spain: Technical University of Catalonia (UPC), Spain
  • River Publishers: River Publishers
  • CTTC: Technological Center for Telecommunications of Catalonia
  • CTIF: Kyranova Ltd, Center for TeleInFrastruktur

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 October 2011

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Author Tags

  1. bio-inspired modeling
  2. medical data processing
  3. nano-scale networking
  4. neural networks
  5. self-organizing networks
  6. smart communications

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ISABEL '11
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  • Technical University of Catalonia Spain
  • River Publishers
  • CTTC
  • CTIF

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Cited By

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  • (2012)An ant colony biological inspired way for statistical shortest paths in complex brain networksProceedings of the 7th International Conference on Body Area Networks10.5555/2442691.2442704(48-51)Online publication date: 24-Feb-2012

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