AI-Driven Edge Computing for Real-time Decision Making in IoT Environments
Abstract
The convergence of Artificial Intelligence (AI) and Internet of Things (IoT) technologies has revolutionized the landscape of real-time decision-making in IoT environments. This research paper delves into the burgeoning field of AI-driven edge computing, focusing on its pivotal role in enabling rapid and informed decisions within IoT frameworks.
The paper investigates the fundamental aspects of edge computing, where computational processes are decentralized and executed closer to the data source, reducing latency and enhancing efficiency. Moreover, it explores the integration of AI algorithms at the edge, facilitating advanced analytics, predictive modeling, and cognitive capabilities in IoT devices and networks.
Various methodologies and architectures for implementing AI-driven edge computing in IoT environments are scrutinized, emphasizing their significance in addressing critical challenges such as limited bandwidth, latency concerns, and privacy issues. Additionally, the paper surveys cutting-edge AI models, including machine learning, deep learning, and reinforcement learning, and their applicability in empowering edge devices for autonomous decision-making.
Furthermore, this study examines practical use cases across diverse domains, such as smart manufacturing, healthcare systems, transportation, and smart cities, showcasing the tangible benefits accrued from AI-driven edge computing. Additionally, ethical considerations and potential security vulnerabilities associated with the deployment of AI at the edge in IoT networks are analyzed, providing insights into mitigating risks and ensuring robustness.
In conclusion, this research underscores the transformative potential of AI-driven edge computing in facilitating real-time decision-making within IoT ecosystems. It offers a comprehensive understanding of the synergistic interplay between AI, edge computing, and IoT, paving the way for innovative solutions and applications in a hyper-connected world.
References
Suryadevara, C. K. (2016). Sparkling Insights: Automated Diamond Price Prediction Using Machine Learning. A Journal of Advances in Management IT & Social Sciences
Suryadevara, Chaitanya Krishna, Predictive Analysis for Big MartSales using Machine Learning Algorithms (November 24, 2020). International Research Journal of Natural and Applied Sciences, Available at SSRN: https://ssrn.com/abstract=
Suryadevara, C. K. (2021). Feline vs. Canine: A Deep Dive into Image Classification of Cats and Dogs. International Research Journal of Mathematics, Engineering and IT..
Suryadevara, C. K. (2021). Twitter Sentiment Analysis: Exploring Public Sentiments on Social Media. International Journal of Research in Engineering and Applied Sciences.
Suryadevara, C. K. (2022). Forensic Foresight: A Comparative Study of Operating System Forensics Tools. International Journal of Engineering, Science and Mathematics.
krishna Suryadevara, C. (2023). NOVEL DEVICE TO DETECT FOOD CALORIES USING MACHINE LEARNING. Open Access Repository, 10(9), 52-61.
Chaitanya Krishna Suryadevara, “TOWARDS PERSONALIZED HEALTHCARE - AN INTELLIGENT MEDICATION RECOMMENDATION SYSTEM”, IEJRD - International Multidisciplinary Journal, vol. 5, no. 9, p. 16, Dec. 2020.
Suryadevara, Chaitanya Krishna, Predictive Modeling for Student Performance: Harnessing Machine Learning to Forecast Academic Marks (December 22, 2018). International Journal of Research in Engineering and Applied Sciences (IJREAS), Vol. 8 Issue 12, December-2018, Available at SSRN: https://ssrn.com/abstract=4591990
Suryadevara, Chaitanya Krishna, Unveiling Urban Mobility Patterns: A Comprehensive Analysis of Uber (December 21, 2019). International Journal of Engineering, Science and Mathematics, Vol. 8 Issue 12, December 2019, Available at SSRN: https://ssrn.com/abstract=4591998
Chaitanya Krishna Suryadevara. (2019). A NEW WAY OF PREDICTING THE LOAN APPROVAL PROCESS USING ML TECHNIQUES. International Journal of Innovations in Engineering Research and Technology, 6(12), 38–48. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3654
Chaitanya Krishna Suryadevara. (2020). GENERATING FREE IMAGES WITH OPENAI’S GENERATIVE MODELS. International Journal of Innovations in Engineering Research and Technology, 7(3), 49–56. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3653
Chaitanya Krishna Suryadevara. (2020). REAL-TIME FACE MASK DETECTION WITH COMPUTER VISION AND DEEP LEARNING: English. International Journal of Innovations in Engineering Research and Technology, 7(12), 254–259. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3184
Chaitanya Krishna Suryadevara. (2021). ENHANCING SAFETY: FACE MASK DETECTION USING COMPUTER VISION AND DEEP LEARNING. International Journal of Innovations in Engineering Research and Technology, 8(08), 224–229. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3672
Chaitanya Krishna Suryadevara, “DIABETES RISK ASSESSMENT USING MACHINE LEARNING: A COMPARATIVE STUDY OF CLASSIFICATION ALGORITHMS”, IEJRD - International Multidisciplinary Journal, vol. 8, no. 4, p. 10, Aug. 2023.
Chaitanya Krishna Suryadevara. (2023). REVOLUTIONIZING DIETARY MONITORING: A COMPREHENSIVE ANALYSIS OF THE INNOVATIVE MOBILE APP FOR TRACKING DIETARY COMPOSITION. International Journal of Innovations in Engineering Research and Technology, 10(8), 44–50. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3673
Chaitanya krishna Suryadevara. (2023). NOVEL DEVICE TO DETECT FOOD CALORIES USING MACHINE LEARNING. Open Access Repository, 10(9), 52–61. Retrieved from https://oarepo.org/index.php/oa/article/view/3546
Vegesna, V. V. (2023). AI-Enabled Blockchain Solutions for Sustainable Development, Harnessing Technological Synergy towards a Greener Future. (2023). International Journal of Sustainable Development Through AI, ML and IoT, 2(2), 1-10. https://ijsdai.com/index.php/IJSDAI/article/view/23
Vegesna, V. V. (2023). Enhancing Cyber Resilience by Integrating AI-Driven Threat Detection and Mitigation Strategies. Transactions on Latest Trends in Artificial Intelligence, 4(4).
Kasula, B. Y. (2023). Harnessing Machine Learning for Personalized Patient Care. Transactions on Latest Trends in Artificial Intelligence, 4(4).
Kasula, B. Y. (2023). Framework Development for Artificial Intelligence Integration in Healthcare: Optimizing Patient Care and Operational Efficiency. Transactions on Latest Trends in IoT, 6(6), 77-83.
Vegesna, V. V. (2023). Comprehensive Analysis of AI-Enhanced Defense Systems in Cyberspace. (2023). International Numeric Journal of Machine Learning and Robots, 7(7). https://injmr.com/index.php/fewfewf/article/view/21
Vegesna, V. V. (2023). Enhancing Cybersecurity Through AI-Powered Solutions: A Comprehensive Research Analysis. (2023). International Meridian Journal, 5(5), 1-8. https://meridianjournal.in/index.php/IMJ/article/view/21
Kasula, B. Y. (2023). Leveraging Natural Language Processing and Machine Learning for Enhanced Content Rating. (2023). International Meridian Journal, 5(5). https://meridianjournal.in/index.php/IMJ/article/view/8
Kasula, B. Y. (2023). Revealing Insights: Machine Learning-Based Prediction of Thyroid Disorders. (2023). International Journal of Creative Research In Computer Technology and Design, 5(5). https://jrctd.in/index.php/IJRCTD/article/view/17
Vegesna, D. (2023). Privacy-Preserving Techniques in AI-Powered Cyber Security: Challenges and Opportunities. International Journal of Machine Learning for Sustainable Development, 5(4), 1-8. Retrieved from https://www.ijsdcs.com/index.php/IJMLSD/article/view/408
Kasula, B. (2023). AI Applications in Healthcare a Comprehensive Review of Advancements and Challenges. International Journal of Managment Education for Sustainable Development, 6(6). Retrieved from https://ijsdcs.com/index.php/IJMESD/article/view/400
Kasula, B. Y. (2023). A Machine Learning Approach for Differential Diagnosis and Prognostic Prediction in Alzheimer's Disease. International Journal of Sustainable Development in Computing Science, 5(4), 1-8.
Kasula, B. Y. (2023). Machine Learning Models for Understanding Blood-Brain Barrier Integrity and Transport Mechanisms. International Journal of Machine Learning for Sustainable Development, 5(4), 1-8.
Vegesna, V. V. (2023). A Critical Investigation and Analysis of Strategic Techniques Before Approving Cloud Computing Service Frameworks. International Journal of Management, Technology and Engineering, 13.
Kasula, B. Y. (2023). Revolutionizing Healthcare Delivery: Innovations and Challenges in Supply Chain Management for Improved Patient Care. Transactions on Latest Trends in Health Sector, 15(15).
Kasula, B. Y. (2023). Machine Learning Applications in Diabetic Healthcare: A Comprehensive Analysis and Predictive Modeling. (2023). International Numeric Journal of Machine Learning and Robots, 7(7). https://injmr.com/index.php/fewfewf/article/view/19
Kasula, B. Y. (2023). AI-Driven Machine Learning Solutions for Sustainable Development in Healthcare—Pioneering Efficient, Equitable, and Innovative Health Service. (2023). International Journal of Sustainable Development Through AI, ML and IoT, 2(2), 1-7. https://ijsdai.com/index.php/IJSDAI/article/view/26
Kasula, B. Y. (2023). Synergizing AI, IoT, and Blockchain: Empowering Next-Generation Smart Systems in Healthcare. International Journal of Sustainable Development in Computing Science, 5(2), 60-64.
Vegesna, V. V. (2023). A Comprehensive Investigation of Privacy Concerns in the Context of Cloud Computing Using Self-Service Paradigms. International Journal of Management, Technology and Engineering, 13.
Vegesna, V. V. (2023). The Utilization of Information Systems for Supply Chain Management for Multicomponent Productivity Based on Cloud Computing. International Journal of Management, Technology and Engineering, 11.
Vegesna, V. V. (2023). Utilising VAPT Technologies (Vulnerability Assessment & Penetration Testing) as a Method for Actively Preventing Cyberattacks. International Journal of Management, Technology and Engineering, 12.
Vegesna, V. V. (2023). A Highly Efficient and Secure Procedure for Protecting Privacy in Cloud Data Storage Environments. International Journal of Management, Technology and Engineering, 11.
