A New Intelligent Approach for Optimizing 6LoWPAN MAC Layer Parameters

Al-Kaseem, Bilal R.; Al-Raweshidy, Hamed S.; Al-Dunainawi, Yousif; Banitsas, Konstantinos

doi:10.1109/access.2017.2737951

Cited by 13 publications

(10 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, in [216] the authors aim to select the optimal MAC parameter settings in 6LoWPAN networks to reduce excessive collisions, packet losses and latency. First, the MAC layer parameters are used as input to a NN to predict the throughput and latency, followed by an optimization algorithm to achieve high throughput with minimum delay.…”

Section: Network Predictionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Network Predictionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…• Network performance prediction [120], [81], [201], [112], [202], [203], [30], [204], [205], [206], [207] • Network traffic prediction [30,81,112,120,[201][202][203][204][205][206][207] Information processing…”

Section: 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

View full text Add to dashboard Cite

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.

show abstract

“…Nonappropriate parameters for low power personal area network (LowPAN) MAC for IPv6 applications lead to collisions, latency, and packet losses under high traffic conditions. Using intelligent mechanisms with genetic algorithm (GA), artificial neural networks (ANN), and particle swarm optimizations (PSO), optimal parameter selection 23 for MAC is possible that will further improve throughput with minimum latency.…”

Section: Related Workmentioning

confidence: 99%

FRAME routing with game theory optimization for wireless networks

Radha

Bala

Nagabushanam

2019

Int J Communication

View full text Add to dashboard Cite

Summary Wireless ad hoc networks have a wide variety of applications in the present world. Creating routes between the nodes and with low energy consumption is a major challenge in wireless ad hoc networks. In this paper, we have proposed FRAME routing for ad hoc networks, which consider the entire network based on hierarchal Bayesian model. It works on game theory–based Chow‐Liu algorithm for establishing routes from source to destination considering the best neighbors in the path. FRAME routing is further analyzed with SMAC and conventional MAC in various scenarios. They are also compared with conventional MAC in AODV routing in terms of energy consumption, delay, throughput, PDR, and goodput. The entire simulations are carried out using NS2 and SMAC in FRAME routing that give better performance compared with conventional MAC in FRAME, AODV routing.

show abstract

A New Intelligent Approach for Optimizing 6LoWPAN MAC Layer Parameters

Cited by 13 publications

References 23 publications

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

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

FRAME routing with game theory optimization for wireless networks

Contact Info

Product

Resources

About