Comparative Study of QoS-aware Network Coding Protocols in WSNs

Sghaier, Asma; Meddeb, Aref

doi:10.1109/aiccsa56895.2022.10017702

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Our challenge is to implement a new paradigm, NCD-CLA (Network Coding-based Duty Cycle Learning Algorithm) [7][8][9] [10], which holds the potential to offer significant benefits across various communication network metrics, such as throughput, delay, energy efficiency, wireless resources, security, complexity, and resilience to link failures.…”

Section: Introductionmentioning

confidence: 99%

Real Time Qos in Wsn Based Network Coding and Reinforcement Learning

Sghaier,

Medeb,

Medeb

2023

IJCAI

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In recent years, wireless sensor networks have experienced significant advancements, driven by a reduction in development costs. This rapid growth in WSNs has led to the emergence of various potential and emerging applications, including real-time applications, which pose challenges due to their substantial requirements. As the number of applications continues to increase, ensuring both reliable and real-time Quality of Service (QoS) communication in resource-constrained WSNs becomes a paramount concern. To address this challenge, we propose the use of network coding (NC), a novel research area applicable in diverse environments to overcome several shortcomings within a network. Additionally, we focus on the duty cycle, recognized as one of the most popular techniques for energy conservation. Specifically, we employ the Duty Cycle Learning Algorithm (DCLA) to determine the optimal duty cycle. To guarantee the expected real-time QoS and reliability, we introduce NCDCLA (Network Coding-based Duty Cycle Learning Algorithm). Through simulations in OPNET, our results demonstrate that our approach achieves commendable reliable performance.Povzetek: Nova metoda NCDCLA za zagotavljanje zanesljivosti QoS v WSN je zasnovana kot kombinacija mrežnega kodiranja in algoritma za učenje delovnega cikla.

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Section: Introductionmentioning

confidence: 99%

Real Time Qos in Wsn Based Network Coding and Reinforcement Learning

Sghaier,

Medeb,

Medeb

2023

IJCAI

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Hybridization of Machine Learning Techniques for WSN Optimal Cluster Head Selection

Praveenkumar¹,

Kirthika²,

Arumugam³

et al. 2023

IJEER

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Wireless sensor networks (WSN) keep developing in recent days concerning the self-covered network, self-healing network, and association of system component circuit selections that enable the implementation process. Wireless sensor network lifetime stabilization is essential to providing a higher quality experience to consumers. The wireless sensor network is associated with classifiers that keep learning the data pattern and further modify the cluster selection to produce dynamic results. The presented system is focused on creating an efficient wireless sensor network in which cluster head selection is dynamically programmed to improve the life span of the devices. The energy utilized by each node is pre-programmed with random assignments. The values are configured by the machine learning techniques to improve the hop death. The models developed using the parameters help project energy consumption. The proposed system considers a support vector machine (SVM), and the Gaussian regression process (GRP) enabled the comparative study of lifespan analysis and support systems to make cluster selection efficient. The proposed model is used to test the current selection of cluster heads using a support rectangle machine and further modify the regression process using the Gaussian regression model. Evaluation of network lifetime and flexibility obtained in cluster selection.

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Comparative Study of QoS-aware Network Coding Protocols in WSNs

Cited by 2 publications

References 17 publications

Real Time Qos in Wsn Based Network Coding and Reinforcement Learning

Real Time Qos in Wsn Based Network Coding and Reinforcement Learning

Hybridization of Machine Learning Techniques for WSN Optimal Cluster Head Selection

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