Yıl 2021, Cilt 9 , Sayı 1, Sayfalar 39 - 46 2021-01-29

Extending Wireless Sensor Networks’ Lifetimes Using Deep Reinforcement Learning in a Software-Defined Network Architecture
Extending Wireless Sensor Networks’ Lifetimes Using Deep Reinforcement Learning in a Software-Defined Network Architecture

Zainab ABBOOD [1] , Mahmoud SHUKER [2] , Çağatay AYDIN [3] , Doğu Çağdaş ATİLLA [4]


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 a good potential toward 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 on 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.
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 a good potential toward 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 on 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.
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Birincil Dil en
Konular Mühendislik
Yayınlanma Tarihi Ocak 2021
Bölüm Makaleler
Yazarlar

Orcid: 0000-0001-8822-4866
Yazar: Zainab ABBOOD (Sorumlu Yazar)
Kurum: Zainab Abbood
Ülke: Iraq


Orcid: 0000-0001-7211-8460
Yazar: Mahmoud SHUKER
Kurum: Al-Mansour University College
Ülke: Iraq


Orcid: 0000-0002-1895-0333
Yazar: Çağatay AYDIN
Kurum: ALTINBAŞ ÜNİVERSİTESİ
Ülke: Turkey


Orcid: 0000-0002-4249-6951
Yazar: Doğu Çağdaş ATİLLA
Kurum: ALTINBAŞ ÜNİVERSİTESİ
Ülke: Turkey


Tarihler

Başvuru Tarihi : 11 Şubat 2020
Kabul Tarihi : 29 Eylül 2020
Yayımlanma Tarihi : 29 Ocak 2021

IEEE Z. Abbood , M. Shuker , Ç. Aydın ve D. Atilla , "Extending Wireless Sensor Networks’ Lifetimes Using Deep Reinforcement Learning in a Software-Defined Network Architecture", Academic Platform Journal of Engineering and Science, c. 9, sayı. 1, ss. 39-46, Oca. 2021, doi:10.21541/apjes.687496