{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:35:47Z","timestamp":1754156147908,"version":"3.41.2"},"reference-count":28,"publisher":"Emerald","issue":"5","license":[{"start":{"date-parts":[[2020,10,30]],"date-time":"2020-10-30T00:00:00Z","timestamp":1604016000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJWIS"],"published-print":{"date-parts":[[2020,10,30]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>With the advent of the web computing era, the transmission mode of the Internet of Everything has caused an explosion in data volume, which has brought severe challenges to traditional routing protocols. The limitations of the existing routing protocols under the condition of rapid data growth are elaborated, and the routing problem is remodeled as a Markov decision process. this paper aims to solve the problem of high blocking probability due to the increase in data volume by combining deep reinforcement learning. Finally, the correctness of the proposed algorithm in this paper is verified by simulation.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>The limitations of the existing routing protocols under the condition of rapid data growth are elaborated and the routing problem is remodeled as a Markov decision process. Based on this, a deep reinforcement learning method is used to select the next-hop router for each data transmission task, thereby minimizing the length of the data transmission path while avoiding data congestion.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>Simulation results show that the proposed method can significantly reduce the probability of data congestion and increase network throughput.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This paper proposes an intelligent routing algorithm for the network congestion caused by the explosive growth of data volume in the future of the big data era. With the help of deep reinforcement learning, it is possible to dynamically select the transmission jump router according to the current network state, thereby reducing the probability of congestion and improving network throughput.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijwis-08-2020-0055","type":"journal-article","created":{"date-parts":[[2020,10,29]],"date-time":"2020-10-29T01:16:28Z","timestamp":1603934188000},"page":"529-544","source":"Crossref","is-referenced-by-count":3,"title":["Optimal path strategy for the web computing under deep reinforcement learning"],"prefix":"10.1108","volume":"16","author":[{"given":"Mu","family":"Shengdong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wang","family":"Fengyu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiong","family":"Zhengxian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuang","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhang","family":"Lunfeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"article-title":"Deep reinforcement learning paradigm for dense wireless networks in smart cities","volume-title":"In Smart Cities Performability, Cognition, and Security","year":"2020","key":"key2020110603180688000_ref001"},{"issue":"1","key":"key2020110603180688000_ref002","article-title":"Traffic signs detection for real-world application of an advanced driving assisting system using deep learning","volume":"51","year":"2019","journal-title":"Neural Processing Letters"},{"issue":"3","key":"key2020110603180688000_ref003","article-title":"Sheldon, etc using cross-classified multivariate mixed response models with application to life history traits in great tits (parus major)","volume":"7","year":"2007","journal-title":"Statistical Modelling: An International Journal"},{"issue":"3","key":"key2020110603180688000_ref004","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/TCSS.2016.2527158","article-title":"Guest editorial on advances in tools and techniques for enabling cyberphysical social systems \u2013 part I","volume":"2","year":"2015","journal-title":"IEEE Transactions on Computational Social Systems"},{"issue":"5","key":"key2020110603180688000_ref005","first-page":"922","article-title":"Nuclear energy 5. 0: new formation and system architecture of nuclear power industry in the new IT era","volume":"44","year":"2018","journal-title":"Acta Automatica Sinica"},{"issue":"1","key":"key2020110603180688000_ref006","first-page":"1","article-title":"Software-defifined systems and knowledge automation: a parallel paradigm shift from newton to merton","volume":"41","year":"2015","journal-title":"Acta Automatica Sinica"},{"first-page":"1","volume-title":"Applications of Evolutionary Computing","year":"2001","key":"key2020110603180688000_ref007"},{"year":"1988","key":"key2020110603180688000_ref008","article-title":"Routing information protocol"},{"article-title":"Deep learning for multimedia data in IoT","volume-title":"in Multimedia Big Data Computing for IoT Applications","year":"2020","key":"key2020110603180688000_ref009"},{"issue":"3","key":"key2020110603180688000_ref010","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1109\/MWC.2016.1600317WC","article-title":"The deep learning vision for heterogeneous network traffic control: proposal, challenges and future perspective","volume":"24","year":"2017","journal-title":"IEEE Wireless Communications"},{"issue":"1","key":"key2020110603180688000_ref011","doi-asserted-by":"publisher","first-page":"18","DOI":"10.4018\/IJMCMC.2019010102","article-title":"Research on reliability and validity of mobile networks-based automated writing evaluation","volume":"10","year":"2019","journal-title":"International Journal of Mobile Computing and Multimedia Communications (IJMCMC)"},{"article-title":"An effective deep learning neural network model for short\u2010term load forecasting","volume-title":"Concurrency and Computation Practice and Experience","year":"2020","key":"key2020110603180688000_ref012"},{"issue":"3","key":"key2020110603180688000_ref013","doi-asserted-by":"publisher","first-page":"23","DOI":"10.4018\/IJMCMC.2019070102","article-title":"Mobile edge computing: cost-efficient content delivery in resource-constrained mobile computing environment","volume":"10","year":"2019","journal-title":"International Journal of Mobile Computing and Multimedia Communications"},{"article-title":"A data preprocessing method to classify and summarize aspect-based opinions using deep learning","volume-title":"in Intelligent Information and Database Systems","year":"2019","key":"key2020110603180688000_ref014"},{"issue":"10","key":"key2020110603180688000_ref015","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/MSPEC.2016.7572524","article-title":"The internet of fewer things","volume":"53","year":"2016","journal-title":"IEEE Spectrum"},{"issue":"1","key":"key2020110603180688000_ref016","first-page":"844","article-title":"IoT communications with M-PSK modulated ambient back scatter: algorithm, analysis and implementation","volume":"6","year":"2019","journal-title":"IEEE Web Computing Journal"},{"issue":"1","key":"key2020110603180688000_ref017","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MC.2017.9","article-title":"The emergence of edge computing","volume":"50","year":"2017","journal-title":"Computer"},{"issue":"5","key":"key2020110603180688000_ref018","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/JIOT.2016.2579198","article-title":"Edge computing: vision and challenges","volume":"3","year":"2016","journal-title":"IEEE Internet of Things Journal"},{"issue":"5","key":"key2020110603180688000_ref019","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1109\/MC.2016.145","article-title":"The promise of edge computing","volume":"49","year":"2016","journal-title":"Computer"},{"issue":"1","key":"key2020110603180688000_ref020","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1109\/MWC.2017.1700244","article-title":"On removing routing protocol from future wireless networks: a real-time deep learning approach for intelligent traffic control","volume":"25","year":"2018","journal-title":"IEEE Wireless Communications"},{"issue":"15\/16","key":"key2020110603180688000_ref021","article-title":"Integrating compositional pattern-producing networks and optimized convolution neural networks using deep learning techniques for detecting brain abnormalities","volume":"79","year":"2019","journal-title":"Multimedia Tools and Applications"},{"issue":"5","key":"key2020110603180688000_ref022","first-page":"907","article-title":"Edge computing an emerging computing model for the internet of everything era","volume":"54","year":"2017","journal-title":"Journal of Computer Research and Development"},{"issue":"4","key":"key2020110603180688000_ref023","first-page":"481","article-title":"Blockchain: the state of the art and future trends","volume":"42","year":"2016","journal-title":"Acta Automatica Sinica"},{"article-title":"Token economics in energy systems: concept, functionality and applications","volume-title":"Eprintar Xiv: 1808. 01261","year":"2018","key":"key2020110603180688000_ref024"},{"issue":"9","key":"key2020110603180688000_ref025","first-page":"1544","article-title":"Blockchain based intelligent distributed electrical energy systems: needs, concepts, approaches and vision","volume":"43","year":"2017","journal-title":"Acta Automatica Sinica"},{"issue":"4","key":"key2020110603180688000_ref026","doi-asserted-by":"publisher","first-page":"34","DOI":"10.4018\/IJMCMC.2018100103","article-title":"Deep reinforcement learning for mobile video offloading in heterogeneous cellular networks","volume":"9","year":"2018","journal-title":"International Journal of Mobile Computing and Multimedia Communications (IJMCMC)"},{"issue":"12","key":"key2020110603180688000_ref027","first-page":"1080","article-title":"Formal verifification method of smart contract","volume":"2","year":"2016","journal-title":"Journal of Information Securyity Research"},{"article-title":"Optimization of multitask parallel mobile edge computing strategy based on deep learning architecture","volume-title":"Design Automation for Embedded Systems","year":"2019","key":"key2020110603180688000_ref028"}],"container-title":["International Journal of Web Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJWIS-08-2020-0055\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJWIS-08-2020-0055\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:24:15Z","timestamp":1753395855000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/ijwis\/article\/16\/5\/529-544\/164660"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,30]]},"references-count":28,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2020,10,30]]}},"alternative-id":["10.1108\/IJWIS-08-2020-0055"],"URL":"https:\/\/doi.org\/10.1108\/ijwis-08-2020-0055","relation":{},"ISSN":["1744-0084","1744-0084"],"issn-type":[{"type":"print","value":"1744-0084"},{"type":"print","value":"1744-0084"}],"subject":[],"published":{"date-parts":[[2020,10,30]]}}}