Optimal data transmission and pathfinding for WSN and decentralized IoT systems using I-GWO and Ex-GWO algorithms

Basit Öğe Kaydı
dc.authoridAmir Seyyedabbasi / 0000-0001-5186-4499en_US
dc.authoridFarzad Kiani / 0000-0002-0354-9344en_US
dc.authoridTofigh Allahviranloo / 0000-0002-6673-3560en_US
dc.authorscopusidAmir Seyyedabbasi / 57202833910en_US
dc.authorscopusidFarzad Kiani / 36662461100en_US
dc.authorscopusidTofigh Allahviranloo / 8834494700en_US
dc.authorwosidAmir Seyyedabbasi / HJH-7387-2023en_US
dc.authorwosidFarzad Kiani / O-3363-2013en_US
dc.authorwosidTofigh Allahviranloo / V-4843-2019en_US
dc.contributor.authorSeyyedabbasi, Amir
dc.contributor.authorKiani, Farzad
dc.contributor.authorAllahviranloo, Tofigh
dc.contributor.authorFernandez-Gamiz, Unai
dc.contributor.authorNoeiaghdam, Samad
dc.date.accessioned2022-10-11T06:31:34Z
dc.date.available2022-10-11T06:31:34Z
dc.date.issued2023en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümüen_US
dc.description.abstractEfficient resource use is a very important issue in wireless sensor networks and decentralized IoT-based systems. In this context, a smooth pathfinding mechanism can achieve this goal. However, since this problem is a Non-deterministic Polynomial-time (NP-hard) problem type, metaheuristic algorithms can be used. This article proposes two new energy-efficient routing methods based on Incremental Grey Wolf Optimization (I-GWO) and Expanded Grey Wolf Optimization (Ex-GWO) algorithms to find optimal paths. Moreover, in this study, a general architecture has been proposed, making it possible for many different metaheuristic algorithms to work in an adaptive manner as well as these algorithms. In the proposed methods, a new fitness function is defined to determine the next hop based on some parameters such as residual energy, traffic, distance, buffer size and hop size. These parameters are important measurements in subsequent node selections. The main purpose of these methods is to minimize traffic, improve fault tolerance in related systems, and increase reliability and lifetime. The two metaheuristic algorithms mentioned above are used to find the best values ??for these parameters. The suggested methods find the best path of any length for the path between any source and destination node. In this study, no ready dataset was used, and the established network and system were run in the simulation environment. As a result, the optimal path has been discovered in terms of the minimum cost of the best paths obtained by the proposed methods. These methods can be very useful in decentralized peer-to-peer and distributed systems. The metrics for performance evaluation and comparisons are i) network lifetime, ii) the alive node ratio in the network, iii) the packet delivery ratio and lost data packets, iv) routing overhead, v) throughput, and vi) convergence behavior. According to the results, the proposed methods generally choose the most suitable and efficient ways with minimum cost. These methods are compared with Genetic Algorithm Based Routing (GAR), Artificial Bee Colony Based routing (ABCbased), Multi-Agent Protocol based on Ant Colony Optimization (MAP-ACO), and Wireless Sensor Networks based on Grey Wolf optimizer. (GWO-WSN) algorithms. The simulation results show that the proposed methods outperform the others.en_US
dc.identifier.citationSeyyedabbasi, A., Kiani, F., Allahviranloo, T., Fernandez-Gamiz, U., & Noeiaghdam, S. (2023). Optimal data transmission and pathfinding for WSN and decentralized IoT systems using I-GWO and ex-GWO algorithms. Alexandria Engineering Journal, 63, 339-357. doi:10.1016/j.aej.2022.08.009en_US
dc.identifier.doi10.1016/j.aej.2022.08.009en_US
dc.identifier.endpage357en_US
dc.identifier.issn1110-0168en_US
dc.identifier.scopus2-s2.0-85136255874en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage339en_US
dc.identifier.urihttps://doi.org/10.1016/j.aej.2022.08.009
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3184
dc.identifier.volume63en_US
dc.identifier.wosWOS:000888011600001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorSeyyedabbasi, Amir
dc.institutionauthorKiani, Farzad
dc.institutionauthorAllahviranloo, Tofigh
dc.language.isoenen_US
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAlexandria Engineering Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDIoTen_US
dc.subjectMetaheuristic Algorithmen_US
dc.subjectPathfindingen_US
dc.subjectSwarm Intelligenceen_US
dc.subjectWSNen_US
dc.titleOptimal data transmission and pathfinding for WSN and decentralized IoT systems using I-GWO and Ex-GWO algorithmsen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
Ä°sim:
1-s2.0-S1110016822005324-main.pdf
Boyut:
3.79 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
Ä°ndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
Ä°sim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
Ä°ndir

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu
Yazılım Mühendisliği Bölümü Makale Koleksiyonu