{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:25:30Z","timestamp":1742919930833,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030192228"},{"type":"electronic","value":"9783030192235"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","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":[[2019]]},"DOI":"10.1007\/978-3-030-19223-5_6","type":"book-chapter","created":{"date-parts":[[2019,5,16]],"date-time":"2019-05-16T23:22:43Z","timestamp":1558048963000},"page":"74-88","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Method Based on Dispersion Analysis for Data Reduction in WSN"],"prefix":"10.1007","author":[{"given":"Samuel","family":"Oliveira","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Janine","family":"Kniess","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vinicius","family":"Marques","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"issue":"12","key":"6_CR1","doi-asserted-by":"publisher","first-page":"5072","DOI":"10.1109\/JSEN.2016.2550599","volume":"16","author":"MA Alsheikh","year":"2016","unstructured":"Alsheikh, M.A., Lin, S., Niyato, D., Tan, H.P.: Rate-distortion balanced data compression for wireless sensor networks. IEEE Sens. J. 16(12), 5072\u20135083 (2016)","journal-title":"IEEE Sens. J."},{"key":"6_CR2","unstructured":"Casta\u00f1eda, W.A.C.: Metodologia de gest\u00e3o ub\u00edqua para tecnologia m\u00e9dico-hospitalar utilizando tecnologias pervasivas. Ph.D. thesis, Universidade Federal de Santa Catarina (2016)"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Chen, Y., Shen, C., Zhang, K., Wang, H., Gao, Q.: Leach algorithm based on energy consumption equilibrium. In: 2018 International Conference on Intelligent Transportation, Big Data Smart City (ICITBS), pp. 677\u2013680, January 2018","DOI":"10.1109\/ICITBS.2018.00176"},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Dias, G.M., Bellalta, B., Oechsner, S.: Using data prediction techniques to reduce data transmissions in the IoT. In: 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT). IEEE, December 2016","DOI":"10.1109\/WF-IoT.2016.7845518"},{"issue":"4","key":"6_CR5","first-page":"452","volume":"12","author":"ME El-Telbany","year":"2017","unstructured":"El-Telbany, M.E., Maged, M.A.: Exploiting sparsity in wireless sensor networks for energy saving: a comparative study. Int. J. Appl. Eng. Res. 12(4), 452\u2013460 (2017)","journal-title":"Int. J. Appl. Eng. Res."},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Fathy, Y., Barnaghi, P., Tafazolli, R.: An adaptive method for data reduction in the internet of things. In: Proceedings of IEEE 4th World Forum on Internet of Things. IEEE (2018)","DOI":"10.1109\/WF-IoT.2018.8355187"},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Huang, Z., Li, M., Song, Y., Zhang, Y., Chen, Z.: Adaptive compressive data gathering for wireless sensor networks. In: 2017 3rd IEEE International Conference on Computer and Communications (ICCC), pp. 362\u2013367, December 2017","DOI":"10.1109\/CompComm.2017.8322572"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Jaber, A., Taam, M.A., Makhoul, A., Jaoude, C.A., Zahwe, O., Harb, H.: Reducing the data transmission in sensor networks through Kruskal-Wallis model. In: 2017 IEEE 13th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). IEEE, October 2017","DOI":"10.1109\/WiMOB.2017.8115780"},{"key":"6_CR9","unstructured":"Karim, S.: Energy efficiency in wireless sensor networks, through data compression. Master\u2019s thesis, University of Oslo (2017)"},{"key":"6_CR10","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.compeleceng.2016.09.025","volume":"58","author":"Z Li","year":"2017","unstructured":"Li, Z., Zhang, W., Qiao, D., Peng, Y.: Lifetime balanced data aggregation for the internet of things. Comput. Electr. Eng. 58, 244\u2013264 (2017)","journal-title":"Comput. Electr. Eng."},{"key":"6_CR11","unstructured":"Madden, S.: Intel Lab Data (2004). http:\/\/db.lcs.mit.edu\/labdata\/labdata.html. Accessed 15 Mar 2019"},{"key":"6_CR12","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.procs.2013.09.028","volume":"21","author":"A Masoum","year":"2013","unstructured":"Masoum, A., Meratnia, N., Havinga, P.J.: A distributed compressive sensing technique for data gathering in wireless sensor networks. Procedia Comput. Sci. 21, 207\u2013216 (2013). The 4th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN-2013) and the 3rd International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH)","journal-title":"Procedia Comput. Sci."},{"key":"6_CR13","unstructured":"Queensland Government: Ambient estuarine water quality monitoring data (includes near real-time sites) - 2012 to present day (2015). https:\/\/data.qld.gov.au\/dataset\/ambient-estuarine-water-quality-monitoring-data-near-real-time-sites-2012-to-present-day. Accessed 15 Mar 2019"},{"key":"6_CR14","unstructured":"Santini, S., Romer, K.: An adaptive strategy for quality-based data reduction in wireless sensor networks. In: Proceedings of the 3rd International Conference on Networked Sensing Systems (INSS 2006), pp. 29\u201336 (2006)"},{"key":"6_CR15","unstructured":"UK Power Networks: SmartMeter Energy Consumption Data in London Households (2015). https:\/\/data.london.gov.uk\/dataset\/smartmeter-energy-use-data-in-london-households. Accessed 15 Mar 2019"},{"issue":"2","key":"6_CR16","doi-asserted-by":"publisher","first-page":"750","DOI":"10.1016\/j.snb.2007.09.060","volume":"129","author":"SD Vito","year":"2008","unstructured":"Vito, S.D., Massera, E., Piga, M., Martinotto, L., Francia, G.D.: On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario. Sens. Actuators B: Chem. 129(2), 750\u2013757 (2008)","journal-title":"Sens. Actuators B: Chem."},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Zegarra, E.T., Schouery, R.C.S., Miyazawa, F.K., Villas, L.A.: A continuous enhancement routing solution aware of data aggregation for wireless sensor networks. In: 2016 IEEE 15th International Symposium on Network Computing and Applications (NCA), pp. 93\u2013100, October 2016","DOI":"10.1109\/NCA.2016.7778600"}],"container-title":["Lecture Notes in Computer Science","Green, Pervasive, and Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-19223-5_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T11:11:32Z","timestamp":1694776292000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-19223-5_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030192228","9783030192235"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-19223-5_6","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":"27 April 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"GPC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Green, Pervasive, and Cloud Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Uberl\u00e2ndia","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 May 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 May 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"gpc2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.gpc2019.facom.ufu.br\/index.html","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"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"38","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"17","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"45% - 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"}},{"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"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}