Maximizing coverage and maintaining connectivity in WSN and decentralized IoT: an efficient metaheuristic-based method for environment-aware node deployment

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dc.authoridAmir Seyyedabbasi / 0000-0001-5186-4499en_US
dc.authorscopusidAmir Seyyedabbasi / 57202833910en_US
dc.authorwosidAmir Seyyedabbasi / GFG-1335-2022en_US
dc.contributor.authorNematzadeh, Sajjad
dc.contributor.authorTorkamanian-Afshar, Mahsa
dc.contributor.authorSeyyedabbasi, Amir
dc.contributor.authorKiani, Farzad
dc.date.accessioned2022-10-31T08:02:52Z
dc.date.available2022-10-31T08:02:52Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümüen_US
dc.description.abstractThe node deployment problem is a non-deterministic polynomial time (NP-hard). This study proposes a new and efficient method to solve this problem without the need for predefined circumstances about the environments independent of terrain. The proposed method is based on a metaheuristic algorithm and mimics the grey wolf optimizer (GWO) algorithm. In this study, we also suggested an enhanced version of the GWO algorithm to work adaptively in such problems and named it Mutant-GWO (MuGWO). Also, the suggested model ensures connectivity by generating topology graphs and potentially supports data transmission mechanisms. Therefore, the proposed method based on MuGWO can enhance resources utilization, such as reducing the number of nodes, by maximizing the coverage rate and maintaining the connectivity. While most studies assume classical rectangle uniform environments, this study also focuses on custom (environment-aware) maps in line with the importance and requirements of the real world. The motivation of supporting custom maps by this study is that environments can consist of custom shapes with prioritized and critical areas. In this way, environment awareness halts the deployment of nodes in undesired regions and averts resource waste. Besides, novel multi-purpose fitness functions of the proposed method satisfy a convenient approach to calculate costs instead of using complicated processes. Accordingly, this method is suitable for large-scale networks thanks to the capability of the distributed architecture and the metaheuristic-based approach. This study justifies the improvements in the suggested model by presenting comparisons with a Deterministic Grid-based approach and the Original GWO. Moreover, this method outperforms the fruit fly optimization algorithm, bat algorithm (BA), Optimized BA, harmony search, and improved dynamic deployment technique based on genetic algorithm methods in declared scenarios in literature, considering the results of simulations.en_US
dc.identifier.citationNematzadeh, S., Torkamanian-Afshar, M., Seyyedabbasi, A., & Kiani, F. (2022). Maximizing coverage and maintaining connectivity in WSN and decentralized IoT: An efficient metaheuristic-based method for environment-aware node deployment. Neural Computing and Applications, doi:10.1007/s00521-022-07786-1en_US
dc.identifier.doi10.1007/s00521-022-07786-1en_US
dc.identifier.scopus2-s2.0-85138231123en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1007/s00521-022-07786-1
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3211
dc.identifier.wosWOS:000857798800002en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorSeyyedabbasi, Amir
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofNeural Computing and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConnectivityen_US
dc.subjectCoverageen_US
dc.subjectDIoTen_US
dc.subjectEnvironment Awareen_US
dc.subjectMetaheuristicen_US
dc.subjectMutant GWOen_US
dc.subjectNode deploymenten_US
dc.subjectWSNen_US
dc.titleMaximizing coverage and maintaining connectivity in WSN and decentralized IoT: an efficient metaheuristic-based method for environment-aware node deploymenten_US
dc.typeArticleen_US

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