{"title":"驾驭人工智能与物联网的关系:全面回顾数据分析和隐私范例","authors":"","doi":"10.1016/j.iot.2024.101318","DOIUrl":null,"url":null,"abstract":"<div><p>Integrating 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 must comprehensively address the multifaceted dimensions of the AI-driven IoT landscape. This review aims to bridge this gap by systematically examining existing literature and acknowledging the limitations of past studies. The study uses a meticulous approach guided by established methodologies to achieve this aim. The chosen methodology ensures the rigour and validity of the review, aligning with PRISMA 2020 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. Our analysis shows that integrating AI with IoT improves operational efficiency, service personalisation, and data-driven decisions in healthcare, manufacturing, and urban resource management. Real-time machine learning algorithms and edge computing solutions are set to revolutionise IoT data processing and analysis by improving system responsiveness and privacy. However, increasing concerns about data privacy and security emphasise the need for new regulatory frameworks and data protection technologies to ensure the ethical adoption of AI-driven IoT technologies.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2542660524002592/pdfft?md5=24abcf4a9c69bf711b561192ce140157&pid=1-s2.0-S2542660524002592-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Navigating the nexus of AI and IoT: A comprehensive review of data analytics and privacy paradigms\",\"authors\":\"\",\"doi\":\"10.1016/j.iot.2024.101318\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Integrating 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 must comprehensively address the multifaceted dimensions of the AI-driven IoT landscape. This review aims to bridge this gap by systematically examining existing literature and acknowledging the limitations of past studies. The study uses a meticulous approach guided by established methodologies to achieve this aim. The chosen methodology ensures the rigour and validity of the review, aligning with PRISMA 2020 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. Our analysis shows that integrating AI with IoT improves operational efficiency, service personalisation, and data-driven decisions in healthcare, manufacturing, and urban resource management. Real-time machine learning algorithms and edge computing solutions are set to revolutionise IoT data processing and analysis by improving system responsiveness and privacy. However, increasing concerns about data privacy and security emphasise the need for new regulatory frameworks and data protection technologies to ensure the ethical adoption of AI-driven IoT technologies.</p></div>\",\"PeriodicalId\":29968,\"journal\":{\"name\":\"Internet of Things\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2024-08-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2542660524002592/pdfft?md5=24abcf4a9c69bf711b561192ce140157&pid=1-s2.0-S2542660524002592-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Internet of Things\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2542660524002592\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Internet of Things","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2542660524002592","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Navigating the nexus of AI and IoT: A comprehensive review of data analytics and privacy paradigms
Integrating 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 must comprehensively address the multifaceted dimensions of the AI-driven IoT landscape. This review aims to bridge this gap by systematically examining existing literature and acknowledging the limitations of past studies. The study uses a meticulous approach guided by established methodologies to achieve this aim. The chosen methodology ensures the rigour and validity of the review, aligning with PRISMA 2020 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. Our analysis shows that integrating AI with IoT improves operational efficiency, service personalisation, and data-driven decisions in healthcare, manufacturing, and urban resource management. Real-time machine learning algorithms and edge computing solutions are set to revolutionise IoT data processing and analysis by improving system responsiveness and privacy. However, increasing concerns about data privacy and security emphasise the need for new regulatory frameworks and data protection technologies to ensure the ethical adoption of AI-driven IoT technologies.
期刊介绍:
Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT.
The journal will place a high priority on timely publication, and provide a home for high quality.
Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.