Enhancement of the performance of MANET using machine learning approach based on SDNs
[ X ]
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier GmbH
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Deep learning (DL) is a subdivision of machine learning (ML) that employs numerous algorithms, each of which provides various explanations of the data it consumes; mobile ad-hoc networks (MANET) are growing in prominence. For reasons including node mobility, due to the potential wireless sensor network (WSN) to provide a small-cost solution to real-world contact challenges. But the lifespan in this network is restricted lifespan. Therefore, the wireless sensor network (WSN) is more vulnerable to battery consumption. On the other hand, routing packets in a Wireless Sensor Network (WSN) is a challenging task, according to the limited resources available on the nodes of these networks, especially their energy sources. The use of Machine Learning (ML) techniques in a Software-Defined Network (SDN) topology has shown good potential for solving such a complex task. However, existing techniques emphasize finding the shortest paths to deliver the packets, which can overload certain nodes in the network, depending on their positioning. In this study, a new method is proposed to extend the lifetime of the WSN by balancing the loading on the nodes, using a Deep Reinforcement Learning (DRL) approach. By emphasizing the lifetime of the network, the proposed method has been able to discover and use alternative routes to deliver the packets, avoiding the use of nodes with low energy. Hence, the average number of hops the packets travel through has been increased, but the time required for the first node to exhaust its energy has been significantly increased.
Açıklama
Anahtar Kelimeler
DL, DRL, MANET, ML, SDN, WSN
Kaynak
Optik
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
272
Sayı
Künye
Abbood, Z. A., Atilla, D. Ç., Aydın, Ç. (2023). Enhancement of the performance of MANET using machine learning approach based on SDNs. Optik, 272, 170268.
