{"title":"Swarm intelligence techniques and their applications in fog/edge computing: an in-depth review","authors":"Reyhane Ghafari, Najme Mansouri","doi":"10.1007/s10462-025-11351-2","DOIUrl":null,"url":null,"abstract":"<div><p>Recent advances in the Internet of Things (IoT) have connected diverse devices that often have limited resources and processing power. Artificial intelligence (AI) applications in fog and edge computing are greatly enhanced by Swarm Intelligence (SI) techniques. These SI methods improve resource allocation, task scheduling, and load balancing, making distributed systems more efficient and responsive to changing conditions. This paper systematically reviews 91 studies (2019–2023) on SI applications in fog/edge environments. We compare fog, edge, and cloud computing paradigms and analyze SI-based approaches using case studies, performance metrics, and evaluation tools. This review identifies key advantages and limitations of current SI-based approaches and highlights open issues and future research directions to enhance distributed computing systems. These insights aim to guide the development of more efficient and responsive AI-driven resource management strategies in fog/edge environments.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 11","pages":""},"PeriodicalIF":13.9000,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11351-2.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Intelligence Review","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s10462-025-11351-2","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Recent advances in the Internet of Things (IoT) have connected diverse devices that often have limited resources and processing power. Artificial intelligence (AI) applications in fog and edge computing are greatly enhanced by Swarm Intelligence (SI) techniques. These SI methods improve resource allocation, task scheduling, and load balancing, making distributed systems more efficient and responsive to changing conditions. This paper systematically reviews 91 studies (2019–2023) on SI applications in fog/edge environments. We compare fog, edge, and cloud computing paradigms and analyze SI-based approaches using case studies, performance metrics, and evaluation tools. This review identifies key advantages and limitations of current SI-based approaches and highlights open issues and future research directions to enhance distributed computing systems. These insights aim to guide the development of more efficient and responsive AI-driven resource management strategies in fog/edge environments.
期刊介绍:
Artificial Intelligence Review, a fully open access journal, publishes cutting-edge research in artificial intelligence and cognitive science. It features critical evaluations of applications, techniques, and algorithms, providing a platform for both researchers and application developers. The journal includes refereed survey and tutorial articles, along with reviews and commentary on significant developments in the field.