{"title":"Exploring machine learning solutions for overcoming challenges in IoT-based wireless sensor network routing: a comprehensive review","authors":"","doi":"10.1007/s11276-024-03697-2","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>An industry-wide paradigm change has been sparked by the growth of Internet of Things (IoT)-based Wireless Sensor Networks (WSNs), which has made reliable and effective routing methods necessary. This thorough analysis looks at how Machine Learning (ML) techniques may be used to solve the problems that come with WSN routing. A summary of standard routing algorithms and an examination of their shortcomings comprise the first portion of the paper. The integration of ML approaches, such as reinforcement learning and supervised and unsupervised learning, is then explored in order to improve WSN routing efficiency. The article examines the difficulties and factors related to ML-based routing, including data quality, energy efficiency, scalability, and security. Applications and case studies show how ML is really used in WSN routing, offering insights into effective tactics and lessons discovered. Evaluation metrics and performance assessments are included in a separate section that uses simulation and experimental data to compare ML-based and conventional techniques. Looking forward, the study describes new breakthroughs in ML for WSNs and points out unresolved issues, providing a guide for future research paths. The important results and their consequences are outlined in the conclusion, which also highlights how ML has the potential to revolutionize WSN routing in the future.</p>","PeriodicalId":23750,"journal":{"name":"Wireless Networks","volume":"232 1","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Wireless Networks","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11276-024-03697-2","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
An industry-wide paradigm change has been sparked by the growth of Internet of Things (IoT)-based Wireless Sensor Networks (WSNs), which has made reliable and effective routing methods necessary. This thorough analysis looks at how Machine Learning (ML) techniques may be used to solve the problems that come with WSN routing. A summary of standard routing algorithms and an examination of their shortcomings comprise the first portion of the paper. The integration of ML approaches, such as reinforcement learning and supervised and unsupervised learning, is then explored in order to improve WSN routing efficiency. The article examines the difficulties and factors related to ML-based routing, including data quality, energy efficiency, scalability, and security. Applications and case studies show how ML is really used in WSN routing, offering insights into effective tactics and lessons discovered. Evaluation metrics and performance assessments are included in a separate section that uses simulation and experimental data to compare ML-based and conventional techniques. Looking forward, the study describes new breakthroughs in ML for WSNs and points out unresolved issues, providing a guide for future research paths. The important results and their consequences are outlined in the conclusion, which also highlights how ML has the potential to revolutionize WSN routing in the future.
摘要 基于物联网（IoT）的无线传感器网络（WSN）的发展引发了整个行业的范式变革，这使得可靠而有效的路由选择方法变得十分必要。本文将深入分析如何利用机器学习（ML）技术解决 WSN 路由问题。本文的第一部分总结了标准路由算法并分析了其缺点。然后探讨了如何整合强化学习、监督和非监督学习等 ML 方法，以提高 WSN 路由效率。文章探讨了与基于 ML 的路由相关的困难和因素，包括数据质量、能效、可扩展性和安全性。应用和案例研究展示了如何在 WSN 路由中真正使用 ML，深入探讨了有效的策略和发现的经验教训。评估指标和性能评估包含在一个单独的章节中，该章节使用模拟和实验数据来比较基于 ML 的技术和传统技术。展望未来，本研究描述了 WSN 在 ML 方面的新突破，并指出了尚未解决的问题，为未来的研究路径提供了指导。结论部分概述了重要成果及其后果，还强调了 ML 有可能在未来彻底改变 WSN 路由。
期刊介绍：
The wireless communication revolution is bringing fundamental changes to data networking, telecommunication, and is making integrated networks a reality. By freeing the user from the cord, personal communications networks, wireless LAN''s, mobile radio networks and cellular systems, harbor the promise of fully distributed mobile computing and communications, any time, anywhere.
Focusing on the networking and user aspects of the field, Wireless Networks provides a global forum for archival value contributions documenting these fast growing areas of interest. The journal publishes refereed articles dealing with research, experience and management issues of wireless networks. Its aim is to allow the reader to benefit from experience, problems and solutions described.
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