Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Alstom, G.: T & D Protective Relays Application Guide, 3 edn. CEE relays Ltd (1987)
David Garlan, S.W.C., Schmerl, B.: Increasing system dependability through architecture-based self repair. In: Appears in Architecting Dependable Systems, 2003
Erik, H., Poul, T.: A statistical test for the mean squared error. J. Stat. Comput. Simul. 63, 321–347 (1999)
Field, A.: Discovering Statistics Using SPSS (Introducing Statistical Methods S.), 2nd edn. SAGE Publication, Thousand Oaks (2005)
Fawcett, T.: ROC Graphs: Notes and Practical Considerations for Researchers. HP Laboratories, CA (2004)
Garlan, D.: Model-based adaptation for self-healing systems. Presented at the In ACM SIGSOFT Workshop on Self-Healing Systems (WOSS’02). Charleston, SC, 2002a
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, 2002b
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 http://repository.cmu.edu/compsci/690/ (2001)
Garlan, B.S.: Rainbow: Architecture-based self-adaptation with reusable infrastructure. IEEE Comput. Soc. 37(10), (2004)
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)
Haykin, S., Li, L.: Nonlinear adaptive prediction of nonstationary signals. IEEE Trans. Signal Process. 43, 526–535 (1995)
Kon, F.: The case for reflective middleware. Presented at the Communications of the ACM, 2002
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 2009
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)
Mousa Al-Zawi, M.: Autonomic computing: using adaptive neural network in self-healing systems. PhD Thesis, Liverpool John Moores University (2012)
Mason, C.R.: The art and science of protective relaying, 1 edn. Wiley. Ariva, Network Protection & Automation Guide Barcelona, Spain (2002)
Mikic-Rakic, N.M., Medvidovic, N.: Architectural style requirements for self-healing systems. Presented at the Wass’02. Charleston, South Carolina, USA, 2002
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)
Peyman Oreizy, G., Taylor, R.N. et al.: An architecture-based approach to self-adaptive software. IEEE Intell. Syst. Appl. 14, 54–62 (1999)
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)
Tosi, D.: Research Perspectives in Self-healing Systems. University of Milano, Bicocca (2004)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
AI-Jumeily, D., Al-Zawi, M., Hussain, A.J., Dobre, C. (2014). Adaptive Pipelined Neural Network Structure in Self-aware Internet of Things. In: Bessis, N., Dobre, C. (eds) Big Data and Internet of Things: A Roadmap for Smart Environments. Studies in Computational Intelligence, vol 546. Springer, Cham. https://doi.org/10.1007/978-3-319-05029-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-05029-4_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05028-7
Online ISBN: 978-3-319-05029-4
eBook Packages: EngineeringEngineering (R0)