{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T17:53:29Z","timestamp":1764784409348},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,3,30]],"date-time":"2020-03-30T00:00:00Z","timestamp":1585526400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,3,30]],"date-time":"2020-03-30T00:00:00Z","timestamp":1585526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Wireless Com Network"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>With the benefit of partially or entirely offloading computations to a nearby server, mobile edge computing gives user equipment (UE) more powerful capability to run computationally intensive applications. However, a critical challenge emerged: how to select the optimal set of components to offload considering the UE performance as well as its battery usage constraints. In this paper, we propose a novel energy and performance efficient deep learning based offloading algorithm. The optimal offloading schemes of components based on remaining energy and its performance can be determined by our proposed algorithm. All of these considerations are modeled as a cost function; then, a deep learning network is trained to compute the solution by which the optimal offloading scheme can be determined. Experimental results show that the proposed method is superior to existing methods in terms of energy and performance constraints.<\/jats:p>","DOI":"10.1186\/s13638-020-01678-5","type":"journal-article","created":{"date-parts":[[2020,3,30]],"date-time":"2020-03-30T19:04:03Z","timestamp":1585595043000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Deep learning-based computation offloading with energy and performance optimization"],"prefix":"10.1186","volume":"2020","author":[{"given":"Yongsheng","family":"Gong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Congmin","family":"Lv","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suzhi","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Houpeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,3,30]]},"reference":[{"issue":"18","key":"1678_CR1","doi-asserted-by":"publisher","first-page":"1587","DOI":"10.1002\/wcm.1203","volume":"13","author":"H. T. Dinh","year":"2013","unstructured":"H. T. Dinh, C. Lee, D. Niyato, P. Wang, A survey of mobile cloud computing: architecture, applications, and approaches. Wirel. Commun. Mob. Comput.13(18), 1587\u20131611 (2013). https:\/\/doi.org\/10.1002\/wcm.1203.","journal-title":"Wirel. Commun. Mob. Comput."},{"issue":"1","key":"1678_CR2","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1109\/SURV.2013.062613.00160","volume":"16","author":"A. U. R. Khan","year":"2014","unstructured":"A. U. R. Khan, M. Othman, S. A. Madani, S. U. Khan, A survey of mobile cloud computing application models. IEEE Commun. Surv. Tutor.16(1), 393\u2013413 (2014). https:\/\/doi.org\/10.1109\/surv.2013.062613.00160.","journal-title":"IEEE Commun. Surv. Tutor."},{"issue":"4","key":"1678_CR3","doi-asserted-by":"publisher","first-page":"1607","DOI":"10.1007\/s11277-014-2102-7","volume":"80","author":"Y. Wang","year":"2015","unstructured":"Y. Wang, I. -R. Chen, D. -C. Wang, A survey of mobile cloud computing applications: perspectives and challenges. Wirel. Pers. Commun.80(4), 1607\u20131623 (2015). https:\/\/doi.org\/10.1007\/s11277-014-2102-7.","journal-title":"Wirel. Pers. Commun."},{"issue":"3","key":"1678_CR4","doi-asserted-by":"publisher","first-page":"1628","DOI":"10.1109\/COMST.2017.2682318","volume":"19","author":"P. Mach","year":"2017","unstructured":"P. Mach, Z. Becvar, Mobile edge computing: a survey on architecture and computation offloading. IEEE Commun. Surv. Tutor.19(3), 1628\u20131656 (2017). https:\/\/doi.org\/10.1109\/comst.2017.2682318.","journal-title":"IEEE Commun. Surv. Tutor."},{"issue":"6","key":"1678_CR5","doi-asserted-by":"publisher","first-page":"3538","DOI":"10.1109\/TII.2019.2896965","volume":"15","author":"J. Xu","year":"2019","unstructured":"J. Xu, S. Wang, B. K. Bhargava, F. Yang, A blockchain-enabled trustless crowd-intelligence ecosystem on mobile edge computing. Ieee Trans. Ind. Inf.15(6), 3538\u20133547 (2019). https:\/\/doi.org\/10.1109\/tii.2019.2896965.","journal-title":"Ieee Trans. Ind. Inf."},{"issue":"3","key":"1678_CR6","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1109\/MCOM.2015.7060486","volume":"53","author":"H. Flores","year":"2015","unstructured":"H. Flores, P. Hui, S. Tarkoma, Y. Li, S. Srirama, R. Buyya, Mobile code offloading: from concept to practice and beyond. IEEE Commun. Mag.53(3), 80\u201388 (2015). https:\/\/doi.org\/10.1109\/mcom.2015.7060486.","journal-title":"IEEE Commun. Mag."},{"key":"1678_CR7","unstructured":"L. Jiao, R. Friedman, X. Fu, S. Secci, Z. Smoreda, H. Tschofenig, in 2013 Future Network & Mobile Summit. Cloud-based computation offloading for mobile devices: state of the art, challenges and opportunities. (IEEE), pp. 1\u201311. https:\/\/ieeexplore.ieee.org\/document\/6633526."},{"key":"1678_CR8","doi-asserted-by":"publisher","first-page":"6757","DOI":"10.1109\/ACCESS.2017.2685434","volume":"5","author":"S. Wang","year":"2017","unstructured":"S. Wang, X. Zhang, Y. Zhang, L. Wang, J. Yang, W. Wang, A survey on mobile edge networks: convergence of computing, caching and communications. IEEE Access. 5:, 6757\u20136779 (2017). https:\/\/doi.org\/10.1109\/access.2017.2685434.","journal-title":"IEEE Access"},{"issue":"4","key":"1678_CR9","doi-asserted-by":"publisher","first-page":"2322","DOI":"10.1109\/COMST.2017.2745201","volume":"19","author":"Y. Mao","year":"2017","unstructured":"Y. Mao, C. You, J. Zhang, K. Huang, K. B. Letaief, A survey on mobile edge computing: the communication perspective. IEEE Commun. Surv. Tutor.19(4), 2322\u20132358 (2017). https:\/\/doi.org\/10.1109\/comst.2017.2745201.","journal-title":"IEEE Commun. Surv. Tutor."},{"issue":"3","key":"1678_CR10","doi-asserted-by":"publisher","first-page":"1657","DOI":"10.1109\/COMST.2017.2705720","volume":"19","author":"T. Taleb","year":"2017","unstructured":"T. Taleb, K. Samdanis, B. Mada, H. Flinck, S. Dutta, D. Sabella, On multi-access edge computing: a survey of the emerging 5G network edge cloud architecture and orchestration. IEEE Commun. Surv. Tutor.19(3), 1657\u20131681 (2017). https:\/\/doi.org\/10.1109\/comst.2017.2705720.","journal-title":"IEEE Commun. Surv. Tutor."},{"issue":"2","key":"1678_CR11","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1109\/CC.2016.7405725","volume":"13","author":"Y. Yu","year":"2016","unstructured":"Y. Yu, Mobile edge computing towards 5G: vision, recent progress, and open challenges. China Commun.13(2), 89\u201399 (2016).","journal-title":"China Commun."},{"issue":"11","key":"1678_CR12","first-page":"1","volume":"11","author":"Y. C. Hu","year":"2015","unstructured":"Y. C. Hu, M. Patel, D. Sabella, N. Sprecher, V. Young, Mobile edge computing\u2014a key technology towards 5G. ETSI White Pap.11(11), 1\u201316 (2015).","journal-title":"ETSI White Pap."},{"issue":"4","key":"1678_CR13","doi-asserted-by":"publisher","first-page":"974","DOI":"10.1109\/TMC.2018.2847350","volume":"18","author":"J. Xu","year":"2019","unstructured":"J. Xu, S. Wang, N. Zhang, F. Yang, X. Shen, Reward or penalty: aligning incentives of stakeholders in crowdsourcing. IEEE Trans. Mob. Comput.18(4), 974\u2013985 (2019). https:\/\/doi.org\/10.1109\/TMC.2018.2847350.","journal-title":"IEEE Trans. Mob. Comput."},{"key":"1678_CR14","doi-asserted-by":"publisher","unstructured":"G. Orsini, D. Bade, W. Lamersdorf, CloudAware: A Context-adaptive Middleware for Mobile Edge and Cloud Computing Applications, (2016). https:\/\/doi.org\/10.1109\/fas-w.2016.54.","DOI":"10.1109\/fas-w.2016.54"},{"key":"1678_CR15","doi-asserted-by":"publisher","unstructured":"M. Yang, Y. Wen, J. Cai, C. H. Foh, Energy Minimization via Dynamic Voltage Scaling for Real-Time Video Encoding on Mobile Devices, (2012). https:\/\/doi.org\/10.1109\/icc.2012.6364132.","DOI":"10.1109\/icc.2012.6364132"},{"key":"1678_CR16","doi-asserted-by":"publisher","unstructured":"X. Ran, H. Chen, Z. Liu, J. Chen, Delivering Deep Learning to Mobile Devices Via Offloading, (2017). https:\/\/doi.org\/10.1145\/3097895.3097903.","DOI":"10.1145\/3097895.3097903"},{"key":"1678_CR17","doi-asserted-by":"publisher","unstructured":"E. Cuervo, A. Balasubramanian, D. -k. Cho, A. Wolman, S. Saroiu, R. Chandra, P. Bahl, Maui: making smartphones last longer with code offload. (ACM), pp. 49\u201362. https:\/\/doi.org\/10.1145\/1814433.1814441.","DOI":"10.1145\/1814433.1814441"},{"key":"1678_CR18","doi-asserted-by":"publisher","unstructured":"B. -G. Chun, S. Ihm, P. Maniatis, M. Naik, A. Patti, in Proceedings of the Sixth Conference on Computer Systems. Clonecloud: elastic execution between mobile device and cloud. (ACM), pp. 301\u2013341. https:\/\/doi.org\/10.1145\/1966445.1966473.","DOI":"10.1145\/1966445.1966473"},{"key":"1678_CR19","doi-asserted-by":"publisher","unstructured":"C. Shi, K. Habak, P. Pandurangan, M. Ammar, M. Naik, E. Zegura, in Proceedings of the 15th ACM International Symposium on Mobile Ad Hoc Networking and Computing. Cosmos: computation offloading as a service for mobile devices. (ACM), pp. 287\u2013296. https:\/\/doi.org\/10.1145\/2632951.2632958.","DOI":"10.1145\/2632951.2632958"},{"issue":"4","key":"1678_CR20","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/MPRV.2009.82","volume":"8","author":"M. Satyanarayanan","year":"2009","unstructured":"M. Satyanarayanan, P. Bahl, R. Caceres, N. Davies, The case for vm-based cloudlets in mobile computing. IEEE Pervasive Comput.8(4), 14\u201323 (2009). https:\/\/doi.org\/10.1109\/mprv.2009.82.","journal-title":"IEEE Pervasive Comput."},{"key":"1678_CR21","doi-asserted-by":"publisher","unstructured":"K. Habak, M. Ammar, K. A. Harras, E. Zegura, FemtoClouds: Leveraging Mobile Devices to Provide Cloud Service at the Edge, (2015). https:\/\/doi.org\/10.1109\/cloud.2015.12.","DOI":"10.1109\/cloud.2015.12"},{"key":"1678_CR22","doi-asserted-by":"publisher","unstructured":"R. Golchay, F. Le Mouel, J. Ponge, N. Stouls, in Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering, volume 201. Spontaneous Proximity Clouds: Making Mobile Devices to Collaborate for Resource and Data Sharing, (2017), pp. 480\u2013489. https:\/\/doi.org\/10.1007\/978-3-319-59288-6_45.","DOI":"10.1007\/978-3-319-59288-6_45"}],"container-title":["EURASIP Journal on Wireless Communications and Networking"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13638-020-01678-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13638-020-01678-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13638-020-01678-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,29]],"date-time":"2021-03-29T23:10:56Z","timestamp":1617059456000},"score":1,"resource":{"primary":{"URL":"https:\/\/jwcn-eurasipjournals.springeropen.com\/articles\/10.1186\/s13638-020-01678-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,30]]},"references-count":22,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["1678"],"URL":"https:\/\/doi.org\/10.1186\/s13638-020-01678-5","relation":{},"ISSN":["1687-1499"],"issn-type":[{"value":"1687-1499","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,30]]},"assertion":[{"value":"16 January 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 March 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"69"}}