{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T03:10:09Z","timestamp":1751685009958,"version":"3.41.0"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319942674"},{"type":"electronic","value":"9783319942681"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-94268-1_59","type":"book-chapter","created":{"date-parts":[[2018,6,12]],"date-time":"2018-06-12T12:49:21Z","timestamp":1528807761000},"page":"720-732","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Retrieving the Relative Kernel Dataset from Big Sensory Data for Continuous Query"],"prefix":"10.1007","author":[{"given":"Tongxin","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinbao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyao","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingshu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianzhong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,6,13]]},"reference":[{"key":"59_CR1","unstructured":"Says, G.: 6.4 billion connected things will be in use in 2016, up 30 percent from 2015. Gartner Inc. (2015)"},{"key":"59_CR2","unstructured":"Tillman, K.: How many internet connections are in the world? Right. now. CISCO (2013). http:\/\/blogs.cisco.com\/news\/cisco-connections-counter"},{"issue":"1","key":"59_CR3","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.aeue.2011.05.002","volume":"66","author":"J Yu","year":"2012","unstructured":"Yu, J., Qi, Y., Wang, G., Gu, X.: A cluster-based routing protocol for wireless sensor networks with nonuniform node distribution. AEUE Int. J. Electron. Commun. 66(1), 54\u201361 (2012)","journal-title":"AEUE Int. J. Electron. Commun."},{"key":"59_CR4","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.jpdc.2011.07.002","volume":"72","author":"J Yu","year":"2012","unstructured":"Yu, J., Wang, N., Wang, G.: Constructing minimum extended weakly-connected dominating sets for clustering in ad hoc networks. J. Parallel Distrib. Comput. 72, 35\u201347 (2012)","journal-title":"J. Parallel Distrib. Comput."},{"key":"59_CR5","doi-asserted-by":"crossref","unstructured":"Yu, J., Ning, X., Sun, Y., Wang, S., Wang, Y.: Constructing a self-stabilizing CDS with bounded diameter in wireless networks under SINR. In: 2017 Proceedings of IEEE INFOCOM, pp. 1\u20139 (2017)","DOI":"10.1109\/INFOCOM.2017.8057225"},{"key":"59_CR6","doi-asserted-by":"crossref","unstructured":"Shi, T., Cheng, S., Cai, Z., Li, J.: Adaptive connected dominating set discovering algorithm in energy-harvest sensor networks. In: 2016 Proceedings of IEEE INFOCOM, pp. 1\u20139 (2016)","DOI":"10.1109\/INFOCOM.2016.7524504"},{"issue":"2","key":"59_CR7","doi-asserted-by":"publisher","first-page":"11:1","DOI":"10.1145\/3027488","volume":"13","author":"J Li","year":"2017","unstructured":"Li, J., Cheng, S., Cai, Z., Yu, J., Wang, C., Li, Y.: Approximate holistic aggregation in wireless sensor networks. TOSN 13(2), 11:1\u201311:24 (2017)","journal-title":"TOSN"},{"key":"59_CR8","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1016\/j.tcs.2015.07.056","volume":"607","author":"Z He","year":"2015","unstructured":"He, Z., Cai, Z., Cheng, S., Wang, X.: Approximate aggregation for tracking quantiles and range countings in wireless sensor networks. Theor. Comput. Sci. 607, 381\u2013390 (2015)","journal-title":"Theor. Comput. Sci."},{"key":"59_CR9","doi-asserted-by":"crossref","unstructured":"Chen, Q., Gao, H., Cai, Z., Cheng, L., Li, J.: Energy-collision aware data aggregation scheduling for energy harvesting sensor networks. In: 2018 Proceedings of IEEE INFOCOM (2018)","DOI":"10.1109\/INFOCOM.2018.8486366"},{"issue":"11","key":"59_CR10","doi-asserted-by":"publisher","first-page":"5198","DOI":"10.1109\/TVT.2014.2375330","volume":"64","author":"S Cheng","year":"2015","unstructured":"Cheng, S., Cai, Z., Li, J.: Curve query processing in wireless sensor networks. IEEE Trans. Veh. Technol. 64(11), 5198\u20135209 (2015)","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"7","key":"59_CR11","doi-asserted-by":"publisher","first-page":"1787","DOI":"10.1109\/TMC.2016.2613529","volume":"16","author":"X Zheng","year":"2017","unstructured":"Zheng, X., Cai, Z., Li, J., Gao, H.: A study on application-aware scheduling in wireless networks. IEEE Trans. Mob. Comput. 16(7), 1787\u20131801 (2017)","journal-title":"IEEE Trans. Mob. Comput."},{"key":"59_CR12","unstructured":"Cheng, S., Li, J., Ren, Q., Yu, L.: Bernoulli sampling based ($$\\varepsilon $$\u03b5, $$\\delta $$\u03b4)-approximate aggregation in large-scale sensor networks. In: Proceedings of the 29th Conference on Information Communications, pp. 1181\u20131189. IEEE Press (2010)"},{"key":"59_CR13","doi-asserted-by":"crossref","unstructured":"Huang, Z., Wang, L., Yi, K., Liu, Y.: Sampling based algorithms for quantile computation in sensor networks. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of data, pp. 745\u2013756. ACM (2011)","DOI":"10.1145\/1989323.1989401"},{"issue":"2","key":"59_CR14","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1109\/TWC.2012.122212.121032","volume":"12","author":"H Zheng","year":"2013","unstructured":"Zheng, H., Xiao, S., Wang, X., Tian, X., Guizani, M.: Capacity and delay analysis for data gathering with compressive sensing in wireless sensor networks. IEEE Trans. Wirel. Commun. 12(2), 917\u2013927 (2013)","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"59_CR15","doi-asserted-by":"crossref","unstructured":"Wang, H., Zhu, Y., Zhang, Q.: Compressive sensing based monitoring with vehicular networks. In: 2013 Proceedings of IEEE INFOCOM, pp. 2823\u20132831. IEEE (2013)","DOI":"10.1109\/INFCOM.2013.6567092"},{"key":"59_CR16","doi-asserted-by":"crossref","unstructured":"Cheng, S., Cai, Z., Li, J., Fang, X.: Drawing dominant dataset from big sensory data in wireless sensor networks. In: 2015 Proceedings of IEEE INFOCOM, pp. 531\u2013539. IEEE (2015)","DOI":"10.1109\/INFOCOM.2015.7218420"},{"issue":"4","key":"59_CR17","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1109\/TKDE.2016.2645212","volume":"29","author":"S Cheng","year":"2017","unstructured":"Cheng, S., Cai, Z., Li, J., Gao, H.: Extracting kernel dataset from big sensory data in wireless sensor networks. IEEE Trans. Knowl. Data Eng. 29(4), 813\u2013827 (2017)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"59_CR18","unstructured":"Zong-tian, T.L.L.: Least-squares method piecewise linear fitting. Comput. Sci., S1 (2012)"},{"key":"59_CR19","doi-asserted-by":"crossref","unstructured":"Masiero, R., Quer, G., Munaretto, D., Rossi, M., Widmer, J., Zorzi, M.: Data acquisition through joint compressive sensing and principal component analysis. In: IEEE GLOBECOM 2009, pp. 1\u20136. IEEE (2009)","DOI":"10.1109\/GLOCOM.2009.5425458"},{"key":"59_CR20","doi-asserted-by":"crossref","unstructured":"Macua, S.V., Belanovic, P., Zazo, S.: Consensus-based distributed principal component analysis in wireless sensor networks. In: 2010 IEEE Eleventh International Workshop on SPAWC, pp. 1\u20135. IEEE (2010)","DOI":"10.1109\/SPAWC.2010.5671089"},{"key":"59_CR21","doi-asserted-by":"crossref","unstructured":"Rooshenas, A., Rabiee, H.R., Movaghar, A., Naderi, M.Y.: Reducing the data transmission in wireless sensor networks using the principal component analysis. In: 2010 Sixth International Conference on ISSNIP, pp. 133\u2013138. IEEE (2010)","DOI":"10.1109\/ISSNIP.2010.5706781"}],"container-title":["Lecture Notes in Computer Science","Wireless Algorithms, Systems, and Applications"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-94268-1_59","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T02:52:30Z","timestamp":1751683950000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-94268-1_59"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319942674","9783319942681"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-94268-1_59","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]}}}