{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T04:57:44Z","timestamp":1785819464021,"version":"3.56.0"},"reference-count":229,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Commun. Surv. Tutorials"],"published-print":{"date-parts":[[2018]]},"DOI":"10.1109\/comst.2018.2844341","type":"journal-article","created":{"date-parts":[[2018,6,6]],"date-time":"2018-06-06T19:00:47Z","timestamp":1528311647000},"page":"2923-2960","source":"Crossref","is-referenced-by-count":1220,"title":["Deep Learning for IoT Big Data and Streaming Analytics: A Survey"],"prefix":"10.1109","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9947-3470","authenticated-orcid":false,"given":"Mehdi","family":"Mohammadi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0903-1204","authenticated-orcid":false,"given":"Ala","family":"Al-Fuqaha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3936-7833","authenticated-orcid":false,"given":"Sameh","family":"Sorour","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8972-8094","authenticated-orcid":false,"given":"Mohsen","family":"Guizani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref170","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2016.04.006"},{"key":"ref172","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2017.8057098"},{"key":"ref171","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2017.1600304WC"},{"key":"ref174","article-title":"An analysis of deep neural network models for practical applications","author":"canziani","year":"2016","journal-title":"arXiv preprint arXive 1605 07678v4"},{"key":"ref173","year":"2018","journal-title":"Global cell coverage maps - opensignal"},{"key":"ref176","author":"bourguignat","year":"2014","journal-title":"Interpretable vs Powerful Predictive Models Why We Need Them Both"},{"key":"ref175","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref178","doi-asserted-by":"publisher","DOI":"10.1145\/3106237.3106290"},{"key":"ref177","author":"chollet","year":"2018","journal-title":"Deep Learning with Python"},{"key":"ref168","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-016-0667-5"},{"key":"ref169","doi-asserted-by":"publisher","DOI":"10.1109\/INDIN.2017.8104767"},{"key":"ref39","article-title":"How to construct deep recurrent neural networks","author":"pascanu","year":"2013","journal-title":"arXiv preprint arXiv 1312 6026"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/5.58337"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1017\/atsip.2013.9"},{"key":"ref32","article-title":"Learning longer memory in recurrent neural networks","author":"mikolov","year":"2014","journal-title":"arXiv preprint arXiv 1412 7753"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref37","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref36","author":"chauvin","year":"1995","journal-title":"Backpropagation Theory Architectures and Applications"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1038\/323533a0"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"ref181","doi-asserted-by":"publisher","DOI":"10.1109\/LPT.2017.2755663"},{"key":"ref180","first-page":"2654","article-title":"Do deep nets really need to be deep?","author":"ba","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref185","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001163"},{"key":"ref184","first-page":"1135","article-title":"Learning both weights and connections for efficient neural network","author":"han","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref183","first-page":"598","article-title":"Optimal brain damage","volume":"2","author":"lecun","year":"1989","journal-title":"Proc NIPS"},{"key":"ref182","first-page":"2148","article-title":"Predicting parameters in deep learning","author":"denil","year":"2013","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref189","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2016.7477614"},{"key":"ref188","doi-asserted-by":"publisher","DOI":"10.1145\/2627369.2627613"},{"key":"ref187","article-title":"Binarized neural networks: Training deep neural networks with weights and activations constrained to +1 or ?1","author":"courbariaux","year":"2016","journal-title":"arXiv preprint arXiv 1602 02830v3 [cs LG]"},{"key":"ref186","first-page":"2285","article-title":"Compressing neural networks with the hashing trick","volume":"37","author":"chen","year":"2015","journal-title":"Proceedings of the 32nd Intl Conf on Machine Learning"},{"key":"ref28","article-title":"Improving neural networks by preventing co-adaptation of feature detectors","author":"hinton","year":"2012","journal-title":"arXiv preprint arXiv 1207 0580"},{"key":"ref27","first-page":"315","article-title":"Deep sparse rectifier neural networks","author":"glorot","year":"2011","journal-title":"Proc 14th Int Conf Artificial Intell"},{"key":"ref179","doi-asserted-by":"publisher","DOI":"10.5244\/C.28.6"},{"key":"ref29","first-page":"1139","article-title":"On the importance of initialization and momentum in deep learning","author":"sutskever","year":"2013","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/ACCESS.2014.2332453","article-title":"Toward scalable systems for big data analytics: A technology tutorial","volume":"2","author":"hu","year":"2014","journal-title":"IEEE Access"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1038\/nature07634"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CTS.2013.6567203"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1126\/science.1127647"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-09177-8_11"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/S0169-7439(97)00061-0"},{"key":"ref50","first-page":"3546","article-title":"Semi-supervised learning with ladder networks","author":"rasmus","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref51","author":"lecun","year":"2018","journal-title":"The MNIST Database of Handwritten Digits"},{"key":"ref154","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-51811-4_49"},{"key":"ref153","author":"steinberg","year":"2015","journal-title":"Changing the Game The Rise of Sports Analytics Forbes"},{"key":"ref156","first-page":"1","article-title":"Applying deep learning to basketball trajectories","author":"shah","year":"2016","journal-title":"arXiv preprint arXiv 1608 03793 [cs NE]"},{"key":"ref155","first-page":"1","article-title":"Classifying NBA offensive plays using neural networks","author":"wang","year":"2016","journal-title":"MIT Sloan Sports Anal Conf"},{"key":"ref150","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2016.07.144"},{"key":"ref152","doi-asserted-by":"publisher","DOI":"10.1145\/3004725.3004729"},{"key":"ref151","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2017.2699169"},{"key":"ref146","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2016.12.012"},{"key":"ref147","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2017.02.007"},{"key":"ref148","doi-asserted-by":"publisher","DOI":"10.1109\/TSM.2016.2628865"},{"key":"ref149","first-page":"8","article-title":"On accurate and reliable anomaly detection for gas turbine combustors: A deep learning approach","author":"yan","year":"2015","journal-title":"Proc Annu Conf Prognostics Health Manag Soc"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2016.2593643"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/WF-IoT.2014.6803130"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.05.120"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2006.880583"},{"key":"ref52","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref40","first-page":"190","article-title":"Training and analysing deep recurrent neural networks","author":"hermans","year":"2013","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref167","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2013.6525600"},{"key":"ref166","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2017.2772792"},{"key":"ref165","doi-asserted-by":"publisher","DOI":"10.3390\/s17091967"},{"key":"ref164","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-017-2017-7"},{"key":"ref163","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.216"},{"key":"ref162","doi-asserted-by":"publisher","DOI":"10.1109\/MCE.2016.2614422"},{"key":"ref161","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.382"},{"key":"ref160","doi-asserted-by":"publisher","DOI":"10.1109\/ITNEC.2016.7560389"},{"key":"ref4","author":"panetta","year":"2016","journal-title":"Top 10 Strategic Technology Trends for 2017"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1126\/science.aap8062"},{"key":"ref6","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-06245-7","author":"chen","year":"2014","journal-title":"Big Data Related Technologies Challenges and Future Prospects"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2018.1700298"},{"key":"ref8","first-page":"45","article-title":"Fast and interactive analytics over Hadoop data with spark","volume":"37","author":"zaharia","year":"2012","journal-title":"USENIX login"},{"key":"ref159","doi-asserted-by":"publisher","DOI":"10.1145\/2766959"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2014.2325029"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-802806-3.00008-7"},{"key":"ref157","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-017-0495-0"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/2213836.2213934"},{"key":"ref158","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.217"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.323"},{"key":"ref45","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1561\/2200000006"},{"key":"ref47","author":"metz","year":"2018","journal-title":"How an A I ‘Cat-and-Mouse Game’ Generates Believable Fake Photos"},{"key":"ref42","first-page":"37","article-title":"Autoencoders, unsupervised learning, and deep architectures","volume":"27","author":"baldi","year":"2012","journal-title":"Proc ICML Workshop Unsupervised Transf Learn"},{"key":"ref41","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"chung","year":"2014","journal-title":"arXiv preprint arXiv 1412 3555v1 [cs NE]"},{"key":"ref44","first-page":"3581","article-title":"Semi-supervised learning with deep generative models","author":"kingma","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref43","article-title":"Tutorial on variational autoencoders","author":"doersch","year":"2016","journal-title":"arXiv preprint arXiv 1606 05908v2 [stat ML]"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654889"},{"key":"ref72","author":"raschka","year":"2017","journal-title":"Machine Learning in Python"},{"key":"ref71","article-title":"Theano: New features and speed improvements","author":"bastien","year":"2012","journal-title":"arXiv preprint arXiv 1211 5590v1 [cs SC]"},{"key":"ref70","first-page":"1","article-title":"Torch7: A MATLAB-like environment for machine learning","author":"collobert","year":"2011","journal-title":"NIPS Workshop BigLearn"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/CCBD.2016.029"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2668840"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.5244\/C.29.41"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2016.7841045"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0119044"},{"key":"ref60","article-title":"Playing Atari with deep reinforcement learning","author":"mnih","year":"2013","journal-title":"arXiv preprint arXiv 1312 5602v1 [cs LG]"},{"key":"ref62","first-page":"17","article-title":"Deep learning of representations for unsupervised and transfer learning","volume":"27","author":"bengio","year":"2012","journal-title":"Proc ICML Workshop Unsupervised Transfer Learn"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2712560"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2014.6854517"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1145\/2502081.2502112"},{"key":"ref65","first-page":"228","article-title":"Online algorithms for sum-product networks with continuous variables","author":"jaini","year":"2016","journal-title":"Proc 8th Int Conf Probabilistic Graph Models"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46227-1_48"},{"key":"ref67","article-title":"Comparative study of deep learning software frameworks","author":"bahrampour","year":"2016","journal-title":"arXiv preprint arXiv 1511 06435v3 [cs LG]"},{"key":"ref68","author":"candel","year":"2015","journal-title":"Deep learning with h2o"},{"key":"ref69","article-title":"Tensorflow: Large-scale machine learning on heterogeneous distributed systems","author":"abadi","year":"2016","journal-title":"arXiv preprint arXiv 1603 04467"},{"key":"ref197","doi-asserted-by":"publisher","DOI":"10.1109\/ASPDAC.2016.7428029"},{"key":"ref198","doi-asserted-by":"publisher","DOI":"10.1038\/nphys551"},{"key":"ref199","doi-asserted-by":"publisher","DOI":"10.1109\/MSPEC.2017.7880447"},{"key":"ref193","doi-asserted-by":"publisher","DOI":"10.1109\/IPSN.2016.7460664"},{"key":"ref194","first-page":"250","article-title":"14.7 A $288~\\mu $ w programmable deep-learning processor with 270KB on-chip weight storage using non-uniform memory hierarchy for mobile intelligence","author":"bang","year":"2017","journal-title":"Proc IEEE Int Solid-State Circuits Conf (ISSCC)"},{"key":"ref195","author":"netzer","year":"2018","journal-title":"The Street View House Numbers (SVHN) Dataset"},{"key":"ref196","article-title":"ApproxDBN: Approximate computing for discriminative deep belief networks","author":"xu","year":"2017","journal-title":"arXiv preprint arXiv 1704 03993v3 [cs NE]"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2016.2636750"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.02.055"},{"key":"ref190","doi-asserted-by":"publisher","DOI":"10.1145\/2627369.2627625"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.04.011"},{"key":"ref191","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2016.2616357"},{"key":"ref92","first-page":"1666","article-title":"DeepFi: Deep learning for indoor fingerprinting using channel state information","author":"wang","year":"2015","journal-title":"Proc IEEE Wireless Commun Netw Conf (WCNC)"},{"key":"ref192","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1145\/2644865.2541967","article-title":"DianNao: A small-footprint high-throughput accelerator for ubiquitous machine-learning","volume":"49","author":"chen","year":"2014","journal-title":"ACM SIGPLAN Notices"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1109\/ISSCC.2017.7870352"},{"key":"ref90","author":"larry","year":"2017","journal-title":"Voice Control Everywhere Low-Power Special-Purpose Chip Could Make Speech Recognition Ubiquitous in Electronics"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.214"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1109\/ISM.2016.0050"},{"key":"ref96","first-page":"76","article-title":"Indoor positioning solely based on user’s sight","author":"becker","year":"2017","journal-title":"Proc Int Conf Inf Sci Appl"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-017-0483-7"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2017.2662008"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1145\/2971648.2971731"},{"key":"ref84","article-title":"Application of deep convolutional neural networks for detecting extreme weather in climate datasets","author":"liu","year":"2016","journal-title":"arXiv preprint arXiv 1605 01156 [CS cv]"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/3289801"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1145\/2820975.2820980"},{"key":"ref89","first-page":"1","article-title":"The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east","volume":"2007","author":"gantz","year":"2012","journal-title":"IDC IView IDC Analyze the Future"},{"key":"ref85","first-page":"1","article-title":"Chainer: A next-generation open source framework for deep learning","author":"tokui","year":"2015","journal-title":"Proc Workshop Mach Learn Syst (LearningSys) 29th Annu Conf Neural Inf Process Syst (NIPS)"},{"key":"ref86","article-title":"A deep-learning approach for operation of an automated realtime flare forecast","author":"hada-muranushi","year":"2016","journal-title":"arXiv preprint arXiv 1606 01587v1 [astro-ph SR]"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1145\/2991561.2991575"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1145\/2996890.2996907"},{"key":"ref200","doi-asserted-by":"publisher","DOI":"10.1109\/VLSIC.2016.7573525"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2016.2561962"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.3390\/s16010115"},{"key":"ref209","article-title":"Lessons learned from deploying deep learning at scale","author":"daniel","year":"0"},{"key":"ref203","first-page":"222","article-title":"Cognitive computation and communication: A complement solution to cloud for IoT","author":"nguyen-thanh","year":"2016","journal-title":"Proc Int Conf Adv Technol Commun (ATC)"},{"key":"ref204","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2017.2679740"},{"key":"ref201","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"howard","year":"2017","journal-title":"arXiv preprint arXiv 1704 04861v1 [cs CV]"},{"key":"ref202","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2016.2633287"},{"key":"ref207","first-page":"571","article-title":"Project ADAM: Building an efficient and scalable deep learning training system","volume":"14","author":"chilimbi","year":"2014","journal-title":"Proc USENIX Symp Oper Syst Design Implementation"},{"key":"ref208","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080246"},{"key":"ref205","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2015.7263375"},{"key":"ref206","first-page":"1337","article-title":"Deep learning with COTS HPC systems","volume":"28","author":"coates","year":"2013","journal-title":"Proc 30th Int Conf Mach Learn"},{"key":"ref211","first-page":"115","article-title":"Big data analytics platforms for real-time applications in IoT","author":"simmhan","year":"2016","journal-title":"Big Data Analytics"},{"key":"ref210","author":"hemsoth","year":"2015","journal-title":"GPU Platforms Set to Lengthen Deep Learning Reach"},{"key":"ref212","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-62404-4_16"},{"key":"ref213","doi-asserted-by":"publisher","DOI":"10.1109\/SEC.2016.38"},{"key":"ref214","year":"2017","journal-title":"List of Datasets for Machine Learning Research"},{"key":"ref215","first-page":"372","article-title":"The limitations of deep learning in adversarial settings","author":"mcdaniel","year":"2016","journal-title":"Proc IEEE Eur Symp Secur Privacy (EuroS P)"},{"key":"ref216","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-014-0007-7"},{"key":"ref217","first-page":"21","article-title":"Big data, analytics and the path from insights to value","volume":"52","author":"lavalle","year":"2011","journal-title":"Mit Sloan Manag Rev"},{"key":"ref218","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298640"},{"key":"ref219","doi-asserted-by":"publisher","DOI":"10.1109\/CIEL.2014.7015739"},{"key":"ref220","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2016.7474340"},{"key":"ref222","year":"2017","journal-title":"Cyber-Physical Systems (CPS)—Program Solicitation (NSF 17-529)"},{"key":"ref221","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2724938"},{"key":"ref229","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2016.7759589"},{"key":"ref228","article-title":"Deep-learning in mobile robotics-from perception to control systems: A survey on why and why not","author":"tai","year":"2016","journal-title":"arXiv preprint arXiv 1612 07139v3 [cs RO]"},{"key":"ref227","first-page":"241","article-title":"3D integral imaging based augmented reality with deep learning implemented by faster R-CNN","author":"sutanto","year":"2017","journal-title":"Proc Int Conf Mobile Wireless Technol"},{"key":"ref226","doi-asserted-by":"publisher","DOI":"10.1109\/SITIS.2016.17"},{"key":"ref225","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2016.7759428"},{"key":"ref224","doi-asserted-by":"publisher","DOI":"10.1109\/IRC.2017.77"},{"key":"ref223","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2017.2727878"},{"key":"ref127","article-title":"Deep-learning first: Drive.ai’s path to autonomous driving","author":"ackerman","year":"2017","journal-title":"IEEE Spectr"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0173317"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2012.02.023"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1109\/SmartCity.2015.63"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-50478-0_19"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-39601-9_4"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1600425CM"},{"key":"ref133","first-page":"1","article-title":"Learning to diagnose with LSTM recurrent neural networks","author":"lipton","year":"2016","journal-title":"Proc ICLR"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2016.2636665"},{"key":"ref131","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2655486"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1109\/ICDSP.2014.6900806"},{"key":"ref136","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2015.7325900"},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2017.2681128"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging2010006"},{"key":"ref137","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2017.2657778"},{"key":"ref139","doi-asserted-by":"publisher","DOI":"10.3390\/s16081222"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2013.09.004"},{"key":"ref141","doi-asserted-by":"publisher","DOI":"10.1186\/s40561-015-0009-8"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783273"},{"key":"ref143","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2017.2700227"},{"key":"ref2","volume":"180","author":"manyika","year":"2013","journal-title":"Disruptive Technologies Advances That Will Transform Life Business and the Global Economy"},{"key":"ref144","first-page":"505","article-title":"Deep knowledge tracing","author":"piech","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2015.2444095"},{"key":"ref145","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2014.95"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1145\/2740070.2631434"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0155781"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2017.2703842"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2016.2557846"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1007\/s12193-015-0195-2"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.494"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-016-0957-7"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1145\/2994551.2994569"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref112","author":"hazen","year":"2016","journal-title":"Microsoft and Liebherr Collaborating on New Generation of Smart Refrigerators"},{"key":"ref110","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1145\/2810103.2813687","article-title":"Privacy-preserving deep learning","author":"shokri","year":"2015","journal-title":"Proc 22nd ACM SIGSAC Conf Comput Commun Security"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2013.103013.00206"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2013.042313.00197"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2014.2320099"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2017.2707140"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1186\/s13634-016-0355-x"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2697839"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.1007\/s11356-016-7812-9"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.14778\/2809974.2809979"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2016.7498348"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1111\/dpr.12142"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/2481244.2481246"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.10.055"},{"key":"ref114","author":"yoshida","year":"2016","journal-title":"The Internet of Things That Matter Integrating Smart Cities With Smart Agriculture Digitalist Magazine"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1109\/MIE.2016.2615575"},{"key":"ref116","first-page":"2618","article-title":"Deeptransport: Prediction and simulation of human mobility and transportation mode at a citywide level","author":"song","year":"2016","journal-title":"Proc IJCAI"},{"key":"ref115","year":"2016","journal-title":"Toshiba and Dell Technologies’ Deep Learning Testbed for IoT Is First Approved by Industrial Internet Consortium"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.1109\/WF-IoT.2016.7845408"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1109\/SMC.2016.7844820"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1016\/j.segan.2016.02.005"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1109\/SMC.2016.7844673"}],"container-title":["IEEE Communications Surveys & Tutorials"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9739\/8540503\/08373692.pdf?arnumber=8373692","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,3]],"date-time":"2023-09-03T01:36:25Z","timestamp":1693704985000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8373692\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":229,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/comst.2018.2844341","relation":{},"ISSN":["1553-877X","2373-745X"],"issn-type":[{"value":"1553-877X","type":"electronic"},{"value":"2373-745X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]}}}