{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T11:25:10Z","timestamp":1770895510385,"version":"3.50.1"},"reference-count":55,"publisher":"Wiley","license":[{"start":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T00:00:00Z","timestamp":1673913600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2023,1,17]]},"abstract":"<jats:p>In the fog computing paradigm, if the computing resources of an end device are insufficient, the user\u2019s tasks can be offloaded to nearby devices or the central cloud. In addition, due to the limited energy of mobile devices, optimal offloading is crucial. The method presented in this paper is based on the auction theory, which has been used in recent studies to optimize computation offloading. We propose a bid prediction mechanism using Q-learning. Nodes participating in the auction announce a bid value to the auctioneer entity, and the node with the highest bid value is the auction winner. Then, only the winning node has the right to offload the tasks on its upstream (parent) node. The main idea behind Q-learning is that it is stateless and only considers the current state to perform an action. The evaluation results show that the bid values predicted by the Q-learning method are near-optimal. On average, the proposed method consumes less energy than traditional and state-of-the-art techniques. Also, it reduces the execution time of tasks and leads to less consumption of network resources.<\/jats:p>","DOI":"10.1155\/2023\/5222504","type":"journal-article","created":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T22:06:31Z","timestamp":1673993191000},"page":"1-20","source":"Crossref","is-referenced-by-count":20,"title":["An Auction-Based Bid Prediction Mechanism for Fog-Cloud Offloading Using Q-Learning"],"prefix":"10.1155","volume":"2023","author":[{"given":"Reza","family":"Besharati","sequence":"first","affiliation":[{"name":"Department of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0292-7008","authenticated-orcid":true,"given":"Mohammad Hossein","family":"Rezvani","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran"}]},{"given":"Mohammad Mehdi","family":"Gilanian Sadeghi","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1007\/s10723-021-09576-w"},{"key":"2","doi-asserted-by":"crossref","first-page":"e4363","DOI":"10.1002\/ett.4363","article-title":"Mobile\u2010fog\u2010cloud assisted deep reinforcement learning and blockchain\u2010enable IoMT system for healthcare workflows","author":"A. Lakhan","year":"2021","journal-title":"Transactions on Emerging Telecommunications Technologies"},{"key":"3","first-page":"372","article-title":"Architecture for latency reduction in healthcare internet-of-things using reinforcement learning and fuzzy based fog computing","author":"S. Shukla"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1109\/tgcn.2019.2960767"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1109\/tsipn.2022.3171336"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1109\/jiot.2020.2996213"},{"key":"7","first-page":"1","article-title":"Deep reinforcement learning for fog computing-based vehicular system with multi-operator support","author":"X. Zhang"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1109\/tvt.2020.3041929"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1016\/j.suscom.2018.10.006"},{"issue":"3","key":"10","first-page":"32","article-title":"Energy-efficient computation offloading and resource allocation in fog computing for internet of everything","volume":"16","author":"Q. Li","year":"2019","journal-title":"China Communications"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2020.107527"},{"key":"12","first-page":"542","article-title":"A prototype auction-based mechanism for computation offloading in fog-cloud environments","author":"R. Besharati"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1109\/lwc.2019.2911521"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1109\/jsac.2019.2906793"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-021-03388-2"},{"key":"16","volume-title":"Reinforcement Learning: An Introduction","author":"R. S. Sutton","year":"2018"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-022-07245-y"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1109\/comst.2020.2988367"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2022.103354"},{"key":"20","article-title":"Resource provisioning in fog computing through deep reinforcement learning","author":"J. Santos"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.09.060"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.23919\/icn.2020.0020"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-02561-3"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2021.04.028"},{"key":"25","doi-asserted-by":"publisher","DOI":"10.1109\/jiot.2020.3009540"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.1109\/jiot.2018.2878435"},{"key":"27","first-page":"1","article-title":"Deep reinforced energy efficient traffic grooming in fog-cloud elastic optical networks","author":"R. Zhu"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1108\/ijicc-03-2020-0021"},{"key":"29","first-page":"589","article-title":"An efficient service dispersal mechanism for fog and cloud computing using deep reinforcement learning","author":"C. K. Dehury"},{"key":"30","doi-asserted-by":"crossref","article-title":"Energy-efficient solution based on reinforcement learning approach in fog networks","author":"A. Mebrek","DOI":"10.1109\/IWCMC.2019.8766441"},{"key":"31","doi-asserted-by":"publisher","DOI":"10.3390\/electronics8111287"},{"key":"32","first-page":"522","article-title":"Personalized service delivery using reinforcement learning in fog and cloud environment","author":"C. K. Dehury"},{"key":"33","doi-asserted-by":"publisher","DOI":"10.3390\/electronics9091501"},{"key":"34","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2939735"},{"key":"35","first-page":"661","article-title":"Real-time bidding with multi-agent reinforcement learning in display advertising","author":"J. Jin"},{"key":"36","first-page":"1077","article-title":"Optimal Real-Time Bidding for Display Advertising","author":"W. Zhang"},{"key":"37","first-page":"661","article-title":"Real-time bidding by reinforcement learning in display advertising","author":"C. Han"},{"key":"38","first-page":"711","article-title":"Improving real-time bidding using a constrained Markov decision process","author":"M. Du"},{"key":"39","article-title":"Scalable traffic signal controls using fog-cloud based multiagent reinforcement learning","author":"S. Chen","year":"2021"},{"key":"40","article-title":"SecOFF-FCIoT: machine learning based secure offloading in Fog-Cloud of things for smart city applications","volume":"7","author":"A. A. Alli","year":"2019","journal-title":"Internet of Things"},{"key":"41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11042-021-10840-0","article-title":"Reinforcement learning for medical information processing over heterogeneous networks","volume":"80","author":"A. Kishor","year":"2021","journal-title":"Multimedia Tools and Applications"},{"key":"42","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-01768-8"},{"key":"43","first-page":"1","article-title":"Managing fog networks using reinforcement learning based load balancing algorithm","author":"J. Y. Baek"},{"key":"44","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1002\/9781119675525.ch7","article-title":"Reinforcement learning for service function chain allocation in fog computing","volume-title":"Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning","author":"J. Santos","year":"2021"},{"key":"45","first-page":"494","article-title":"A nested two stage game-based optimization framework in mobile cloud computing system","author":"Y. Wang"},{"key":"46","doi-asserted-by":"publisher","DOI":"10.1109\/tcc.2015.2449834"},{"key":"47","first-page":"60","volume-title":"Basic Queueing Theory","author":"J. Sztrik","year":"2012"},{"key":"48","doi-asserted-by":"crossref","DOI":"10.1002\/0471791571","volume-title":"Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications","author":"G. Bolch","year":"2006"},{"key":"49","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2780236"},{"key":"50","doi-asserted-by":"publisher","DOI":"10.1002\/spe.2509"},{"key":"51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10586-022-03542-1","article-title":"Partial offloading with stable equilibrium in fog-cloud environments using replicator dynamics of evolutionary game theory","volume":"25","author":"M. H. Khoobkar","year":"2022","journal-title":"Cluster Computing"},{"key":"52","article-title":"Joint optimization of delay and energy in partial offloading using dual-population replicator dynamics","volume":"216","author":"M. H. Khoobkar","year":"2022","journal-title":"Expert Systems with Applications"},{"key":"53","doi-asserted-by":"publisher","DOI":"10.1007\/s11277-021-08684-w"},{"key":"54","doi-asserted-by":"publisher","DOI":"10.1007\/s11235-020-00711-8"},{"key":"55","volume-title":"Auction Theory","author":"V. Krishna","year":"2009"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2023\/5222504.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2023\/5222504.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2023\/5222504.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T21:59:18Z","timestamp":1701727158000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/complexity\/2023\/5222504\/"}},"subtitle":[],"editor":[{"given":"Roberto","family":"Natella","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2023,1,17]]},"references-count":55,"alternative-id":["5222504","5222504"],"URL":"https:\/\/doi.org\/10.1155\/2023\/5222504","relation":{},"ISSN":["1099-0526","1076-2787"],"issn-type":[{"value":"1099-0526","type":"electronic"},{"value":"1076-2787","type":"print"}],"subject":[],"published":{"date-parts":[[2023,1,17]]}}}