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A systematic review of Quality of Service in Wireless Sensor Networks using Machine Learning: Recent trend and future vision
Journal of Network and Computer Applications ( IF 7.7 ) Pub Date : 2021-04-23 , DOI: 10.1016/j.jnca.2021.103084
Meena Pundir , Jasminder Kaur Sandhu

Wireless Sensor Network (WSN) is used in different research areas such as military, industry, healthcare, agriculture, Internet of Things (IoT), transportation, and smart cities. The reason behind this is the rapid development of smart sensors. There is a challenging need to satisfy the Quality of Service (QoS) requirements in different applications due to the dynamic network condition, heterogeneous traffic flows and resource-constrained behaviour of sensor nodes. Optimizing the QoS in terms of performance, privacy and security levels is an open issue in the WSN. It has limited resources and is deployed in hostile environment where high performance is difficult to be achieved. The performance level is categorized into four subcategories: deployment phase, layered architecture, measurability, network and application specific parameter. Privacy and security levels are divided into four parameters: security, confidentiality, integrity and safety. A systematic review is presented in this paper based on QoS parameters in the light of Machine Learning (ML) techniques. It also provides a methodological framework for its parameters. This study presents a statistical analysis of the past ten years ranging from 2011 to 2020 on various ML techniques used for the QoS parameters. Finally, the author's vision is highlighted with some discussion on the open issues which forms the baseline for the future research directions.



中文翻译:

使用机器学习的无线传感器网络服务质量的系统综述:最新趋势和未来愿景

无线传感器网络(WSN)用于军事,工业,医疗保健,农业,物联网(IoT),交通运输和智慧城市等不同的研究领域。其背后的原因是智能传感器的飞速发展。由于动态网络条件,异构流量和传感器节点的资源受限行为,迫切需要满足不同应用中的服务质量(QoS)要求。在性能,隐私和安全级别方面优化QoS是WSN中的一个开放问题。它的资源有限,并且部署在难以实现高性能的敌对环境中。性能级别分为四个子类别:部署阶段,分层体系结构,可度量性,网络和特定于应用程序的参数。隐私和安全级别分为四个参数:安全性,机密性,完整性和安全性。根据机器学习(ML)技术,本文基于QoS参数进行了系统的综述。它还为其参数提供了一种方法学框架。这项研究提供了从2011年到2020年的十年间对用于QoS参数的各种ML技术的统计分析。最后,通过对未解决问题的一些讨论突出了作者的远见,这些问题构成了未来研究方向的基础。它还为其参数提供了一种方法学框架。这项研究提供了从2011年到2020年的十年间对用于QoS参数的各种ML技术的统计分析。最后,通过对未解决问题的一些讨论突出了作者的远见,这些问题构成了未来研究方向的基础。它还为其参数提供了一种方法学框架。这项研究提供了从2011年到2020年的十年间对用于QoS参数的各种ML技术的统计分析。最后,通过对未解决问题的一些讨论突出了作者的远见,这些问题构成了未来研究方向的基础。

更新日期:2021-04-23
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