{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T21:40:24Z","timestamp":1773524424898,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,4,10]],"date-time":"2017-04-10T00:00:00Z","timestamp":1491782400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The efficient data access of streaming vehicle data is the foundation of analyzing, using and mining vehicle data in smart cities, which is an approach to understand traffic environments. However, the number of vehicles in urban cities has grown rapidly, reaching hundreds of thousands in number. Accessing the mass streaming data of vehicles is hard and takes a long time due to limited computation capability and backward modes. We propose an efficient streaming spatio-temporal data access based on Apache Storm (ESDAS) to achieve real-time streaming data access and data cleaning. As a popular streaming data processing tool, Apache Storm can be applied to streaming mass data access and real time data cleaning. By designing the Spout\/bolt workflow of topology in ESDAS and by developing the speeding bolt and other bolts, Apache Storm can achieve the prospective aim. In our experiments, Taiyuan BeiDou bus location data is selected as the mass spatio-temporal data source. In the experiments, the data access results with different bolts are shown in map form, and the filtered buses\u2019 aggregation forms are different. In terms of performance evaluation, the consumption time in ESDAS for ten thousand records per second for a speeding bolt is approximately 300 milliseconds, and that for MongoDB is approximately 1300 milliseconds. The efficiency of ESDAS is approximately three times higher than that of MongoDB.<\/jats:p>","DOI":"10.3390\/s17040815","type":"journal-article","created":{"date-parts":[[2017,4,13]],"date-time":"2017-04-13T02:39:17Z","timestamp":1492051157000},"page":"815","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Efficient Streaming Mass Spatio-Temporal Vehicle Data Access in Urban Sensor Networks Based on Apache Storm"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3424-1729","authenticated-orcid":false,"given":"Lianjie","family":"Zhou","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Luoyu Road 129, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3521-9972","authenticated-orcid":false,"given":"Nengcheng","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Luoyu Road 129, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeqiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,4,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/ACCESS.2015.2500733","article-title":"Smart cities, big data, and communities: Reasoning from the viewpoint of attractors","volume":"4","author":"Ianuale","year":"2015","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1109\/TSG.2012.2224389","article-title":"A decentralized security framework for data aggregation and access control in smart grids","volume":"4","author":"Ruj","year":"2011","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_3","first-page":"1","article-title":"Fine-Grained Access to Energy Resources of Smart Building via Energy Service Interface","volume":"99","author":"Lee","year":"2013","journal-title":"IEEE Internet Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"333","DOI":"10.3844\/jcssp.2012.333.336","article-title":"Efficient sensor stream data processing system to use cache technique for ubiquitous sensor network application service","volume":"8","author":"Park","year":"2012","journal-title":"J. Comput. Sci."},{"key":"ref_5","unstructured":"Kim, C.H., Park, K., Fu, J., and Elmasri, R. (2005, January 3\u20136). Architectures for Streaming Data Processing in Sensor Networks. Proceedings of the ACS\/IEEE 2005 International Conference on Computer Systems and Applications, Cairo, Egypt."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1109\/TC.2015.2417566","article-title":"A general communication cost optimization framework for big data stream processing in geo-distributed data centers","volume":"65","author":"Gu","year":"2015","journal-title":"IEEE Trans. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1016\/j.jcss.2014.04.022","article-title":"A spatiotemporal compression based approach for efficient big data processing on cloud","volume":"80","author":"Yang","year":"2014","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_8","unstructured":"M\u00fcller, R., Alonso, G., and Kossmann, D. (2007, January 7\u201310). SwissQM: Next Generation Data Processing in Sensor Networks. Proceedings of the Third Biennial Conference on Innovative Data Systems Research, CIDR, Asilomar, CA, USA."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1109\/TIM.2006.887321","article-title":"Application of an ANFIS algorithm to Sensor Data Processing","volume":"56","author":"Depari","year":"2007","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.procs.2016.05.322","article-title":"An evaluation of data stream processing systems for data driven applications","volume":"80","author":"Samosir","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"497","DOI":"10.4028\/www.scientific.net\/AMM.571-572.497","article-title":"A real-time log analyzer based on MongoDB","volume":"571\u2013572","author":"Lv","year":"2014","journal-title":"Appl. Mech. Mater."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.cpc.2015.03.006","article-title":"Distributed database kriging for adaptive sampling (D(2)KAS)","volume":"192","author":"Roehm","year":"2015","journal-title":"Comput. Phys. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3395","DOI":"10.1186\/1471-2105-11-S12-S1","article-title":"An overview of the Hadoop\/Mapreduce\/Hbase framework and its current applications in bioinformatics","volume":"11","author":"Taylor","year":"2010","journal-title":"BMC Bioinform."},{"key":"ref_14","first-page":"217","article-title":"Cassandra: An online failure prediction strategy for dynamically evolving systems","volume":"86","author":"Angelis","year":"1994","journal-title":"J. Infect. Dis."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yang, J., Ping, W., Liu, L., and Hu, Q. (2012, January 1\u20133). Memcache and MongoDB Based GIS Web Service. Proceedings of the Second International Conference on Cloud and Green Computing, Xiangtan, China.","DOI":"10.1109\/CGC.2012.19"},{"key":"ref_16","first-page":"1247","article-title":"Effects of cache mechanism on wireless data access","volume":"2","author":"Lin","year":"2003","journal-title":"IEEE Trans. Commun."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.dss.2008.05.001","article-title":"A new approach for a proxy-level web caching mechanism","volume":"46","author":"Kumar","year":"2008","journal-title":"Decis. Support Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.sysarc.2007.03.008","article-title":"A novel caching mechanism for peer-to-peer based media-on-demand streaming","volume":"54","author":"Tian","year":"2008","journal-title":"J. Syst. Architect."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4430","DOI":"10.3390\/s150204430","article-title":"Mining personal data using smartphones and wearable devices: A survey","volume":"15","author":"Habib","year":"2015","journal-title":"Sensors"},{"key":"ref_20","unstructured":"(2017, March 22). Apache Software Foundation. Available online: http:\/\/www.apache.org\/."},{"key":"ref_21","unstructured":"(2016, August 17). Apache Storm Prototype. Available online: http:\/\/storm.apache.org\/."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Karunaratne, P., Karunasekera, S., and Harwood, A. (2016). Distributed stream clustering using micro-clusters on apache storm. J. Parallel Distrib. Comput.","DOI":"10.1016\/j.jpdc.2016.06.004"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1109\/TSC.2010.3","article-title":"Interacting with the SOA-based internet of Things: Discovery, Query, Selection, and On-Demand Provisioning of Web Services","volume":"3","author":"Guinard","year":"2010","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_24","unstructured":"(2016, November 08). The Kafka Project. Available online: http:\/\/www.kafka.org\/."},{"key":"ref_25","unstructured":"Shieh, C.K., Huang, S.W., Sun, L.D., Tsai, M.F., and Chilamkurti, N. (2016). A topology-based scaling mechanism for apache storm. Int. J. Netw. Manag."},{"key":"ref_26","unstructured":"Br\u00f6ring, A., Stasch, C., and Echterhoff, J. (2012). OGC Sensor Observation Service Interface Standard (Version 2.0), Open Geospatial Consortium."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2652","DOI":"10.3390\/s110302652","article-title":"New generation sensor web enablement","volume":"11","author":"Echterhoff","year":"2011","journal-title":"Sensors"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ning, Q., Chen, C.A., Stoleru, R., and Chen, C. (2015, January 5\u20137). Mobile Storm: Distributed Real-Time Stream Processing for Mobile Clouds. Proceedings of the IEEE International Conference on Cloud NETWORKING, Niagara Falls, ON, Canada.","DOI":"10.1109\/CloudNet.2015.7335296"},{"key":"ref_29","unstructured":"(2016, June 17). 52 North SOS, 2016. Available online: http:\/\/52north.org\/communities\/sensorweb\/sos\/index.html."},{"key":"ref_30","unstructured":"Botts, M. (2007). OpenGIS Sensor Model Language (SensorML) Implementation Specification, Open Geospatial Consortium."},{"key":"ref_31","unstructured":"Cox, S. (2007). OGC Implementation Specification 07-022r1: Observations and Measurements\u2014Part 17 1\u2014Observation Schema, Open Geospatial Consortium."},{"key":"ref_32","unstructured":"Cox, S. (2007). OGC Implementation Specification 07-022r3: Observations and Measurements\u2014Part 19 2\u2014Sampling Features, Open Geospatial Consortium."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhou, L., Chen, N., Yuan, S., and Chen, Z. (2016). An Efficient Method of Sharing Mass Spatio-Temporal Trajectory Data Based on Cloudera Impala for Traffic Distribution Mapping in an Urban City. Sensors, 16.","DOI":"10.3390\/s16111813"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1007\/s10291-013-0339-3","article-title":"Performance assessment of single- and dual-frequency Beidou\/GPS single-epoch kinematic positioning","volume":"3","author":"He","year":"2014","journal-title":"GPS Solut."},{"key":"ref_35","unstructured":"(2016, November 18). Apache S4 Project. Available online: http:\/\/incubator.apache.org\/s4\/."},{"key":"ref_36","unstructured":"(2016, November 18). Spark Project. Available online: http:\/\/spark.apache.org\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/4\/815\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:32:20Z","timestamp":1760207540000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/4\/815"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,4,10]]},"references-count":36,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2017,4]]}},"alternative-id":["s17040815"],"URL":"https:\/\/doi.org\/10.3390\/s17040815","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,4,10]]}}}