Volume 4, Issue 1, June 2020, Page: 30-35
Autonomous Systems and Reliability Assessment: A Systematic Review
Kalesanwo Olamide, School of Computing and Engineering Sciences, Babcock University, Ilishan Remo, Nigeria
Kuyoro ‘Shade, School of Computing and Engineering Sciences, Babcock University, Ilishan Remo, Nigeria
Eze Monday, School of Computing and Engineering Sciences, Babcock University, Ilishan Remo, Nigeria
Awodele Oludele, School of Computing and Engineering Sciences, Babcock University, Ilishan Remo, Nigeria
Received: Mar. 14, 2020;       Accepted: Mar. 25, 2020;       Published: Apr. 30, 2020
DOI: 10.11648/j.ajai.20200401.13      View  438      Downloads  172
The advancement of technology has heralded novel computing devices and gadgets like self-driving cars, IoT devices, and autonomous systems. These advancements required high computational demand in achieving its goals. In matching the high computational demand of these new technologies, machine learning, parallelism, multicore processing and scaling are some of the approaches and techniques put in place. However, there is a pressure on the architectural development of recent computing devices as the traditional transistors seem to be fast outgrown. This article examines the reliability of autonomous systems using the PRISMA approach. Autonomous systems are systems that can fully operate and perform operations (computational or otherwise) with minimal human intervention. They are also capable of evaluating their performance. Thus, there is a need for a high degree of reliability. Several existing autonomous systems were reviewed and reliability issues of these systems were discussed. It was discovered that the reliability of a complex system is dependent on the reliability of underlying individual components and compromise of any of the underlying components of the autonomous system can affect the overall reliability of the entire system. The effort to enhance the reliability of these components will, in turn, improve the reliability of the entire system.
Autonomous, Complex Systems, Components, Reliability
To cite this article
Kalesanwo Olamide, Kuyoro ‘Shade, Eze Monday, Awodele Oludele, Autonomous Systems and Reliability Assessment: A Systematic Review, American Journal of Artificial Intelligence. Vol. 4, No. 1, 2020, pp. 30-35. doi: 10.11648/j.ajai.20200401.13
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
B. Becerik-Gerber, D. J. Gerber, and K. Ku, “The pace of technological innovation in architecture, engineering, and construction education: Integrating recent trends into the curricula,” Electron. J. Inf. Technol. Constr., vol. 16, pp. 411–432, 2011.
M. J. Irwin and J. P. Shen, “Revitalizing computer architecture research,” Comput. Res. Assoc., 2005.
M. Salem, “What is an ‘Autonomous System?,’” Udacity, 2018. [Online]. Available: https://blog.udacity.com/2018/09/what-is-an-autonomous-system.html. [Accessed: 13-Nov-2018].
S. Kate Devitt, “Trustworthiness of autonomous systems,” Stud. Syst. Decis. Control, vol. 117, pp. 161–184, 2018.
I. Gheta, M. Heizmann, A. Belkin, and J. Beyerer, “World Modeling for Autonomous Systems,” in Advances in artificial intelligence. 33rd Annual German Conference on AI, 2010, pp. 176–183.
“Basic Concepts of reliability,” vol. 19, no. 3, pp. 19–45, 2015.
S. V Adve and J. A. Rivers, “Lifetime Reliability : Toward an Architectural Solution as Scaling Threatens To Erode Reliability Standards, Lifetime Microarchitectural Intervention Offers a Novel Way to Manage,” pp. 70–80, 2005.
L. Xing, G. Levitin, and C. Wang, “Fundamental Reliability Theory,” in Dynamic System Reliability: Modeling and Analysis of Dynamic and Dependent Behaviors, John Wiley and Sons, Ltd, 2019, pp. 7–26.
T. Austin, V. Bertacco, S. Mahlke, and Y. Cao, “Reliable systems on unreliable fabrics,” IEEE Des. Test Comput., vol. 25, no. 4, pp. 322–332, 2008.
M. Ottavi, D. Gizopoulos, and S. Pontarelli, “Dependable multicore architectures at nanoscale,” Dependable Multicore Archit. Nanoscale, pp. 1–281, 2017.
E. Y. Nakagawa, R. Capilla, E. Woods, and P. Kruchten, “Sustainability and longevity of systems and architectures,” J. Syst. Softw., vol. 140, pp. 1–2, 2018.
K. Moslehi and R. Kumar, “A Reliability Perspective of the Smart Grid,” vol. 1, no. 1, pp. 57–64, 2010.
G. Caralis and A. Zervos, “Value of wind energy on the reliability of autonomous power systems,” IET Renew. Power Gener., vol. 4, no. 2, p. 186, 2010.
P. Paliwal, N. P. Patidar, and R. K. Nema, “A novel method for reliability assessment of autonomous PV-wind-storage system using probabilistic storage model,” Int. J. Electr. Power Energy Syst., vol. 55, pp. 692–703, 2014.
N. Kalra and S. M. Paddock, “Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?,” Transp. Res. Part A Policy Pract., vol. 94, pp. 182–193, 2016.
Y. Ren, L. Guo, and S. Jiang, “Review of the reliability in a robotic application: autonomous driving cars,” Int. Robot. Autom. J., vol. 4, no. 3, pp. 220–223, 2018.
H. Fazlollahtabar and S. T. A. Niaki, “Modified branching process for the reliability analysis of complex systems: Multiple-robot systems,” Commun. Stat. - Theory Methods, vol. 47, no. 7, pp. 1641–1652, 2018.
E. Fernandez, “Reliability assessment of a remote hybrid renewable energy system using Monte Carlo simulation Sarangthem Sanajaoba Singh * and,” vol. 9, no. 3, pp. 368–381, 2018.
D. Naga, R. Rupesh, G. Karthikeya, and K. Ravi, “Improvising Reliability of Autonomous Car using Risk detection,” pp. 4–7, 2018.
M. P. Brito and G. Griffiths, “Updating autonomous underwater vehicle risk based on the effectiveness of failure prevention and correction,” J. Atmos. Ocean. Technol., vol. 35, no. 4, pp. 797–808, 2018.
Browse journals by subject