AI-Driven Predictive Analytics for Smart City Infrastructure in IoT Environments
Abstract
With the proliferation of Internet of Things (IoT) devices and the advancement of Artificial Intelligence (AI), the concept of smart cities has emerged as a promising solution for efficient and sustainable urban development. This paper presents a comprehensive study on the integration of AI-driven predictive analytics into the infrastructure of smart cities operating within IoT environments.
The research delves into the potential of AI algorithms, such as machine learning and deep learning, to analyze vast streams of data collected from diverse IoT sensors embedded in urban systems. These algorithms enable predictive analytics, allowing city planners and administrators to anticipate and mitigate various urban challenges, including traffic congestion, energy consumption, waste management, and public safety.
The paper highlights the significance of leveraging real-time data generated by IoT devices to train AI models that can forecast trends, anomalies, and potential disruptions in city operations. Moreover, it explores the deployment of edge computing techniques to process data closer to the source, reducing latency and enhancing the responsiveness of AI-driven predictive models.
Furthermore, ethical considerations and privacy concerns related to the collection and utilization of citizens' data within smart city infrastructures are addressed. The discussion emphasizes the importance of implementing robust governance frameworks and transparent practices to ensure data security and protect individuals' privacy rights.
In conclusion, this research emphasizes the transformative potential of AI-driven predictive analytics in optimizing smart city infrastructure within IoT ecosystems. The findings underscore the need for collaborative efforts among stakeholders to harness the full capabilities of AI and IoT technologies in building resilient and sustainable cities for the future.
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.