Deep Q-Learning Technique for Offloading Offline/Online Computation in Blockchain-Enabled Green IoT-Edge Scenarios

dc.authoridHeidari, Arash/0000-0003-4279-8551
dc.authoridJabraeil Jamali, Mohammad Ali/0000-0001-7687-5469
dc.authoridJafari Navimipour, Nima/0000-0002-5514-5536
dc.authorwosidHeidari, Arash/AAK-9761-2021
dc.authorwosidJabraeil Jamali, Mohammad Ali/I-8032-2019
dc.authorwosidJafari Navimipour, Nima/AAF-5662-2021
dc.contributor.authorHeidari, Arash
dc.contributor.authorJamali, Mohammad Ali Jabraeil
dc.contributor.authorNavimipour, Nima Jafari
dc.contributor.authorAkbarpour, Shahin
dc.date.accessioned2023-10-19T15:12:02Z
dc.date.available2023-10-19T15:12:02Z
dc.date.issued2022
dc.department-temp[Heidari, Arash; Jamali, Mohammad Ali Jabraeil; Akbarpour, Shahin] Islamic Azad Univ, Dept Comp Engn, Shabestar Branch, Shabestar 5381637181, Iran; [Navimipour, Nima Jafari] Islamic Azad Univ, Dept Comp Engn, Tabriz Branch, Tabriz 5157944533, Iran; [Navimipour, Nima Jafari] Kadir Has Univ, Fac Engn & Nat Sci, Dept Comp Engn, TR-34083 Istanbul, Turkeyen_US
dc.description.abstractThe number of Internet of Things (IoT)-related innovations has recently increased exponentially, with numerous IoT objects being invented one after the other. Where and how many resources can be transferred to carry out tasks or applications is known as computation offloading. Transferring resource-intensive computational tasks to a different external device in the network, such as a cloud, fog, or edge platform, is the strategy used in the IoT environment. Besides, offloading is one of the key technological enablers of the IoT, as it helps overcome the resource limitations of individual objects. One of the major shortcomings of previous research is the lack of an integrated offloading framework that can operate in an offline/online environment while preserving security. This paper offers a new deep Q-learning approach to address the IoT-edge offloading enabled blockchain problem using the Markov Decision Process (MDP). There is a substantial gap in the secure online/offline offloading systems in terms of security, and no work has been published in this arena thus far. This system can be used online and offline while maintaining privacy and security. The proposed method employs the Post Decision State (PDS) mechanism in online mode. Additionally, we integrate edge/cloud platforms into IoT blockchain-enabled networks to encourage the computational potential of IoT devices. This system can enable safe and secure cloud/edge/IoT offloading by employing blockchain. In this system, the master controller, offloading decision, block size, and processing nodes may be dynamically chosen and changed to reduce device energy consumption and cost. TensorFlow and Cooja's simulation results demonstrated that the method could dramatically boost system efficiency relative to existing schemes. The findings showed that the method beats four benchmarks in terms of cost by 6.6%, computational overhead by 7.1%, energy use by 7.9%, task failure rate by 6.2%, and latency by 5.5% on average.en_US
dc.identifier.citation27
dc.identifier.doi10.3390/app12168232en_US
dc.identifier.issn2076-3417
dc.identifier.issue16en_US
dc.identifier.scopus2-s2.0-85137331418en_US
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/app12168232
dc.identifier.urihttps://hdl.handle.net/20.500.12469/5323
dc.identifier.volume12en_US
dc.identifier.wosWOS:000846970600001en_US
dc.identifier.wosqualityN/A
dc.khas20231019-WoSen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofApplied Sciences-Baselen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectResource-AllocationEn_Us
dc.subjectOptimizationEn_Us
dc.subjectIdentificationEn_Us
dc.subjectAggregationEn_Us
dc.subjectNetworksEn_Us
dc.subjectAwareEn_Us
dc.subjectSmartEn_Us
dc.subjectResource-Allocation
dc.subjectOptimization
dc.subjectBlockchainen_US
dc.subjectIdentification
dc.subjectdeep learningen_US
dc.subjectAggregation
dc.subjectIoTen_US
dc.subjectNetworks
dc.subjectOffloadingen_US
dc.subjectAware
dc.subjectQoSen_US
dc.subjectSmart
dc.subjectprivacyen_US
dc.titleDeep Q-Learning Technique for Offloading Offline/Online Computation in Blockchain-Enabled Green IoT-Edge Scenariosen_US
dc.typeArticleen_US
dspace.entity.typePublication

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