An Overview of Machine Learning-Based Energy-Efficient Routing Algorithms in Wireless Sensor Networks

Ding, Qianao; Zhu, R. Y.; Líu, Hao

doi:10.3390/electronics10131539

Cited by 49 publications

(17 citation statements)

References 80 publications

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“…Wireless sensor networks [78] In this article, the authors investigated and proposed a theoretical hypothetical model of wireless sensor networks as an effective method for creating an energy-efficient green routing model that can overcome the limitations of traditional green routing methods. For comparison, the authors built several wireless sensor networks and tested attacks on them, after which they applied machine learning to determine the probability of attacks.…”

Section: Name Of the Property Publication A Brief Descriptionmentioning

confidence: 99%

“…Analyzing the sources of the above table, we can say that in [69,78,80,83,85,86] the authors built schematic models to demonstrate their usefulness, and in [71,[73][74][75][76][77]81,82,84,[87][88][89][90] the authors cited models either existing, described in words or only mathematically. This approach may not be clear to everyone, and may also cause difficulties.…”

Section: Name Of the Property Publication A Brief Descriptionmentioning

confidence: 99%

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A Survey on Threat-Modeling Techniques: Protected Objects and Classification of Threats

et al. 2022

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Information security is one of the most important attributes of distributed systems that often operate on unreliable networks. Enabling security features during the development of a distributed system requires the careful analysis of potential attacks or threats in different contexts, a process often referred to as «threat modeling». Information protection should be comprehensive, but it is also necessary to take into account the possibility of the emergence of threats specific to a certain information system. Many public and private organizations are still trying to implement system models and the threats directed at them on their own. The main reason for this is the lack of useful and high-quality methodologies that can help developers design system models. This review explores a variety of the literature on confidentiality- and integrity-aware system design methodologies, as well as threat classification methods, and identifies key issues that may be referenced by organizations to make design system processes easier. In particular, this article takes a look at the extent to which existing methodologies cover objects of protection and methods of classifying threats, as well as whether there are such models of systems in which the object itself and the threats directed at it are described. This includes whether the compiled models exhibit symmetry or asymmetry. This literature research shows that methodologies appear to be heterogeneous and versatile, since existing methodologies often only focus on one object of protection (a system). Based on the given analysis, it can be concluded that the existing methodologies only relate superficially to the description of system models and threats, and it is necessary to develop a more complete abstract model of the protected object and threats aimed at it in order to make this model suitable for any organization and protect it against most threats.

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Section: Name Of the Property Publication A Brief Descriptionmentioning

confidence: 99%

Section: Name Of the Property Publication A Brief Descriptionmentioning

confidence: 99%

A Survey on Threat-Modeling Techniques: Protected Objects and Classification of Threats

et al. 2022

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“…e experiments demonstrated that the Q-routing strategy can efficiently avoid the network congestion and minimize the packet transmission time when compared to standard shortest path routing. However, although many subsequent works have perfected and optimized the method [17,18], limited by the computing power of the router and the design of the network layer structure, the intelligent routing algorithm is difficult to be deployed in real network scenarios.…”

Section: Overview Of Intelligent Routing Algorithmsmentioning

confidence: 99%

“…[36] Path creation Flow-based Centralized Ref. [16][17][18][19][20][21] Path creation Packet-based Online Decentralized Online Ref. [14] Splitting ratio configuration Epoch-based Centralized Ref.…”

Section: Utilize Intelligent Modules To Assist Routing Calculationmentioning

confidence: 99%

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Comparative Analysis of Routing Schemes Based on Machine Learning

Shaoyu

Tan

Madsen

et al. 2022

Mobile Information Systems

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Machine learning-based distributed routing algorithms, in contrast to traditional mathematical model-driven distributed routing algorithms, are typically data-driven, allowing them to adapt to dynamically changing network environments and various performance evaluation index optimization requirements. It is quite likely that it will become a key part of the next-generation Internet in the future. However, current intelligent routing research is still in its early stages. This article provides a comprehensive review of the state-of-the-art routing algorithms based on machine learning. First, important research on existing data-driven intelligent routing algorithms is presented with the key concepts and applications of these systems demonstrated. To enable intelligent routing algorithms to be deployed in real scenarios with cheap cost and high reliability, two appropriate training deployment frameworks and intelligent routing algorithm training and deployment strategies are given. Finally, the future development of machine learning-based intelligent routing systems is examined. The opportunities and problems that have been encountered, as well as prospective research directions, are discussed.

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A systematic literature review of machine learning applications in IoT

Gherbi

Senouci

Harbi

et al. 2023

Int J Communication

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The Internet of Things (IoT) is a network of interconnected smart objects having capabilities that collectively form an ecosystem and enable the delivery of smart services to users. The IoT is providing several benefits into people's lives through the environment. The various applications that are run in the IoT environment offer facilities and services. The most crucial services provided by IoT applications are quick decision for efficient management. Recently, machine learning (ML) techniques have been successfully used to maximize the potential of IoT systems. This paper presents a systematic review of the literature on the integration of ML methods in the IoT. The challenges of IoT systems are split into two categories: fundamental operation and performance. We also look at how ML is assisting in the resolution of fundamental system operation challenges such as security, big data, clustering, routing, and data aggregation.

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An Overview of Machine Learning-Based Energy-Efficient Routing Algorithms in Wireless Sensor Networks

Cited by 49 publications

References 80 publications

A Survey on Threat-Modeling Techniques: Protected Objects and Classification of Threats

A Survey on Threat-Modeling Techniques: Protected Objects and Classification of Threats

Comparative Analysis of Routing Schemes Based on Machine Learning

A systematic literature review of machine learning applications in IoT

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