Version 1
: Received: 27 December 2023 / Approved: 28 December 2023 / Online: 28 December 2023 (11:21:54 CET)
Version 2
: Received: 28 December 2023 / Approved: 28 December 2023 / Online: 29 December 2023 (10:15:06 CET)
Version 3
: Received: 26 January 2024 / Approved: 26 January 2024 / Online: 26 January 2024 (12:05:57 CET)
How to cite:
Marengo, A. Future of AI-Driven IoT: Identifying Emerging Trends in Intelligent Data Analysis and Privacy Protection. Preprints2023, 2023122184. https://doi.org/10.20944/preprints202312.2184.v3
Marengo, A. Future of AI-Driven IoT: Identifying Emerging Trends in Intelligent Data Analysis and Privacy Protection. Preprints 2023, 2023122184. https://doi.org/10.20944/preprints202312.2184.v3
Marengo, A. Future of AI-Driven IoT: Identifying Emerging Trends in Intelligent Data Analysis and Privacy Protection. Preprints2023, 2023122184. https://doi.org/10.20944/preprints202312.2184.v3
APA Style
Marengo, A. (2024). Future of AI-Driven IoT: Identifying Emerging Trends in Intelligent Data Analysis and Privacy Protection. Preprints. https://doi.org/10.20944/preprints202312.2184.v3
Chicago/Turabian Style
Marengo, A. 2024 "Future of AI-Driven IoT: Identifying Emerging Trends in Intelligent Data Analysis and Privacy Protection" Preprints. https://doi.org/10.20944/preprints202312.2184.v3
Abstract
The integration of Artificial Intelligence (AI) with the Internet of Things (IoT) has propelled technological innovation across various industries. This systematic literature review explores the current state and future trajectories of AI in IoT, with a particular focus on emerging trends in intelligent data analysis and privacy protection. The proliferation of IoT devices, marked by voluminous data generation, has reshaped data processing methods, providing actionable insights for informed decision-making. While previous reviews have offered valuable insights, they often fall short of comprehensively addressing the multifaceted dimensions within the AI-driven IoT landscape. This review aims to bridge this gap by systematically examining existing literature and acknowledging the limitations of past studies. To achieve this aim, the study uses a meticulous approach guided by established methodologies. The chosen methodology ensures the rigor and validity of the review, aligning with PRISMA guidelines for systematic reviews. This systematic literature review serves as a comprehensive guide for researchers, practitioners, and policymakers, offering insights into the current landscape and paving the way for future research directions. The identified trends and challenges provide a valuable resource for navigating the evolving domain of AI in IoT, fostering a balanced, secure, and sustainable advancement in this dynamic field.
Keywords
Artificial Intelligence; Internet of Things; Integration; intelligent data analysis; privacy; emerging trends
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
