Optimization of wireless sensor networks MAC protocols using machine learning; a survey

Zubir, Noor Zuriatunadhirah binti; Ramli, Aizat Faiz; Basarudin, Hafiz

doi:10.1109/ice2t.2017.8215998

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…Ref. [57] introduces the latest application of ML in improving the performance of MAC throughput, energy efficiency and delay in wireless sensor networks. It comprehensively reviews the application of ML technology and supports the implementation of real-timebased Internet of things (IoT) applications by optimizing the performance of MAC protocols.…”

Section: Review Of Related Survey Articlesmentioning

confidence: 99%

Survey of Reinforcement-Learning-Based MAC Protocols for Wireless Ad Hoc Networks with a MAC Reference Model

Zheng

Jiang

Feng

et al. 2023

Entropy

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In this paper, we conduct a survey of the literature about reinforcement learning (RL)-based medium access control (MAC) protocols. As the scale of the wireless ad hoc network (WANET) increases, traditional MAC solutions are becoming obsolete. Dynamic topology, resource allocation, interference management, limited bandwidth and energy constraint are crucial problems needing resolution for designing modern WANET architectures. In order for future MAC protocols to overcome the current limitations in frequently changing WANETs, more intelligence need to be deployed to maintain efficient communications. After introducing some classic RL schemes, we investigate the existing state-of-the-art MAC protocols and related solutions for WANETs according to the MAC reference model and discuss how each proposed protocol works and the challenging issues on the related MAC model components. Finally, this paper discusses future research directions on how RL can be used to enable MAC protocols for high performance.

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Section: Review Of Related Survey Articlesmentioning

confidence: 99%

Survey of Reinforcement-Learning-Based MAC Protocols for Wireless Ad Hoc Networks with a MAC Reference Model

Zheng

Jiang

Feng

et al. 2023

Entropy

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“…The authors in [26] reviewed various MAC protocols according to the problem being addressed. The authors in [27] surveyed the work published in the period from 2002 to 2011 in the area of MAC protocols for WSN. Table . I shows a comparison between the different surveys and tutorials in this area.…”

Section: Accronymmentioning

confidence: 99%

“…Navigating through this survey has many forms depending on the objective of the reader. For those who are interested [27] 2017 MAC Protocols Machine Learning WSN Unspecified Isolani et al [29] 2018 MAC General Wireless Networks Unspecified Quintero et al [30] 2018 MAC General WSN Unspecified Zhang et al [31] 2019 General Deep Learning Wireless Networks Unspecified Sun et al [32] 2019 General Machine Learning Wireless Networks Unspecified Fatima et al [33] Ours 2020 MAC Machine Learning Wireless Networks 2011-2021 in gaining a piece of knowledge on the underlying concepts and terminologies of ML, they can start with section II. For the readers who are not familiar with the MAC techniques, III and IV sections give a good background and place the reader in the context of MAC technique design.…”

Section: A Navigation: How You Can Read This Paper?mentioning

confidence: 99%

Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey

Hussien¹

2023

Preprint

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<p>The last decade, (2012 - 2022), saw an unprecedented advance in machine learning (ML) techniques, particularly deep learning (DL). As a result of the proven capabilities of DL, a large amount of work has been presented and studied in almost every field. Since 2012, when the convolution neural networks have been reintroduced in the context of \textit{ImagNet} competition, DL continued to achieve superior performance in many challenging tasks and problems. Wireless communications, in general, and medium access control (MAC) techniques, in particular, were among the fields that were heavily affected by this improvement. MAC protocols play a critical role in defining the performance of wireless communication systems. At the same time, the community lacks a comprehensive survey that collects, analyses, and categorizes the recent work in ML-inspired MAC techniques. In this work, we fill this gap by surveying a long line of work in this era. We solidify the impact of machine learning on wireless MAC protocols. We provide a comprehensive background to the widely adopted MAC techniques, their design issues, and their taxonomy, in connection with the famous application domains. Furthermore, we provide an overview of the ML techniques that have been considered in this context. Finally, we augment our work by proposing some promising future research directions and open research questions that are worth further investigation.</p>

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“…From this study, the authors concluded that the proposed approach is more significant in terms of consumed energy and it has a higher throughput than other compared protocols. The paper [6] demonstrates a complete review of machine learning to enhance the performance of MAC protocol in WSN. This enhancement was in terms of energy-saving, throughput, and delay.…”

Section: Related Workmentioning

confidence: 99%

Minimizing Collision of Fading Channel Using Machine Learning

ALhaddad

Sati

Elmusrati

2021

2021 IEEE Microwave Theory and Techniques in Wireless Communications (MTTW)

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Energy consumption is considered the main challenge of MAC protocol design. Especially when MAC protocol is employed in an environment of limited energy resources as a wireless sensor network. Parameters optimization of the shared channel in sensor communications is the aim of any MAC protocol designer. In this paper, we suggest a machine learning-based approach for the improvement of the performance parameters using channel prediction learning. Channel predication learning ensures that all the learning process is done by the node. The proposed machine learning algorithm takes into consideration the fading channel parameters and suggests a solution that is best suited to optimize the performance parameters. The proposed machine learning approach incorporates the use of Sensor-MAC (SMAC) protocol and suggests the best tuned MAC protocol based on the supervised learning GRNN algorithm. We investigate the system performance using simulation scenarios under various configurations. The overall performance improvement is more than 80% based on all the output performance parameters.

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Optimization of wireless sensor networks MAC protocols using machine learning; a survey

Cited by 5 publications

References 17 publications

Survey of Reinforcement-Learning-Based MAC Protocols for Wireless Ad Hoc Networks with a MAC Reference Model

Survey of Reinforcement-Learning-Based MAC Protocols for Wireless Ad Hoc Networks with a MAC Reference Model

Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey

Minimizing Collision of Fading Channel Using Machine Learning

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