{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T00:08:27Z","timestamp":1704931707151},"reference-count":25,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Commun."],"published-print":{"date-parts":[[2016]]},"DOI":"10.1587\/transcom.2015itp0009","type":"journal-article","created":{"date-parts":[[2016,1,31]],"date-time":"2016-01-31T22:12:29Z","timestamp":1454278349000},"page":"307-314","source":"Crossref","is-referenced-by-count":1,"title":["&lt;i&gt;k&lt;\/i&gt;-Degree Layer-Wise Network for Geo-Distributed Computing between Cloud and IoT"],"prefix":"10.23919","volume":"E99.B","author":[{"given":"Yiqiang","family":"SHENG","sequence":"first","affiliation":[{"name":"National Network New Media Engineering Research Center, Chinese Academy of Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinlin","family":"WANG","sequence":"additional","affiliation":[{"name":"National Network New Media Engineering Research Center, Chinese Academy of Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haojiang","family":"DENG","sequence":"additional","affiliation":[{"name":"National Network New Media Engineering Research Center, Chinese Academy of Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaopeng","family":"LI","sequence":"additional","affiliation":[{"name":"National Network New Media Engineering Research Center, Chinese Academy of Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"1","unstructured":"[1] S. Priyankara, K. Kinoshita, H. Tode, and K. Murakami, \u201cA generalized spatial boundary analysis method for clustering\/multi-hop hybrid routing in wireless sensor networks,\u201d Proc. 2011 IEEE GLOBECOM Workshops (GC Wkshps), pp.281-286, 2011."},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] M. Amarlingam, I. Adithyan, P. Rajalakshmi, Y. Nishimura, M. Yoshida, and K. Yoshihara, \u201cDeployment adviser tool for wireless sensor networks,\u201d Proc. 2014 IEEE World Forum on Internet of Things (WF-IoT), pp.452-457, 2014.","DOI":"10.1109\/WF-IoT.2014.6803209"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] G.E. Hinton and R.R. Salakhutdinov, \u201cReducing the dimensionality of data with neural networks,\u201d Science, vol.313, no.5786, pp.504-507, 2006.","DOI":"10.1126\/science.1127647"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, \u201cGreedy layer-wise training of deep networks,\u201d Advances in Neural Information Processing Systems, vol.19, p.153, 2007.","DOI":"10.7551\/mitpress\/7503.003.0024"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] Y. LeCun, K. Kavukcuoglu, and C. Farabet, \u201cConvolutional networks and applications in vision,\u201d Proc. 2010 IEEE International Symposium on Circuits and Systems, pp.253-256, 2010.","DOI":"10.1109\/ISCAS.2010.5537907"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] Z. Xiao, W. Song, and Q. Chen, \u201cDynamic resource allocation using virtual machines for cloud computing environment,\u201d IEEE Trans. Parallel Distrib. Syst., vol.24, no.6, pp.1107-1117, June 2013.","DOI":"10.1109\/TPDS.2012.283"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] Y. Yang, Y. Zhou, L. Liang, D. He, and Z. Sun, \u201cA sevice-oriented broker for bulk data transfer in cloud computing,\u201d Proc. 2010 Ninth International Conference on Grid and Cloud Computing, pp.264-269, 2010.","DOI":"10.1109\/GCC.2010.60"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] K. Yang and X. Jia, \u201cAn efficient and secure dynamic auditing protocol for data storage in cloud computing,\u201d IEEE Trans. Parallel Distrib. Syst., vol.24, no.9, pp.1717-1726, Sept. 2013.","DOI":"10.1109\/TPDS.2012.278"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] Y. Low, D. Bickson, J. Gonzalez, C. Guestrin, A. Kyrola, and J.M. Hellerstein, \u201cDistributed graphlab,\u201d Proc. VLDB Endowment, vol.5, no.8, pp.716-727, 2012.","DOI":"10.14778\/2212351.2212354"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] B. Ottenw\u00e4lder, B. Koldehofe, K. Rothermel, and U. Ramachandran, \u201cMigCEP: Operator migration for mobility driven distributed complex event processing,\u201d Proc. 7th ACM International Conference on Distributed Event-Based Systems, DEBS&apos;13, pp.183-194, 2013.","DOI":"10.1145\/2488222.2488265"},{"key":"11","unstructured":"[11] J. Wang, J. You, H. Deng, Z. Liu, and G. Chen, \u201cA system and method of on-site service provision,\u201d Patent Cooperation Treaty (PCT), CN2014\/081300, 201410083167.9, 2014."},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] I. Stojmenovic and S. Wen, \u201cThe fog computing paradigm: Scenarios and security issues,\u201d Proc. 2014 Federated Conference on Computer Science and Information Systems, pp.1-8, 2014.","DOI":"10.15439\/2014F503"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] S.J. Stolfo, M.B. Salem, and A.D. Keromytis, \u201cFog computing: Mitigating insider data theft attacks in the cloud,\u201d Proc. 2012 IEEE Symposium on Security and Privacy Workshops, pp.125-128, 2012.","DOI":"10.1109\/SPW.2012.19"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] I.J. Lee, \u201cBig data processing framework of road traffic collision using distributed CEP,\u201d The 16th Asia-Pacific Network Operations and Management Symposium, pp.1-4, 2014.","DOI":"10.1109\/APNOMS.2014.6996577"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] R. Tudoran, A. Costan, R. Wang, L. Bouge, and G. Antoniu, \u201cBridging data in the clouds: An environment-aware system for geographically distributed data transfers,\u201d Proc. 2014 14th IEEE\/ACM International Symposium on Cluster, Cloud and Grid Computing, pp.92-101, 2014.","DOI":"10.1109\/CCGrid.2014.86"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] P. Bellavista, A. Corradi, A. Reale, and N. Ticca, \u201cPriority-based resource scheduling in distributed stream processing systems for big data applications,\u201d 2014 IEEE\/ACM 7th International Conference on Utility and Cloud Computing, pp.363-370, 2014.","DOI":"10.1109\/UCC.2014.46"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] R.Y. Shtykh and T. Suzuki, \u201cDistributed data stream processing with onix,\u201d 2014 IEEE Fourth International Conference on Big Data and Cloud Computing, pp.267-268, 2014.","DOI":"10.1109\/BDCloud.2014.54"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] K. Zhang and X.-W. Chen, \u201cLarge-scale deep belief nets with mapreduce,\u201d IEEE Access, vol.2, pp.395-403, 2014.","DOI":"10.1109\/ACCESS.2014.2319813"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] S.J. Rennie, P. Fousek, and P.L. Dognin, \u201cFactorial hidden restricted Boltzmann machines for noise robust speech recognition,\u201d 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.4297-4300, 2012.","DOI":"10.1109\/ICASSP.2012.6288869"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] X.-C. Yin, C. Yang, W.-Y. Pei, and H.-W. Hao, \u201cShallow classification or deep learning: An experimental study,\u201d Proc. 2014 22nd International Conference on Pattern Recognition, pp.1904-1909, 2014.","DOI":"10.1109\/ICPR.2014.333"},{"key":"21","unstructured":"[21] D. Li, G. Hinton, and B. Kingsbury, \u201cNew types of deep neural network learning for speech recognition and related applications: An overview,\u201d Proc. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.8599-8603, 2013."},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] P.P. Roy, Y. Chherawala, and M. Cheriet, \u201cDeep-belief-network based rescoring approach for handwritten word recognition,\u201d Proc. 2014 14th International Conference on Frontiers in Handwriting Recognition, pp.506-511, 2014.","DOI":"10.1109\/ICFHR.2014.91"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] G. Mesnil, Y. Dauphin, K. Yao, Y. Bengio, L. Deng, D. Hakkani-Tur, X. He, L. Heck, G. Tur, D. Yu, and G. Zweig, \u201cUsing recurrent neural networks for slot filling in spoken language understanding,\u201d IEEE\/ACM Trans. Audio Speech Lang. Process., vol.23, no.3, pp.530-539, 2015.","DOI":"10.1109\/TASLP.2014.2383614"},{"key":"24","unstructured":"[24] Python: https:\/\/www.python.org\/"},{"key":"25","unstructured":"[25] Theano: http:\/\/deeplearning.net\/software\/theano\/"}],"container-title":["IEICE Transactions on Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transcom\/E99.B\/2\/E99.B_2015ITP0009\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T14:59:03Z","timestamp":1704898743000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transcom\/E99.B\/2\/E99.B_2015ITP0009\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"references-count":25,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2016]]}},"URL":"https:\/\/doi.org\/10.1587\/transcom.2015itp0009","relation":{},"ISSN":["0916-8516","1745-1345"],"issn-type":[{"value":"0916-8516","type":"print"},{"value":"1745-1345","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016]]}}}