A hybrid approach for latency and battery lifetime optimization in IoT devices through offloading and CNN learning

dc.authorid Heidari, Arash/0000-0003-4279-8551
dc.authorwosid Heidari, Arash/AAK-9761-2021
dc.contributor.author Jafari Navimipour, Nima
dc.contributor.author Navimipour, Nima Jafari
dc.contributor.author Jamali, Mohammad Ali Jabraeil
dc.contributor.author Akbarpour, Shahin
dc.contributor.other Computer Engineering
dc.date.accessioned 2023-10-19T15:11:41Z
dc.date.available 2023-10-19T15:11:41Z
dc.date.issued 2023
dc.department-temp [Heidari, Arash] Islamic Azad Univ, Dept Comp Engn, Tabriz Branch, Tabriz, Iran; [Heidari, Arash; Jamali, Mohammad Ali Jabraeil; Akbarpour, Shahin] Islamic Azad Univ, Dept Comp Engn, Shabestar Branch, Shabestar, Iran; [Navimipour, Nima Jafari] Kadir Has Univ, Dept Comp Engn, Istanbul, Turkiye; [Navimipour, Nima Jafari] Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, Touliu 64002, Yunlin, Taiwan en_US
dc.description.abstract Offloading assists in overcoming the resource constraints of specific elements, making it one of the primary technical enablers of the Internet of Things (IoT). IoT devices with low battery capacities can use the edge to offload some of the operations, which can significantly reduce latency and lengthen battery lifetime. Due to their restricted battery capacity, deep learning (DL) techniques are more energy-intensive to utilize in IoT devices. Because many IoT devices lack such modules, numerous research employed energy harvester modules that are not available to IoT devices in real-world circumstances. Using the Markov Decision Process (MDP), we describe the offloading problem in this study. Next, to facilitate partial offloading in IoT devices, we develop a Deep Reinforcement learning (DRL) method that can efficiently learn the policy by adjusting to network dynamics. Convolutional Neural Network (CNN) is then offered and implemented on Mobile Edge Computing (MEC) devices to expedite learning. These two techniques operate together to offer the proper offloading approach throughout the length of the system's operation. Moreover, transfer learning was employed to initialize the Qtable values, which increased the system's effectiveness. The simulation in this article, which employed Cooja and TensorFlow, revealed that the strategy outperformed five benchmarks in terms of latency by 4.1%, IoT device efficiency by 2.9%, energy utilization by 3.6%, and job failure rate by 2.6% on average. en_US
dc.identifier.citationcount 23
dc.identifier.doi 10.1016/j.suscom.2023.100899 en_US
dc.identifier.issn 2210-5379
dc.identifier.issn 2210-5387
dc.identifier.scopus 2-s2.0-85166020565 en_US
dc.identifier.scopusquality Q1
dc.identifier.uri https://doi.org/10.1016/j.suscom.2023.100899
dc.identifier.uri https://hdl.handle.net/20.500.12469/5166
dc.identifier.volume 39 en_US
dc.identifier.wos WOS:001061194900001 en_US
dc.identifier.wosquality Q1
dc.khas 20231019-WoS en_US
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.relation.ispartof Sustainable Computing-Informatics & Systems en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 47
dc.subject Offloading en_US
dc.subject Convolutional neural network en_US
dc.subject IoT en_US
dc.subject Energy utilization en_US
dc.subject Model En_Us
dc.subject Edge en_US
dc.subject Deep reinforcement learning en_US
dc.subject Model
dc.subject Markov decision process en_US
dc.title A hybrid approach for latency and battery lifetime optimization in IoT devices through offloading and CNN learning en_US
dc.type Article en_US
dc.wos.citedbyCount 41
dspace.entity.type Publication
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