Adaptive Pipelined Neural Network Structure in Self-aware Internet of Things

  • Dhiya AI-Jumeily
  • Mohamad Al-Zawi
  • Abir Jaafar HussainEmail author
  • Ciprian Dobre
Part of the Studies in Computational Intelligence book series (SCI, volume 546)


Self-Managing systems are a significant feature in Autonomic Computing which is required for system reliability and performance in a changing environment. The work described in this book chapter is concerned with self-healing systems; systems that can detect and analyse issues with their behavior and performance, and fixe or reconfigure as appropriate. These processes should occur in real-time to restore the desired functionality as soon as possible. The system should ideally maintain functionality during the healing process which occurs at runtime. Adaptive neural networks are proposed as a solution to some of these challenges; monitoring the system and environment, mapping a suitable solution and adapting the system accordingly. A novel application of a modified Pipelined Recurrent Neural Network is proposed in this chapter with experiments aimed to assess its applicability to online.


Power System Mean Square Error Feedforward Neural Network Circuit Breaker Autonomic Computing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Alstom, G.: T & D Protective Relays Application Guide, 3 edn. CEE relays Ltd (1987)Google Scholar
  2. 2.
    David Garlan, S.W.C., Schmerl, B.: Increasing system dependability through architecture-based self repair. In: Appears in Architecting Dependable Systems, 2003Google Scholar
  3. 3.
    Erik, H., Poul, T.: A statistical test for the mean squared error. J. Stat. Comput. Simul. 63, 321–347 (1999)CrossRefzbMATHGoogle Scholar
  4. 4.
    Field, A.: Discovering Statistics Using SPSS (Introducing Statistical Methods S.), 2nd edn. SAGE Publication, Thousand Oaks (2005)Google Scholar
  5. 5.
    Fawcett, T.: ROC Graphs: Notes and Practical Considerations for Researchers. HP Laboratories, CA (2004)Google Scholar
  6. 6.
    Garlan, D.: Model-based adaptation for self-healing systems. Presented at the In ACM SIGSOFT Workshop on Self-Healing Systems (WOSS’02). Charleston, SC, 2002aGoogle Scholar
  7. 7.
    Garlan, D.: Exploiting architectural design knowledge to support self-repairing systems. Presented at the The 14th International Conference on Software Engineering and Knowledge Engineering, Ischia, Italy, 2002bGoogle Scholar
  8. 8.
    Garlan, D., Chang, J.: Using Gauges forArchitecture-Based Monitoring and Adaptation. In: Proceedings of the Working Conference on Complex and Dynamic Systems Architecture, Brisbane, Australia (2001)
  9. 9.
    Garlan, B.S.: Rainbow: Architecture-based self-adaptation with reusable infrastructure. IEEE Comput. Soc. 37(10), (2004)Google Scholar
  10. 10.
    Hussain, A.J., Lisboa, P., El-Deredy, W., Al-Jumeily, D.: Polynomial pipelined neural network and its application to financial time series prediction. Lect. Notes Comput. Sci. 4304, 597–606 (2006)Google Scholar
  11. 11.
    Haykin, S., Li, L.: Nonlinear adaptive prediction of nonstationary signals. IEEE Trans. Signal Process. 43, 526–535 (1995)CrossRefGoogle Scholar
  12. 12.
    Kon, F.: The case for reflective middleware. Presented at the Communications of the ACM, 2002Google Scholar
  13. 13.
    Mousa Al-Zawi, M., Hussain, A., Al-Jumeily, D., Taleb-Bendiab, A.: Using adaptive neural networks in self-healing systems. In: Proceedings of the 2nd International Conference on Developments in eSystems Engineering in Information Technology (DeSE’09), Abu Dhabi, UAE, pp. 227–232. 14–16 Dec 2009Google Scholar
  14. 14.
    Mousa Al-Zawi, M., Hussain, A., Taleb-Bendiab, A., Symons, A.: A survey: autonomic computing. In: 1st International Conference on Digital Communications and Computer Applications (DCCA2007), Jordan, pp. 973–979 (2007)Google Scholar
  15. 15.
    Mousa Al-Zawi, M.: Autonomic computing: using adaptive neural network in self-healing systems. PhD Thesis, Liverpool John Moores University (2012)Google Scholar
  16. 16.
    Mason, C.R.: The art and science of protective relaying, 1 edn. Wiley. Ariva, Network Protection & Automation Guide Barcelona, Spain (2002)Google Scholar
  17. 17.
    Mikic-Rakic, N.M., Medvidovic, N.: Architectural style requirements for self-healing systems. Presented at the Wass’02. Charleston, South Carolina, USA, 2002Google Scholar
  18. 18.
    Pereiraa, E., Pereirab, R., Taleb-Bendiabb, A.: Performance evaluation for self-healing distributed services and fault detection mechanisms. J. Comput. Syst. Sci. 72, 1172–1182 (2006)CrossRefGoogle Scholar
  19. 19.
    Peyman Oreizy, G., Taylor, R.N. et al.: An architecture-based approach to self-adaptive software. IEEE Intell. Syst. Appl. 14, 54–62 (1999)Google Scholar
  20. 20.
    Sterritt, R.: Autonomic computing-a means of achieving dependability. In: Proceedings of IEEE International Conference on the Engineering of Computer Based Systems (ECBS’03), Huntsville, Alabama, USA, pp. 247–251 (2003)Google Scholar
  21. 21.
    Tosi, D.: Research Perspectives in Self-healing Systems. University of Milano, Bicocca (2004)Google Scholar
  22. 22.
    Van Erkel, R., Pattynama, P.M.T.: Receiver operating characteristic (ROC) analysis: basic principles and applications in radiology. Eur. J. Radiol. 27, 88–94 (1998)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Dhiya AI-Jumeily
    • 1
  • Mohamad Al-Zawi
    • 1
    • 2
  • Abir Jaafar Hussain
    • 1
    Email author
  • Ciprian Dobre
    • 3
  1. 1.Applied Computing Research GroupLiverpool John Moores UniversityLiverpoolUK
  2. 2.Institute of Applied TechnologyAbu DhabiUAE
  3. 3.University Politehnica of BucharestBucharestRomania

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