Skip to main content
SpringerLink
Log in
Book cover

Computing and Communication Systems in Urban Development pp 115–139Cite as

Energy Efficient Network Selection for Urban Cognitive Spectrum Handovers

  • Chapter
  • First Online:

Part of the book series: Urban Computing ((UC))

Abstract

Energy efficient network selection for urban cognitive spectrum handover is a concurrent research that required thorough evaluation. The outcome of this research enables environmentalists to indirectly manage the emission of greenhouse gases, including the monitoring of energy costs for various handover procedures. The solutions based on cognitive radio (CR) potentially save energy for various devices and networks. Additionally, the vitality utilization of CR advancements must be considered. This paper critically evaluates the comportments by which institutionalization accomplishments can enhance the utilization of CR to both spare vitalities for portable and remote correspondences, and guarantee that the vitality utilization in CR systems and gadgets is limited. The substantial contention for such arrangements is introduced and evaluated thoroughly in this article. Cognitive radio systems will result in a high transfer speed to portable clients by the means of heterogeneous remote structures and dynamic spectrum methods. Nonetheless, CR systems present some difficulties because of the fluctuating idea of the accessible spectrum, just as the redirection prerequisites of different applications. Spectrum planning capacities can address these difficulties for the acknowledgment of this new network framework.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Bethell, T.: The search for artificial intelligence. Am. Spect. 39(6), 26–35 (2006)

    Google Scholar 

  2. Ordonez, V., Berg, T.L.: Learning high-level judgments of urban perception. In: Computer Vision– ECCV 2014, pp. 494–510. Springer, Cham (2014)

    Chapter  Google Scholar 

  3. Turban, E.: Decision Support Systems and Expert Systems. Prentice-Hall, Englewood Cliffs (1995)

    MATH  Google Scholar 

  4. Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, San Mateo (1993)

    Book  Google Scholar 

  5. Hayes-Roth, F.: Rule-based systems. Commun. ACM. 28, 921–932 (1985)

    Article  Google Scholar 

  6. Ng, K.C., Abramson, B.: Uncertainty management in expert systems. IEEE Intell. Syst. 5(2), 29–47 (1990)

    Google Scholar 

  7. Dhar, V., Stein, R.: Intelligent decision support methods. In: The Science of Knowledge Work. Prentice Hall, Upper Saddle River (1997)

    Google Scholar 

  8. Kawano, S., Huynh, V.N., Ryoke, M., Nakamori, Y.: A context-dependent knowledge model for evaluation of regional environment. Environ. Model. Softw. 20, 343–352 (2005)

    Article  Google Scholar 

  9. Janssen, R., Goosen, H., Verhoeven, M.L., Verhoeven, J.T.A., Omtzigt, A.Q.A., Maltby, E.: Decision support for integrated wetland management. Environ. Model. Softw. 20, 215–229 (2005)

    Article  Google Scholar 

  10. Yao, X.: Evolving artificial neural networks. Proc. IEEE. 87(9), 1423–1447 (1999)

    Article  Google Scholar 

  11. Hammerstrom, D.: Working with neural networks. IEEE Spectr. 30(7), 46–53 (1993)

    Article  Google Scholar 

  12. Jain, A.K., Mao, J., Mohiuddin, K.: Artificial neural networks: a tutorial. IEEE Comput. 29, 31–44 (1996)

    Article  Google Scholar 

  13. Santiago, M.S., Rodrigues, V.: Nonlinear aspects of data integration for land-cover classification in a neural network environment. Adv. Space Res. 14, 265–268 (1994)

    Article  Google Scholar 

  14. Metternicht, G., Gonzalez, S.: FUERO: foundations of a fuzzy exploratory model for soil erosion hazard prediction. Environ. Model. Softw. 20, 715–728 (2005)

    Article  Google Scholar 

  15. Buckeles, B.P., Petry, F.E.: Genetic Algorithms. IEEE Computer Society Press, Los Alamitos (1992)

    Google Scholar 

  16. Brown, I.: Modelling future landscape change on coastal floodplains using a rule-based GIS. Environ. Model. Softw. 21, 1479–1490 (2006)

    Article  Google Scholar 

  17. Watson, I., Marir, F.: Case-based, reasoning: a review. Knowl. Eng. Rev. 9, 327–354 (1994)

    Article  Google Scholar 

  18. Lee, B.H., Scholz, M., Horn, A.: Constructed wetlands: prediction of performance with case-based reasoning (part B). Environ. Eng. Sci. 23, 332–340 (2006)

    Article  Google Scholar 

  19. Fuller, R.: Introduction to Neuro-Fuzzy Systems. Physica-Verlag, Heidelberg (2000)

    Book  Google Scholar 

  20. Lesser, V.R.: Multiagent systems: an emerging subdiscipline of AI. ACM Comput. Surv. 27, 340–342 (1995)

    Article  Google Scholar 

  21. Parrott, L., Lacroix, R., Wade, K.M.: Design considerations for the implementation of multi-agent systems in the dairy industry. Comput. Electron. Agric. 38, 79–98 (2003)

    Article  Google Scholar 

  22. Flores-Mendez, R.A.: Towards a standardization of multi-agent system frameworks. Crossroads. 5(4), 18–24 (1999). http://www.acm.org/crossroads/xrds54/multiagent.html

    Article  Google Scholar 

  23. Denby, B., Le Hégarat-Mascle, S.: Swarm intelligence in optimisation problems. Nucl. Instrum. Meth. Phys. Res. Sect. A. 502, 364–368 (2003)

    Article  Google Scholar 

  24. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)

    MATH  Google Scholar 

  25. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. B. 26, 29–41 (1996)

    Article  Google Scholar 

  26. Shelokar, P.S., Siarry, P., Jayaraman, V.K., Kulkarni, B.D.: Particle swarm and ant colony algorithms hybridized for improved continuous optimization. Appl. Math. Comput. 188(1), 129–142 (2007)

    Article  MathSciNet  Google Scholar 

  27. Sutton, R., Barto, A.: Reinforcement learning: an introduction. http://www.cs.ualberta.ca/%7Esutton/book/ebook/the-book.html (1998)

    Article  Google Scholar 

  28. Harmon, M.E., Harmon, S.S.: Reinforcement learning: a tutorial. http://www.nbu.bg/cogs/events/2000/Readings/Petrov/rltutorial.pdf (2000)

  29. Kaelbling, L., Littman, M., Moore, A.: Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237–285 (1996)

    Article  Google Scholar 

  30. Gray, A., Kilgour, R.: Frequently asked questions: hybrid systems. http://www.cecs.missouri.edu/~rsun/hybrid-FAQ.html (1997)

  31. El-Yacoubi, S., El-Jai, A., Jacewicz, P., Pausas, J.G.: LUCAS: an original tool for landscape modelling. Environ. Model. Softw. 18, 429–437 (2003)

    Article  Google Scholar 

  32. Kuo, J.T., Wang, Y.Y., Lung, W.S.: A hybrid neural-genetic algorithm for reservoir water quality management. Water Res. 40, 1367–1376 (2006)

    Article  Google Scholar 

  33. Huang, S.H., Zhang, H.C.: Neural-expert hybrid approach for intelligent manufacturing: a survey. Comput. Ind. 26, 107–126 (1995)

    Article  Google Scholar 

  34. John, G.H., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: Proceedings of 11th Conference on Uncertainty in Artificial Intelligence, pp. 338–345. Morgan Kaufman, San Mateo (1995)

    Google Scholar 

  35. Kohavi, R., Quinlan, J.R., Will, K., Jan, M.: Decision-tree discovery. In: Handbook of Data Mining and Knowledge Discovery, pp. 267–276. Oxford University Press, New York (2002)

    Google Scholar 

  36. Bouckaert, R.: Bayesian Network Classifiers in Weka. Technical Report. Department of Computer Science, Waikato University, Hamilton (2005)

    Google Scholar 

  37. Kohavi, R.: Scaling up the accuracy of Naive-Bayes classifiers: a decision-tree hybrid. In: Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD), pp. 202–207. AAAI Press, Menlo Park (1996)

    Google Scholar 

  38. Svensson, A.: Arterial Streets for People. Technical Report. Lund University, Department of Technology and Society, Lund (2004)

    Google Scholar 

  39. Yiming, Y.: An evaluation of statistical approaches to text categorization. Inf. Retr. 1(1/2), 69–90 (1999)

    Article  Google Scholar 

  40. Lise, G.: Learning statistical models from relational data. PhD thesis, Stanford University (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

    Anandakumar Haldorai

  2. Department of Computer Science and Engineering, Presidency University, Yelahanka, Bengaluru, Karnataka, India

    Arulmurugan Ramu

  3. Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India

    Suriya Murugan

Authors
  1. Anandakumar Haldorai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arulmurugan Ramu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Suriya Murugan
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Haldorai, A., Ramu, A., Murugan, S. (2019). Energy Efficient Network Selection for Urban Cognitive Spectrum Handovers. In: Computing and Communication Systems in Urban Development. Urban Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-26013-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-26013-2_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26012-5

  • Online ISBN: 978-3-030-26013-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation