A Survey on the Programmability of Wireless MAC Protocols

Isolani, Pedro Heleno; Claeys, Maxim; Donato, Carlos; Granville, Lisandro Zambenedetti; Latré, Steven

doi:10.1109/comst.2018.2881761

Cited by 9 publications

(8 citation statements)

References 141 publications

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“…One of the main services of the MAC layer is to grantee the spectrum sharing in a way that minimizes collisions [20]. Therefore, the need to predict the wireless medium availability to optimize the network performances.…”

Section: Spectrum Predictionmentioning

confidence: 99%

Performances prediction in Wireless Sensor Networks: A survey on Deep learning based-approaches

et al. 2022

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The dynamism and successive changes in the distribution of nodes are among the important characteristics of wireless sensor networks (WSNs). To adapt with those changes, network designers frequently have to configure multiple parameters for each layer on WSN architecture (i.e physical, medium access control, network, and application layer). This tuning has an important impact on the network performances (e.g packet loss, energy efficiency, throughput, network lifetime, etc). However, finding the optimal configuration is the main challenge. Deep learning (DL) based on neural network layers can be used to extract patterns from high-dimensional data provided by sensor nodes. In this paper, we survey the most recent DL approaches which aim to predict WSN performances by finding the pattern on the network parameters (such as transmission power level, MAC protocol type, spectrum availability, congestion points, etc.). Moreover, we classify the studied articles by considering the targeted network layer or cross-layer. This paper can be considered as a starting point for researchers to review the recent DL applications on the optimization of WSNs performances based on multiple network parameters.

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Section: Spectrum Predictionmentioning

confidence: 99%

Performances prediction in Wireless Sensor Networks: A survey on Deep learning based-approaches

et al. 2022

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“…Sharing the limited spectrum resources is the main concern in wireless networks [255]. One of the key functionalities of the MAC layer in wireless networks is to negotiate the access to the wireless medium to share the limited resources in an ad hoc manner.…”

Section: Medium Access Control (Mac) Analysismentioning

confidence: 99%

A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

et al. 2021

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This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY, MAC and network. First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.

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“…Sharing the limited spectrum resources is the main concern in wireless networks [245]. One of the key functionalities of the MAC layer in wireless networks is to negotiate the access to the wireless medium to share the limited resources in an ad hoc manner.…”

Section: Medium Access Control (Mac) Analysismentioning

confidence: 99%

A survey on Machine Learning-based Performance Improvement of Wireless Networks: PHY, MAC and Network layer

Kulin¹,

Kazaz²,

Moerman³

et al. 2020

Preprint

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This paper provides a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack (PHY, MAC and network). First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning for non-machine learning experts to understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.

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A Survey on the Programmability of Wireless MAC Protocols

Cited by 9 publications

References 141 publications

Performances prediction in Wireless Sensor Networks: A survey on Deep learning based-approaches

Performances prediction in Wireless Sensor Networks: A survey on Deep learning based-approaches

A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

A survey on Machine Learning-based Performance Improvement of Wireless Networks: PHY, MAC and Network layer

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