Managing and Mining Sensor Data
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
All three tasks have been addressed both online and offline, with more alarm-related tasks used in online and continuous monitoring.
“For all three tasks data has been addressed both in an online and offline manner, with more alarm related tasks being naturally used in the context of online and continuous monitoring [1].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [1]
Most healthcare systems deal with data acquisition using adequate sensor set, transmission from subject to clinician, integration with other descriptive data, and data storage.
“According to [1,19], most healthcare systems are dealing with the following issues: (1) data acquisition using the adequate sensor set; (2) transmission of data from subject to clinician; (3) integration of data with other descriptive data; and (4) data storage.”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [1]
Preprocessing in healthcare involves filtering unusual data to remove artifacts and removing high-frequency noise.
“Preprocessing in the healthcare domain involves (1) filter unusual data to remove artifacts and (2) remove high frequency noise [1,13].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [1]
The main challenges of the preprocessing phase in healthcare systems include data formatting, data normalization, and data synchronization.
“The main challenges of the preprocessing phase in healthcare systems are addressed in [1] which includes data formatting, data normalization, and data synchronization.”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [1]
Appropriate data processing techniques are essential to make sense of the data.
“This means that in order to make sense of the data, appropriate data processing techniques are essential [1].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [1]
Cited by
Sorted by citations
The Internet of Things—A survey of topics and trends
Whitmore, A., Agarwal, A., & Da Xu, L. (2015) · Information Systems Frontiers · doi:10.1007/s10796-014-9489-2 · Cited by 1,568 · see citing works
Social Sensing: A New Approach to Understanding Our Socioeconomic Environments
Liu, Y., Liu, X., Gao, S., Gong, L., Kang, C., Zhi, Y., Chi, G., & Shi, L. (2015) · Annals of the Association of American Geographers · doi:10.1080/00045608.2015.1018773 · Cited by 804 · see citing works
Mining big data: current status, and forecast to the future
Fan, W., & Bifet, A. (2013) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2481244.2481246 · Cited by 677 · see citing works
“There are many applications of Big Data, for example the following [17; 2]: • Business: costumer personalization, churn detection • Technology: reducing process time from hours to seconds • Health: mining DNA of each person, to discover, monitor and improve health aspects of every one • Smart…”
MethodSun, A. Y., & Scanlon, B. R. (2019) · Environmental Research Letters · doi:10.1088/1748-9326/ab1b7d · Cited by 571 · see citing works
A survey of Internet-of-Things: Future vision, architecture, challenges and services
Singh, D., Tripathi, G., & Jara, A. J. (2014) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot.2014.6803174 · Cited by 511 · see citing works
A roadmap for security challenges in the Internet of Things
Sfar, A. R., Natalizio, E., Challal, Y., & Chtourou, Z. (2017) · Digital Communications and Networks · doi:10.1016/j.dcan.2017.04.003 · Cited by 490 · see citing works
Atluri, G., Karpatne, A., & Kumar, V. (2018) · ACM Computing Surveys · doi:10.1145/3161602 · Cited by 464 · see citing works
Banaee, H., Ahmed, M., & Loutfi, A. (2013) · Sensors · doi:10.3390/s131217472 · Cited by 452 · see citing works
A comprehensive survey of anomaly detection techniques for high dimensional big data
Thudumu, S., Branch, P., Jin, J., & Singh, J. (2020) · Journal Of Big Data · doi:10.1186/s40537-020-00320-x · Cited by 454 · see citing works
A Survey of Healthcare Internet of Things (HIoT): A Clinical Perspective
Habibzadeh, H., Dinesh, K., Shishvan, O. R., Boggio-Dandry, A., Gaurav Sharma, A., & Soyata, T. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2946359 · Cited by 419 · see citing works
“future health events, early detection of diseases, cost reduction, improved accuracy in clinical diagnosis, and clinical decision support [173], [174].”
BackgroundAn Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques
Krishnamurthi, R., Kumar, A., Gopinathan, D., Nayyar, A., & Qureshi, B. (2020) · Sensors · doi:10.3390/s20216076 · Cited by 439 · see citing works
Internet of Things research in supply chain management and logistics: A bibliometric analysis
Rejeb, A., Simske, S., Rejeb, K., Treiblmaier, H., & Zailani, S. H. M. (2020) · Internet of Things · doi:10.1016/j.iot.2020.100318 · Cited by 389 · see citing works
Future Internet of Things: open issues and challenges
Tsai, C., Lai, C., & Vasilakos, A. V. (2014) · Wireless Networks · doi:10.1007/s11276-014-0731-0 · Cited by 273 · see citing works
“In addition to the CI, many recent studies [10–13, 79, 80] has applied data mining or CI-based mining technologies to IoT.”
BackgroundInternet of multimedia things: Vision and challenges
Alvi, S. A., Afzal, B., Shah, G. A., Atzori, L., & Mahmood, W. (2015) · Ad Hoc Networks · doi:10.1016/j.adhoc.2015.04.006 · Cited by 241 · see citing works
An Introduction to Recommender Systems
Aggarwal, C. C. (2016) · Springer eBooks · doi:10.1007/978-3-319-29659-3_1 · Cited by 242 · see citing works
Self-citationDigital transformation of manufacturing through cloud services and resource virtualization
Borangiu, T., Trentesaux, D., André Thomas, B., Leitão, P., & Barata, J. (2019) · Computers in Industry · doi:10.1016/j.compind.2019.01.006 · Cited by 233 · see citing works
Computational Health Informatics in the Big Data Age: A Survey
Fang, R., Pouyanfar, S., Yang, Y., Chen, S.-C., & Iyengar, S. S. (2017) · ACM Computing Surveys · doi:10.1145/2932707 · Cited by 210 · see citing works
Yu, D., & Fang, C. (2023) · Remote Sensing · doi:10.3390/rs15051307 · Cited by 187 · see citing works
Tracking the Evolution of the Internet of Things Concept Across Different Application Domains
Ibarra-Esquer, J. E., Gonzalez-Navarro, F. F., Flores-Rios, B. L., Burtseva, L., & Astorga-Vargas, M. A. (2017) · Sensors · doi:10.3390/s17061379 · Cited by 182 · see citing works
Performance-Based Gas Turbine Health Monitoring, Diagnostics, and Prognostics: A Survey
Hanachi, H., Mechefske, C., Liu, J., Banerjee, A., & Chen, Y. (2018) · IEEE Transactions on Reliability · doi:10.1109/tr.2018.2822702 · Cited by 174 · see citing works
“Data cleaning is an extensive area of study, beyond the scope of this paper and interested readers are referred to [58] and [59] for further studies.”
BackgroundTime Series Management Systems: A Survey
Jensen, S. K., Pedersen, T. B., & Thomsen, C. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2740932 · Cited by 174 · see citing works
“3], [4], [5]. For a general introduction to storage and analysis of time series see [6], [7], a more in-depth introduction to sensor data management, data mining, and stream processing is provided by [8], [9], [10]. While general Database Management Systems (DBMSs), and in particular Relational Database Management Sys…”
InfluentialTrust management and reputation systems in mobile participatory sensing applications: A survey
Mousa, H., Mokhtar, S. B., Hasan, O., Younes, O., Hadhoud, M., & Brunie, L. (2015) · Computer Networks · doi:10.1016/j.comnet.2015.07.011 · Cited by 108 · see citing works
“In social participatory sensing, participants share sensed data with their friends [53], which can provide them higher motivation for contributing their resources to participatory sensing.”
BackgroundA Simulation-driven Methodology for IoT Data Mining Based on Edge Computing
Savaglio, C., & Fortino, G. (2021) · ACM Transactions on Internet Technology · doi:10.1145/3402444 · Cited by 110 · see citing works
HuGaDB: Human Gait Database for Activity Recognition from Wearable Inertial Sensor Networks
Chereshnev, R., & Kertész‐Farkas, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-73013-4_12 · Cited by 117 · see citing works
“The increasing availability of wearable body sensors leads to novel scientific studies and industrial applications [1].”
BackgroundDetecting concept drift in data streams using model explanation
Demšar, J., & Bosnić, Z. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.10.003 · Cited by 101 · see citing works
Spatiotemporal event detection: a review
Yu, M., Bambacus, M., Cervone, G., Clarke, K., Duffy, D. Q., Huang, Q., Li, J., Li, W., Li, Z., Liu, Q., et al. (2020) · International Journal of Digital Earth · doi:10.1080/17538947.2020.1738569 · Cited by 99 · see citing works
A secure and scalable storage system for aggregate data in IoT
Jiang, H., Shen, F., Chen, S., Li, K., & Jeong, Y. (2014) · Future Generation Computer Systems · doi:10.1016/j.future.2014.11.009 · Cited by 92 · see citing works
Efficient and flexible algorithms for monitoring distance-based outliers over data streams
Kontaki, M., Gounaris, A., Papadopoulos, A. N., Tsichlas, K., & Manolopoulos, Y. (2015) · Information Systems · doi:10.1016/j.is.2015.07.006 · Cited by 90 · see citing works
Prescriptive Control of Business Processes
Krumeich, J., Werth, D., & Loos, P. (2015) · Business & Information Systems Engineering · doi:10.1007/s12599-015-0412-2 · Cited by 95 · see citing works
A Survey of Data Semantization in Internet of Things
Shi, F., Li, Q., Zhu, T., & Ning, H. (2018) · Sensors · doi:10.3390/s18010313 · Cited by 85 · see citing works
“In the model-based data preprocessing approach [33], there exists a well-established model which is used to infer most probable sensor values.”
BackgroundMethodInfluentialTime Series Management Systems: A 2022 Survey
Jensen, S. K., Pedersen, T. B., & Thomsen, C. (2022) · VBN Forskningsportal (Aalborg Universitet) · Cited by 84 · see citing works
Hernandez-Suarez, A., Sánchez-Pérez, G., Toscano-Medina, K., Perez-Meana, H. M., Portillo-Portillo, J., Sánchez, V., & Villalba, L. J. G. (2019) · Sensors · doi:10.3390/s19071746 · Cited by 83 · see citing works
“Below are some characteristics that reinforce the notion that OSNs can be treated as social sensors [13,14]:”
BackgroundPourrahmani, H., Yavarinasab, A., Zahedi, R., Gharehghani, A., Mohammadi, M. H., Bastani, P., & herle, J. V. (2022) · Internet of Things · doi:10.1016/j.iot.2022.100579 · Cited by 82 · see citing works
Ensembles of incremental learners to detect anomalies in ad hoc sensor networks
Bosman, H. H. W. J., Iacca, G., Tejada, A., Wörtche, H. J., & Liotta, A. (2015) · Ad Hoc Networks · doi:10.1016/j.adhoc.2015.07.013 · Cited by 79 · see citing works
Data Quality Management in the Internet of Things
Zhang, L., Jeong, D., & Lee, S. (2021) · Sensors · doi:10.3390/s21175834 · Cited by 77 · see citing works
A unified semantic knowledge base for IoT
Nambi, S., Sarkar, C., Prasad, R., et al. (2014) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot.2014.6803232 · Cited by 75 · see citing works
On robust truth discovery in sparse social media sensing
Zhang, D., Han, R., Wang, D., et al. (2016) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2016.7840710 · Cited by 75 · see citing works
Wiemer, H., Drowatzky, L., & Ihlenfeldt, S. (2019) · Applied Sciences · doi:10.3390/app9122407 · Cited by 74 · see citing works
Azar, E., Nikolopoulou, C., & Papadopoulos, S. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.09.022 · Cited by 73 · see citing works
IoT-based occupancy detection system in indoor residential environments
Jeon, Y., Cho, C., Seo, J., Kwon, K., Park, H.-S., Oh, S., & Chung, I.-J. (2018) · Building and Environment · doi:10.1016/j.buildenv.2018.01.043 · Cited by 73 · see citing works
Indoor Localization System Based on Bluetooth Low Energy for Museum Applications
Giuliano, R., Cardarilli, G. C., Cesarini, C., et al. (2020) · Electronics · doi:10.3390/electronics9061055 · Cited by 68 · see citing works
Concept of predictive maintenance of production systems in accordance with industry 4.0
Špendla, L., Kebísek, M., Tanuška, P., & Hrčka, L. (2017) · IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI) · doi:10.1109/sami.2017.7880343 · Cited by 67 · see citing works
“The field level data are usually aggregated into data more suitable for particular decision support task [4].”
MethodŚlȩzak, D., Grzegorowski, M., Janusz, A., Kozielski, M., Nguyen, S. H., Sikora, M., Stawicki, S., & Wróbel, Ł. (2018) · Information Sciences · doi:10.1016/j.ins.2018.04.026 · Cited by 69 · see citing works
Uncertain Frequent Pattern Mining
Leung, C. K.-S. (2014) · Springer eBooks · doi:10.1007/978-3-319-07821-2_14 · Cited by 67 · see citing works
Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)
Fathy, Y., Barnaghi, P. M., & Tafazolli, R. (2018) · ACM Computing Surveys · doi:10.1145/3154525 · Cited by 66 · see citing works
Pluntke, U., Gerke, S., Sridhar, A., Weiss, J., & Michel, B. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856596 · Cited by 66 · see citing works
“The greatest challenges were: noise, missing data and of external disturbance [19].”
BackgroundINTERNET OF THINGS: A REVIEW OF APPLICATIONS AND TECHNOLOGIES
Vongsingthong, S., & Smanchat, S. (2014) · NRCT Data Center · doi:10.14456/sjst.2014.38 · Cited by 64 · see citing works
Spatial autocorrelation and entropy for renewable energy forecasting
Ceci, M., Corizzo, R., Malerba, D., & Rashkovska, A. (2019) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-018-0605-7 · Cited by 63 · see citing works
“Seeking spatial and temporal-aware information in a sensor network leads to additional computational challenges but, at the same time, creates new opportunities for storage, processing and analysis (Aggarwal 2013).”
BackgroundKalaivaani, P. T., & Krishnamoorthi, R. (2020) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2020.103271 · Cited by 63 · see citing works
Data replica placement approaches in fog computing: a review
Torabi, E., Ghobaei‐Arani, M., & Shahidinejad, A. (2022) · Cluster Computing · doi:10.1007/s10586-022-03575-6 · Cited by 62 · see citing works
Towards Scalable and Dynamic Social Sensing Using A Distributed Computing Framework
Zhang, D., Zheng, C., Wang, D., Thain, D., Mu, X., Madey, G., & Huang, C. (2017) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2017.196 · Cited by 58 · see citing works
Integrating machine learning in embedded sensor systems for Internet-of-Things applications
Lee, J., Stanley, M., Spanias, A., & Tepedelenlioğlu, C. (2016) · IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) · doi:10.1109/isspit.2016.7886051 · Cited by 60 · see citing works
“We are addressing here a sensor-based embedded system for analytics on the performance of IoT applications.”
BackgroundCurry, E., Derguech, W., Hasan, S., Kouroupetroglou, C., & Hassan, U. U. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.07.019 · Cited by 58 · see citing works
Cloud computing for improved healthcare: Techniques, potential and challenges
Chauhan, R., & Kumar, A. (2013) · E-Health and Bioengineering Conference (EHB) · doi:10.1109/ehb.2013.6707234 · Cited by 60 · see citing works
Cloud Databases for Internet-of-Things Data
Phan, T. A. M., Nurminen, J. K., & Francesco, M. D. (2014) · doi:10.1109/ithings.2014.26 · Cited by 58 · see citing works
Constraint-aware dynamic truth discovery in big data social media sensing
Zhang, D., Wang, D., & Zhang, Y. (2017) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2017.8257911 · Cited by 56 · see citing works
Data quality analysis and cleaning strategy for wireless sensor networks
Cheng, H., Feng, D., Shi, X., & Chen, C. (2018) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-018-1069-6 · Cited by 55 · see citing works
Knowledge Graphs and Explainable AI in Healthcare
Rajabi, E., & Kafaie, S. (2022) · Information · doi:10.3390/info13100459 · Cited by 63 · see citing works
Introduction to the Internet of Things
Schoder, D. (2018) · Wiley eBooks · doi:10.1002/9781119456735.ch1 · Cited by 60 · see citing works
Crowdsourcing in Remote Sensing: A Review of Applications and Future Directions
Saralıoğlu, E., & Güngör, O. (2020) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2020.2975132 · Cited by 48 · see citing works
“In this way, it is possible to meet remote sensing’s ground reference data requirement through the use of crowdsourcing [37], [38] and SM data.”
BackgroundSEIRA: An effective algorithm for IoT resource allocation problem
Tsai, C. (2017) · Computer Communications · doi:10.1016/j.comcom.2017.10.006 · Cited by 45 · see citing works
Mining top-k high-utility itemsets from a data stream under sliding window model
Dawar, S., Sharma, V., & Goyal, V. (2017) · Applied Intelligence · doi:10.1007/s10489-017-0939-7 · Cited by 44 · see citing works
“Data streams are generated from various sources like click-stream data from the web, sensor networks, telecommunication networks, RFID tags [1], etc.”
BackgroundReimagining the future: Techno innovation advancement in manufacturing
Onu, P., & Mbohwa, C. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2020.12.100 · Cited by 42 · see citing works
A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases
Ke, S., Gong, J., Li, S., et al. (2014) · Sensors · doi:10.3390/s140712990 · Cited by 37 · see citing works
“Since the coordinates could be erroneous due to, for example, blocked GPS signals or unstable positioning sensors, some data cleaning processes are needed to filter these errors [15].”
BackgroundAltulyan, M. S., Yao, L., Wang, X., Huang, C., Kanhere, S. S., & Sheng, Q. Z. (2021) · The Computer Journal · doi:10.1093/comjnl/bxab049 · Cited by 50 · see citing works
Internet of things: Architecture and enabling technologies
Goyal, P., Sahoo, A. K., & Sharma, T. K. (2020) · Materials Today Proceedings · doi:10.1016/j.matpr.2020.04.678 · Cited by 36 · see citing works
A new web-based solution for modelling data mining processes
Medvedev, V., Kurasova, O., Bernatavičienė, J., et al. (2017) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2017.03.001 · Cited by 35 · see citing works
Space-time series clustering: Algorithms, taxonomy, and case study on urban smart cities
Belhadi, A., Djenouri, Y., Nørvåg, K., Ramampiaro, H., Masseglia, F., & Lin, C.-W. (2020) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2020.103857 · Cited by 34 · see citing works
Applications of Frequent Pattern Mining
Aggarwal, C. C. (2014) · Springer eBooks · doi:10.1007/978-3-319-07821-2_18 · Cited by 33 · see citing works
Self-citationAn Introduction to Frequent Pattern Mining
Aggarwal, C. C. (2014) · Springer eBooks · doi:10.1007/978-3-319-07821-2_1 · Cited by 36 · see citing works
Self-citationENACT: Development, Operation, and Quality Assurance of Trustworthy Smart IoT Systems
Ferry, N., Solberg, A., Song, H., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-06019-0_9 · Cited by 32 · see citing works
Pease, S. G., CONWAY, P. P., & West, A. A. (2017) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2017.10.010 · Cited by 30 · see citing works
A Lightweight Semantic Web-based Approach for Data Annotation on IoT Gateways
Al-Osta, M., Bali, A., & Gherbi, A. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.08.339 · Cited by 30 · see citing works
A Review of Missing Sensor Data Imputation Methods
Faizin, R. N., Riasetiawan, M., & Ashari, A. (2019) · doi:10.1109/icst47872.2019.9166287 · Cited by 29 · see citing works
“Missing sensor data problem can affect the next data processing such as sensor data analysis and data mining, so it needs to handle properly [2].”
BackgroundA Unified Perspective for Disinformation Detection and Truth Discovery in Social Sensing: A Survey
Xu, F., Sheng, V. S., & Wang, M. (2021) · ACM Computing Surveys · doi:10.1145/3477138 · Cited by 28 · see citing works
“People or devices can contribute large amounts of observation in social sensing, a new paradigm [1, 127], to a perceive environment.”
BackgroundObserving the Natural World with Flickr
Wang, J., Korayem, M., & Crandall, D. (2013) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2013.66 · Cited by 28 · see citing works
A Novel Methodology to Label Urban Remote Sensing Images Based on Location-Based Social Media Photos
Chi, M., Sun, Z., Qin, Y., Shen, J., & Benediktsson, J. A. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2730585 · Cited by 28 · see citing works
“Therefore, it is possible to use social media data to shorten the semantic gap of RS with “ground reference” collection through social sensing [9] and crowdsensing [10].”
BackgroundA flexible data acquisition system for storing the interactions on mashup user interfaces
Fernández‐García, A. J., Iribarne, L., Corral, A., Criado, J., & Wang, J. Z. (2018) · Computer Standards & Interfaces · doi:10.1016/j.csi.2018.02.002 · Cited by 28 · see citing works
Online community detection in social sensing
Qi, G.-J., Aggarwal, C. C., & Huang, T. S. (2013) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2433396.2433474 · Cited by 27 · see citing works
Self-citationSCHNEIDER, P. L., & Xhafa, F. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-823818-9.00014-6 · Cited by 27 · see citing works
FIDC: A framework for improving data credibility in mobile crowdsensing
Zhou, T., Cai, Z., Wu, K., et al. (2017) · Computer Networks · doi:10.1016/j.comnet.2017.04.015 · Cited by 27 · see citing works
Hybrid delay tolerant network routing protocol for heterogeneous networks
Alaoui, E. A. A., Zekkori, H., & Agoujil, S. (2019) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2019.102456 · Cited by 27 · see citing works
Clone tag detection in distributed RFID systems
Kamaludin, H., Mahdin, H., & Abawajy, J. (2018) · PLoS ONE · doi:10.1371/journal.pone.0193951 · Cited by 27 · see citing works
Khalid, M., Akanmu, A. A., Murzi, H., et al. (2023) · Journal of Civil Engineering Education · doi:10.1061/jceecd.eieng-1902 · Cited by 26 · see citing works
Efficient Indexing and Query Processing of Model-View Sensor Data in the Cloud
Guo, T., Papaioannou, T. G., & Aberer, K. (2014) · Big Data Research · doi:10.1016/j.bdr.2014.07.005 · Cited by 25 · see citing works
Azar, E., & Menassa, C. C. (2015) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000521 · Cited by 25 · see citing works
Event driven and semantic based approach for data processing on IoT gateway devices
Al-Osta, M., Bali, A., & Gherbi, A. (2018) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-0843-y · Cited by 24 · see citing works
Curado, M., Madeira, H., da Cunha, P. R., et al. (2018) · Springer eBooks · doi:10.1007/978-3-319-77492-3_16 · Cited by 24 · see citing works
A Deep Learning Based Explainable Control System for Reconfigurable Networks of Edge Devices
Dassanayake, P. M., Anjum, A., Bashir, A. K., et al. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3083990 · Cited by 23 · see citing works
A survey on multisource heterogeneous urban sensor access and data management technologies
Yang, F., Hua, Y., Li, X., Yang, Z., Yu, X., & Fei, T. (2021) · Measurement Sensors · doi:10.1016/j.measen.2021.100061 · Cited by 23 · see citing works
SensorCentral: A Research Oriented, Device Agnostic, Sensor Data Platform
Rafferty, J., Synnott, J., Ennis, A., Nugent, C., McChesney, I., & Cleland, I. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67585-5_11 · Cited by 22 · see citing works
Data Extrapolation in Social Sensing for Disaster Response
Gu, S., Pan, C., Liu, H., et al. (2014) · IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS) · doi:10.1109/dcoss.2014.34 · Cited by 22 · see citing works
“A survey on analytic challenges in the field recently appeared [19].”
BackgroundSelf-citationA Scalable, Research Oriented, Generic, Sensor Data Platform
Rafferty, J., Synnott, J., Nugent, C., Ennis, A., Catherwood, P. A., McChesney, I., Cleland, I., & McClean, S. I. (2018) · IEEE Access · doi:10.1109/access.2018.2852656 · Cited by 22 · see citing works
“Notably, the majority of solutions have little or no support for research-oriented functionality [5], [8]–[19] Specifically, no platform adequately supports the eight research-oriented features that were identified by the can-”
Knowledge Integration in Smart Factories
Zenkert, J., Weber, C., Dornhöfer, M., Abu-Rasheed, H., & Fathi, M. (2021) · Encyclopedia · doi:10.3390/encyclopedia1030061 · Cited by 18 · see citing works
Building Data-Aware and Energy-Efficient Smart Spaces
Belapurkar, N., Harbour, J., Shelke, S., et al. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2834907 · Cited by 21 · see citing works
“lems include noisy and/or redundant readings [11], and high power consumption due to frequent readings and/or a lot of sensors [12], [13].”
BackgroundMaksimović, M., Vujović, V., & Omanović-Mikličanin, E. (2015) · HAICTA · Cited by 20 · see citing works
Sensor data analysis and anomaly detection using predictive analytics for process industries
Kameswari, U. S., & Babu, I. R. (2015) · doi:10.1109/wci.2015.7495528 · Cited by 20 · see citing works
Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
Nkemeni, V., Mieyeville, F., Kuaban, G. S., et al. (2024) · Sensors · doi:10.3390/s24217024 · Cited by 19 · see citing works
Location2Vec: Generating Distributed Representation of Location by Using Geo-tagged Microblog Posts
Shoji, Y., Takahashi, K., Dürst, M. J., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01159-8_25 · Cited by 19 · see citing works
Building Interoperable and Cross-Domain Semantic Web of Things Applications
Gyrard, A., Serrano, M., & Patel, P. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809764-9.00014-7 · Cited by 19 · see citing works
- Authors
- Aggarwal, C. C.
- Affiliations
- IBM (United States) (US)
- Corresponding author
- Charų C. Aggarwal
- ORCID
- Charų C. Aggarwal
- Venue
- Springer eBooks
- Publisher
- Springer US
- Published
- 2013
- OpenAlex
- W204230938
- Cited by
- 294
- References
- 0
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 9 · 2023: 13 · 2022: 12 · 2021: 21 · 2020: 25 · 2019: 28 · 2018: 42 · 2017: 30 · 2016: 24 · 2015: 34 · 2014: 34 · 2013: 8 · 2012: 1
- FWCI
- 21.52
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- Locations
- Springer eBooks (publishedVersion)
- Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Sensor Networks for Data Analysis
- Physical Sciences › Computer Science › Computer Networks and Communications › Energy Efficient Wireless Sensor Networks
- Physical Sciences › Computer Science › Computer Networks and Communications › Advanced Database Systems and Queries
- Keywords
- sensor data management, sensor data mining, sensor networks, sensor data acquisition, low-cost sensors
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
