{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T02:51:16Z","timestamp":1742957476554,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030134525"},{"type":"electronic","value":"9783030134532"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-13453-2_5","type":"book-chapter","created":{"date-parts":[[2019,2,15]],"date-time":"2019-02-15T06:33:53Z","timestamp":1550212433000},"page":"53-64","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Smart Cities with Deep Edges"],"prefix":"10.1007","author":[{"given":"Gary","family":"White","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siobh\u00e1n","family":"Clarke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,2,16]]},"reference":[{"key":"5_CR1","unstructured":"The world\u2019s cities in 2016. http:\/\/www.un.org\/en\/development\/desa\/population\/publications\/pdf\/urbanization\/the_worlds_cities_in_2016_data_booklet.pdf. Accessed 2016"},{"key":"5_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1007\/978-3-642-20898-0_31","volume-title":"The Future Internet","author":"H Schaffers","year":"2011","unstructured":"Schaffers, H., Komninos, N., Pallot, M., Trousse, B., Nilsson, M., Oliveira, A.: Smart cities and the future internet: towards cooperation frameworks for open innovation. In: Domingue, J., et al. (eds.) FIA 2011. LNCS, vol. 6656, pp. 431\u2013446. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-20898-0_31"},{"key":"5_CR3","unstructured":"Bauer, H., Patel, M., Veira, J.: The internet of things: sizing up the opportunity. McKinsey (2014)"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"White, G., Nallur, V., Clarke, S.: Quality of service approaches in IoT: a systematic mapping. J. Syst. Softw. 132, 186\u2013203 (2017). http:\/\/www.sciencedirect.com\/science\/article\/pii\/S016412121730105X","DOI":"10.1016\/j.jss.2017.05.125"},{"issue":"4","key":"5_CR5","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/MPRV.2009.82","volume":"8","author":"M Satyanarayanan","year":"2009","unstructured":"Satyanarayanan, M., Bahl, P., Caceres, R., Davies, N.: The case for VM-based cloudlets in mobile computing. IEEE Pervasive Comput. 8(4), 14\u201323 (2009)","journal-title":"IEEE Pervasive Comput."},{"key":"5_CR6","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/978-3-319-05029-4_7","volume-title":"Big Data and Internet of Things: A Roadmap for Smart Environments","author":"F Bonomi","year":"2014","unstructured":"Bonomi, F., Milito, R., Natarajan, P., Zhu, J.: Fog computing: a platform for internet of things and analytics. In: Bessis, N., Dobre, C. (eds.) Big Data and Internet of Things: A Roadmap for Smart Environments. SCI, vol. 546, pp. 169\u2013186. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-05029-4_7"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85\u2013117 (2015). http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0893608014002135","DOI":"10.1016\/j.neunet.2014.09.003"},{"issue":"3","key":"5_CR8","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1109\/JSYST.2016.2550538","volume":"10","author":"J Wu","year":"2016","unstructured":"Wu, J., Guo, S., Li, J., Zeng, D.: Big data meet green challenges: greening big data. IEEE Syst. J. 10(3), 873\u2013887 (2016)","journal-title":"IEEE Syst. J."},{"issue":"7553","key":"5_CR9","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)","journal-title":"Nature"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"White, G., Palade, A., Clarke, S.: Forecasting QoS attributes using LSTM networks. In: 2018 International Joint Conference on Neural Networks (IJCNN) (2018)","DOI":"10.1109\/IJCNN.2018.8489052"},{"issue":"2","key":"5_CR11","doi-asserted-by":"publisher","first-page":"1275","DOI":"10.1109\/JIOT.2018.2805263","volume":"5","author":"G Premsankar","year":"2018","unstructured":"Premsankar, G., Francesco, M.D., Taleb, T.: Edge computing for the internet of things: a case study. IEEE Internet Things J. 5(2), 1275\u20131284 (2018)","journal-title":"IEEE Internet Things J."},{"key":"5_CR12","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.tranpol.2018.03.004","volume":"66","author":"TAS Nielsen","year":"2018","unstructured":"Nielsen, T.A.S., Haustein, S.: On sceptics and enthusiasts: what are the expectations towards self-driving cars? Transp. Policy 66, 49\u201355 (2018)","journal-title":"Transp. Policy"},{"issue":"1","key":"5_CR13","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MC.2017.9","volume":"50","author":"M Satyanarayanan","year":"2017","unstructured":"Satyanarayanan, M.: The emergence of edge computing. Computer 50(1), 30\u201339 (2017)","journal-title":"Computer"},{"key":"5_CR14","unstructured":"Bojarski, M., et al.: End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316 (2016)"},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Goswami, G., Bhardwaj, R., Singh, R., Vatsa, M.: MDLFace: memorability augmented deep learning for video face recognition. In: 2014 IEEE International Joint Conference on Biometrics (IJCB), pp. 1\u20137. IEEE (2014)","DOI":"10.1109\/BTAS.2014.6996299"},{"key":"5_CR16","doi-asserted-by":"publisher","unstructured":"Simoens, P., Xiao, Y., Pillai, P., Chen, Z., Ha, K., Satyanarayanan, M.: Scalable crowd-sourcing of video from mobile devices. In: Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys 2013, pp. 139\u2013152. ACM, New York (2013). https:\/\/doi.org\/10.1145\/2462456.2464440","DOI":"10.1145\/2462456.2464440"},{"key":"5_CR17","unstructured":"Gibbs, S.: Typo blamed for Amazon\u2019s internet-crippling outage. https:\/\/www.theguardian.com\/technology\/2017\/mar\/03\/typo-blamed-amazon-web-services-internet-outage"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Davies, N., Taft, N., Satyanarayanan, M., Clinch, S., Amos, B.: Privacy mediators: helping IoT cross the chasm. In: HotMobile 2016, pp. 39\u201344. ACM, New York (2016)","DOI":"10.1145\/2873587.2873600"},{"key":"5_CR19","doi-asserted-by":"crossref","unstructured":"Sun, C., Shrivastava, A., Singh, S., Gupta, A.: Revisiting unreasonable effectiveness of data in deep learning era. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 843\u2013852. IEEE (2017)","DOI":"10.1109\/ICCV.2017.97"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"White, G., Palade, A., Cabrera, C., Clarke, S.: IoTPredict: collaborative QoS prediction in IoT. In: 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom) (PerCom 2018), Athens, Greece, March 2018","DOI":"10.1109\/PERCOM.2018.8444598"},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"White, G., Palade, A., Cabrera, C., Clarke, S.: Quantitative evaluation of QoS prediction in IoT. In: 2017 47th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), pp. 61\u201366, June 2017","DOI":"10.1109\/DSN-W.2017.26"},{"key":"5_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/978-3-319-91764-1_12","volume-title":"Service-Oriented Computing \u2013 ICSOC 2017 Workshops","author":"G White","year":"2018","unstructured":"White, G., Palade, A., Clarke, S.: QoS prediction for reliable service composition in IoT. In: Braubach, L., et al. (eds.) ICSOC 2017. LNCS, vol. 10797, pp. 149\u2013160. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-91764-1_12"},{"issue":"2","key":"5_CR23","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1049\/iet-its.2016.0208","volume":"11","author":"Z Zhao","year":"2017","unstructured":"Zhao, Z., Chen, W., Wu, X., Chen, P.C.Y., Liu, J.: LSTM network: a deep learning approach for short-term traffic forecast. IET Intell. Transp. Syst. 11(2), 68\u201375 (2017)","journal-title":"IET Intell. Transp. Syst."},{"key":"5_CR24","unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D., Wong, W., Woo, W.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. CoRR, vol. abs\/1506.04214 (2015). http:\/\/arxiv.org\/abs\/1506.04214"},{"key":"5_CR25","doi-asserted-by":"crossref","unstructured":"Fiore, U., Palmieri, F., Castiglione, A., Santis, A.D.: Network anomaly detection with the restricted Boltzmann machine. Neurocomputing, 122, 13\u201323 (2013). Advances in Cognitive and Ubiquitous Computing. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0925231213005547","DOI":"10.1016\/j.neucom.2012.11.050"},{"key":"5_CR26","unstructured":"Konecn\u00fd, J., McMahan, H.B., Yu, F.X., Richt\u00e1rik, P., Suresh, A.T., Bacon, D.: Federated learning: strategies for improving communication efficiency. CoRR, vol. abs\/1610.05492 (2016). http:\/\/arxiv.org\/abs\/1610.05492"},{"key":"5_CR27","unstructured":"Foerster, J., Assael, I.A., de Freitas, N., Whiteson, S.: Learning to communicate with deep multi-agent reinforcement learning. In: Advances in Neural Information Processing Systems, pp. 2137\u20132145 (2016)"},{"key":"5_CR28","doi-asserted-by":"crossref","unstructured":"Li, M., Andersen, D.G., Smola, A.J., Yu, K.: Communication efficient distributed machine learning with the parameter server. In: Advances in Neural Information Processing Systems, pp. 19\u201327 (2014)","DOI":"10.1145\/2640087.2644155"},{"issue":"6","key":"5_CR29","doi-asserted-by":"publisher","first-page":"1256","DOI":"10.1080\/00207721.2018.1442886","volume":"49","author":"P Liu","year":"2018","unstructured":"Liu, P., Li, H., Dai, X., Han, Q.: Distributed primal-dual optimisation method with uncoordinated time-varying step-sizes. Int. J. Syst. Sci. 49(6), 1256\u20131272 (2018)","journal-title":"Int. J. Syst. Sci."},{"key":"5_CR30","unstructured":"The MNIST database of handwritten digits. http:\/\/yann.lecun.com\/exdb\/mnist\/"}],"container-title":["Lecture Notes in Computer Science","ECML PKDD 2018 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-13453-2_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T01:02:12Z","timestamp":1707958932000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-13453-2_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030134525","9783030134532"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-13453-2_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"16 February 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dublin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ireland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ecmlpkdd2018.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"535","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"131","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}