Authors : Balusamy Nachiappan

Volume/Issue : Volume 9 - 2024, Issue 10 - October

Google Scholar : https://tinyurl.com/yv3wn97r

Scribd : https://tinyurl.com/j8kahkz6

DOI : https://doi.org/10.38124/ijisrt/IJISRT24OCT867

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This paper explores innovative strategies for enhancing system efficiency in modern infrastructure by integrating artificial intelligence (AI), edge computing, and resource optimization techniques. As the complexity of infrastructure systems increases, traditional methods often fall short in addressing the evolving demands of operational efficiency and reliability. By leveraging AI algorithms for predictive analytics and resource allocation, and utilizing edge computing for real-time data processing, organizations can significantly improve performance and responsiveness. The study examines case studies that highlight successful implementations of these technologies across various sectors, including infrastructure monitoring, and grid maintenance. Insights from this research provide a framework for practitioners to adopt these advanced methodologies, ultimately leading to more resilient and efficient infrastructure systems.

Keywords : System Efficiency, Artificial Intelligence, Edge Computing, Resource Optimization, Predictive Analytics.

References :

1. Helm P. Risk and resilience: strategies for security. Civil Engineering and Environmental Systems. 2015 Apr 3;32(1-2):100-18.
2. Too E, Too L. Strategic infrastructure asset management: a conceptual framework to identify capabilities. Journal of corporate real estate. 2010 Sep 14;12(3):196-208.
3. Rane N, Choudhary S, Rane J. Artificial intelligence for enhancing resilience. Journal of Applied Artificial Intelligence. 2024 Sep 9;5(2):1-33.
4. Choi TM, Wallace SW, Wang Y. Big data analytics in operations management. Production and operations management. 2018 Oct;27(10):1868-83.
5. Yu W, Liang F, He X, Hatcher WG, Lu C, Lin J, Yang X. A survey on the edge computing for the Internet of Things. IEEE access. 2017 Nov 29;6:6900-19.
6. Wang X, Han Y, Leung VC, Niyato D, Yan X, Chen X. Edge AI: Convergence of edge computing and artificial intelligence. Singapore: Springer; 2020 Aug 31.
7. Ezeigweneme CA, Nwasike CN, Adefemi A, Adegbite AO, Gidiagba JO. Smart grids in industrial paradigms: a review of progress, benefits, and maintenance implications: analyzing the role of smart grids in predictive maintenance and the integration of renewable energy sources, along with their overall impact on the industri. Engineering Science & Technology Journal. 2024 Jan 15;5(1):1-20.
8. Bhat SM, Venkitaraman A. Strategic integration of predictive maintenance plans to improve operational efficiency of smart grids. In2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS) 2024 Jun 28 (pp. 1-5). IEEE.
9. Sobb T, Turnbull B, Moustafa N. Supply chain 4.0: A survey of cyber security challenges, solutions and future directions. Electronics. 2020 Nov 6;9(11):1864.
10. Shin W, Han J, Rhee W. AI-assistance for predictive maintenance of renewable energy systems. Energy. 2021 Apr 15;221:119775.
11. Ranyal E, Sadhu A, Jain K. Road condition monitoring using smart sensing and artificial intelligence: A review. Sensors. 2022 Apr 15;22(8):3044.
12. Bhat SM, Venkitaraman A. Hybrid v2x and drone-based system for road condition monitoring. In2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC) 2024 Jun 5 (pp. 1047-1052). IEEE.
13. Feng C, Wang Y, Chen Q, Ding Y, Strbac G, Kang C. Smart grid encounters edge computing: Opportunities and applications. Advances in Applied Energy. 2021 Feb 23;1:100006.
14. Adetola A, Goulding J. Collaborative framework for road infrastructure management. Infrastructure Asset Management. 2016 Apr 8;3(2):71-80.