Real-time Access and Processing of Massive Measurement Data in Smart Power Grids

[1] Cai, L. B., Zhang, W., Zhao, L. X., Yang, X. B., & Chen, L. (2017). Monitoring and operation analysis on power environment of computer room based on big data. Applied Mechanics and Materials, 864, 258-263. Search in Google Scholar

[2] Fang, X., Liu, Y., & Wang, Y. (2020). Research on data processing architecture of nqi service platform in intelligent measurement domain. Journal of Physics: Conference Series, 1607(1), 012128 (6pp). Search in Google Scholar

[3] Qian, J., Cao, Z., Dong, X., Shen, J., & Ye, Y. (2020). Two secure and efficient lightweight data aggregation schemes for smart grid. IEEE Transactions on Smart Grid, PP(99), 1-1. Search in Google Scholar

[4] Gorzaczany, M. B., Piekoszewski, J., & Filip Rudziński. (2020). A modern data-mining approach based on genetically optimized fuzzy systems for interpretable and accurate smart-grid stability prediction. Energies, 13. Search in Google Scholar

[5] Tan, S., Song, W. Z., Stewart, M., Yang, J., & Tong, L. (2017). Online data integrity attacks against real-time electrical market in smart grid. IEEE Transactions on Smart Grid, 9(99), 313-322. Search in Google Scholar

[6] Nejad, H. M., Movahhedinia, N., & Khayyambashi, M. R. (2017). Provisioning required reliability of wireless data communication in smart grid neighborhood area networks. Journal of Supercomputing, 73(2), 1-21. Search in Google Scholar

[7] Kalfarisi, R., Chew, A., Cai, J., Xue, M., Pok, J., & Wu, Z. Y. (2022). Predictive modeling framework accelerated by gpu computing for smart water grid data-driven analysis in near real-time. Advances in engineering software. Search in Google Scholar

[8] Zhou, C., Wang, L., & Wang, L. (2022). Lattice-based provable data possession in the standard model for cloud-based smart grid data management systems:. International Journal of Distributed Sensor Networks, 18(4), 137-147. Search in Google Scholar

[9] Eltayieb, A. L. F. (2019). An efficient attribute-based online/offline searchable encryption and its application in cloud-based reliable smart grid. Journal of systems architecture, 98(1). Search in Google Scholar

[10] Ahmad, I., Lin, J., Manshadi, S. D., Khodayar, M. E., & Vafamehr, A. (2019). A framework for expansion planning of data centers in electricity and data networks under uncertainty. IEEE Transactions on Smart Grid. Search in Google Scholar

[11] Debiao, H., Sherali, Z., Huaqun, W., & Qin, L. (2017). Lightweight data aggregation scheme against internal attackers in smart grid using elliptic curve cryptography. Wireless Communications & Mobile Computing, 2017, 1-11. Search in Google Scholar

[12] Moghaddass, R., & Wang, J. (2017). A hierarchical framework for smart grid anomaly detection using large-scale smart meter data. IEEE Transactions on Smart Grid, 1-1. Search in Google Scholar

[13] Rizwan, K. (2021). Towards big data electricity theft detection based on improved rusboost classifiers in smart grid. Energies, 14. Search in Google Scholar

[14] Kong, P. Y. (2017). Cost efficient data aggregation point placement with interdependent communication and power networks in smart grid. IEEE transactions on smart grid. Search in Google Scholar

[15] Chen, Y., Ortega, J. F. M., Castillejo, P., & Lopez, L. (2019). An elliptic curve-based scalable data aggregation scheme for smart grid. IEEE Systems Journal, PP(99), 1-12. Search in Google Scholar

[16] Wang, Y., Su, Z., Zhang, N., Chen, J., & Zhou, Z. (2020). Spds: a secure and auditable private data sharing scheme for smart grid based on blockchain and smart contract. IEEE Transactions on Industrial Informatics, PP(99), 1-1. Search in Google Scholar

[17] Saad, A., Youssef, T., Elsayed, A. T., Amin, A., Abdalla, O. H., & Mohammed, O. (2019). Data-centric hierarchical distributed model predictive control for smart grid energy management. IEEE transactions on industrial informatics, 15(7), 4086-4098. Search in Google Scholar

[18] Chehri, A., Fofana, I., & Yang, X. (2021). Security risk modeling in smart grid critical infrastructures in the era of big data and artificial intelligence. Sustainability, 13(6), 3196. Search in Google Scholar

[19] Qureshi, N. M. F., Siddiqui, I. F., Unar, M. A., Uqaili, M. A., Nam, C. S., & Shin, D. R., et al. (2019). An aggregate mapreduce data block placement strategy for wireless iot edge nodes in smart grid. Wireless Personal Communications. Search in Google Scholar

[20] Bukhsh, R., Javaid, N., Ali Khan, Z., Ishmanov, F., Afzal, M., & Wadud, Z. (2018). Towards fast response, reduced processing and balanced load in fog-based data-driven smart grid. Energies, 11(12). Search in Google Scholar

[21] Han, S. (2020). A study of data privacy methods in a smart grid cloud storage environment. Basic & clinical pharmacology & toxicology.(S1), 127. Search in Google Scholar

[22] Khayyambashi, Mohammad, Reza, Movahhedinia, Naser, & Nejad, et al. (2017). Provisioning required reliability of wireless data communication in smart grid neighborhood area networks. Journal of Supercomputing. Search in Google Scholar