Research on IoT data aggregation by fusing fast matching algorithms

[1] Daigger, G. T., Sharvelle, S., Arabi, M., et al. (2019). Progress and Promise Transitioning to the One Water/Resource Recovery Integrated Urban Water Management Systems. Journal of Environmental Engineering, 145(10), 04019061. Search in Google Scholar

[2] A J E, Matthias H. Müller a, B D G, et al. (2018). Relating groundwater heat-potential to city-scale heat-demand: A theoretical consideration for urban groundwater resource management. Applied Energy, 228, 1499-1505. Search in Google Scholar

[3] Ruizlozano, M., Tiradovalencia, P., Sianes, A., et al. (2020). SROI Methodology for Public Administration Decisions about Financing with Social Criteria. A Case Study. Sustainability, 12. Search in Google Scholar

[4] F Inês, Rita D A, Sandra C, et al. (2018). Sustainability policies and practices in public sector organisations: The case of the Portuguese Central Public Administration. Journal of Cleaner Production, 202, 616-630. Search in Google Scholar

[5] Behr, F., Oertzen, G., Dienst, M. (2021). Managing Sustainability and Carbon-Neutrality in the Public Administration—Case Report of a German State Institution. Sustainability, 13. Search in Google Scholar

[6] Hanif, S., Ahsan, A., Wise, G. (2020). Icebergs of Expertise-Based Leadership: The Role of Expert Leaders in Public Administration. Sustainability, 12. Search in Google Scholar

[7] Pan, H., Brian, D., Georgia, D., et al. (2018). Socio-Hydrology Modeling for Complex Urban Environments in Support of Integrated Land and Water Resource Management Practices. Land Degradation and Development. Search in Google Scholar

[8] Pvloaia, V. D., Georgescu, M. R., Popescul, D., et al. (2019). ESD for Public Administration: An Essential Challenge for Inventing the Future of Our Society. Sustainability, 11. Search in Google Scholar

[9] Szczepaniuk, D., Szczepaniuk, D. H., Rokicki, D., et al. (2019). Information Security Assessment in Public Administration. Computers & Security, 90, 101709. Search in Google Scholar

[10] Pan, H., Xu, H., Zheng, J., Tong, J., & Cheng, J. (2022). Twin robust matrix machine for intelligent fault identification of outlier samples in roller bearing. Knowledge-based systems. Search in Google Scholar

[11] Eppel, Elizabeth. (2017). Complexity thinking in public administration’s theories-in-use. Public Management Review. Search in Google Scholar

[12] Tomazevic, J. (2018). The interaction between occupational values and job satisfaction in an authoritative public administration organisation. Total Quality Management & Business Excellence, 29(1a2). Search in Google Scholar

[13] Gao, J., Gai, Q., Liu, B., & Shi, Q. (2021). Farm size and pesticide use: Evidence from agricultural production in China. China Agricultural Economic Review, ahead-of-print. Search in Google Scholar

[14] Maciejewski, M. (2017). To do more, better, faster and more cheaply: using big data in public administration. International Review of Administrative Sciences, 83(1_suppl), 120-135. Search in Google Scholar

[15] Wang, Tian, et al. (2019). Privacy-enhanced data collection based on deep learning for the Internet of Vehicles. IEEE Transactions on Industrial Informatics, 16(10), 6663-6672. Search in Google Scholar

[16] Li, Ting, et al. (2022). Trust data collections via vehicles joint with unmanned aerial vehicles in the smart Internet of Things. Transactions on Emerging Telecommunications Technologies, 33(5), e3956. Search in Google Scholar

[17] Almutairi, Mubarak S. (2022). Deep learning-based solutions for 5G network and 5G-enabled Internet of Vehicles: advances, meta-data analysis, and future direction. Mathematical Problems in Engineering, 2022. Search in Google Scholar

[18] Tsouros, Dimosthenis C., et al. (2019). Data acquisition and analysis methods in UAV-based applications for Precision Agriculture. 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS). IEEE. Search in Google Scholar

[19] Sun, Zeyu, et al. (2019). Mobile intelligent computing in Internet of Things: An optimized data gathering method based on compressive sensing. IEEE Access, 7, 66110-66122. Search in Google Scholar

[20] Hu, Huimin, et al. (2020). AoI-minimal trajectory planning and data collection in UAV-assisted wireless powered IoT networks. IEEE Internet of Things Journal, 8(2), 1211-1223. Search in Google Scholar

[21] Salim, Ahmed, et al. (2020). A secure data gathering scheme based on properties of primes and compressive sensing for IoT-based WSNs. IEEE Sensors Journal, 21(4), 5553-5571. Search in Google Scholar

[22] Rajšp, Alen, and Iztok Fister Jr. (2020). A systematic literature review of intelligent data analysis methods for smart sport training. Applied Sciences, 10(9), 3013. Search in Google Scholar

[23] Rahmani, A. M., Babaei, Z., & Souri, A. (2021). Event-driven IoT architecture for data analysis of reliable healthcare application using complex event processing. Cluster Computing, 24(2), 1347-1360. Search in Google Scholar

[24] Liu, Y., et al. (2020). Resource allocation and 3-D placement for UAV-enabled energy-efficient IoT communications. IEEE Internet of Things Journal, 8(3), 1322-1333. Search in Google Scholar

[25] Bindhu, V., & Ranganathan, G. (2021). Hyperspectral image processing in Internet of Things model using clustering algorithm. Journal of ISMAC, 3.02, 163-175. Search in Google Scholar

[26] Demertzis, K., et al. (2021). Darknet traffic big-data analysis and network management for real-time automating of the malicious intent detection process by a weight agnostic neural networks framework. Electronics, 10.7, 781. Search in Google Scholar

[27] Laska, M., et al. (2018). A scalable architecture for real-time stream processing of spatiotemporal IoT stream data—Performance analysis on the example of map matching. ISPRS International Journal of Geo-Information, 7.7, 238. Search in Google Scholar

[28] Prabavathy, S., Sundarakantham, K., & Mercy Shalinie, S. (2018). Design of cognitive fog computing for intrusion detection in the Internet of Things. Journal of Communications and Networks, 20.3, 291-298. Search in Google Scholar

[29] Jiang, L., et al. (2019). Toward practical privacy-preserving processing over encrypted data in IoT: an assistive healthcare use case. IEEE Internet of Things Journal, 6.6, 10177-10190. Search in Google Scholar

[30] Dang, L. M., et al. (2020). Sensor-based and vision-based human activity recognition: A comprehensive survey. Pattern Recognition, 108, 107561. Search in Google Scholar

[31] Kumar, Y. R. S., & Champa, H. N. (2022). Enhanced-knn (m-knn) based outlier detection and sensor data aggregation for large data streams in the iot-cloud. International Journal of Internet Protocol Technology. Search in Google Scholar