Page 369 - From Smart Grid to Internet of Energy
P. 369
Big data, privacy and security in smart grids Chapter 8 333
[10] T. Rabl, H.-A. Jacobsen, Big data generation. in: T. Rabl, M. Poess, C. Baru, H.-A. Jacobsen
(Eds.), Specifying Big Data Benchmarks, Springer, Berlin, Heidelberg, 2014, pp. 20–27,
https://doi.org/10.1007/978-3-642-53974-9_3.
[11] O.B. Sezer, E. Dogdu, A.M. Ozbayoglu, Context-aware computing, learning, and big data in
internet of things: a survey. IEEE Internet Things J. 5 (2018) 1–27, https://doi.org/10.1109/
JIOT.2017.2773600.
[12] P.P. Ray, A survey on internet of things architectures. J. King Saud Univ. Comput. Inf. Sci.
(2016)https://doi.org/10.1016/j.jksuci.2016.10.003.
[13] W. Ejaz, A. Anpalagan, Internet of Things for Smart Cities: Technologies, Big Data and Secu-
rity, Springer Nature Switzerland AG, New York, NY, 2018.
[14] M. Jaradat, M. Jarrah, A. Bousselham, Y. Jararweh, M. Al-Ayyoub, The internet of energy:
smart sensor networks and big data management for smart grid. Proc. Comput. Sci.
56 (2015) 592–597, https://doi.org/10.1016/j.procs.2015.07.250.
[15] GSMA Report, IoT Big Data Framework Architecture, GSM Association, 2018.
[16] D. Ardagna, C. Cappiello, W. Sama ´, M. Vitali, Context-aware data quality assessment for big
data. Futur. Gener. Comput. Syst. 89 (2018) 548–562, https://doi.org/10.1016/j.
future.2018.07.014.
[17] A.M.S. Osman, A novel big data analytics framework for smart cities. Futur. Gener. Comput.
Syst. 91 (2019) 620–633, https://doi.org/10.1016/j.future.2018.06.046.
[18] A. Iosifidis, A. Tefas, I. Pitas, M. Gabbouj, Big media data analysis. Signal Process. Image
Commun. 59 (2017) 105–108, https://doi.org/10.1016/j.image.2017.10.004.
[19] D. Garcı ´a-Gil, J. Luengo, S. Garcı ´a, F. Herrera, Enabling smart data: noise filtering in big data
classification. Inf. Sci. 479 (2019) 135–152, https://doi.org/10.1016/j.ins.2018.12.002.
[20] A. Stetco, F. Dinmohammadi, X. Zhao, V. Robu, D. Flynn, M. Barnes, J. Keane, G. Nenadic,
Machine learning methods for wind turbine condition monitoring: a review. Renew. Energy
133 (2019) 620–635, https://doi.org/10.1016/j.renene.2018.10.047.
[21] I. Portugal, P. Alencar, D. Cowan, The use of machine learning algorithms in recommender
systems: a systematic review. Expert Syst. Appl. 97 (2018) 205–227, https://doi.org/
10.1016/j.eswa.2017.12.020.
[22] M.S. Mahdavinejad, M. Rezvan, M. Barekatain, P. Adibi, P. Barnaghi, A.P. Sheth, Machine
learning for internet of things data analysis: a survey. Digital Commun. Netw. 4 (2018)
161–175, https://doi.org/10.1016/j.dcan.2017.10.002.
[23] N. Dey, A.E. Hassanien, C. Bhatt, A.S. Ashour, S.C. Satapathy (Eds.), Internet of Things and
Big Data Analytics Toward Next-Generation Intelligence, Springer International Publishing,
Cham, 2018https://doi.org/10.1007/978-3-319-60435-0.
[24] C. Tu, X. He, Z. Shuai, F. Jiang, Big data issues in smart grid—a review. Renew. Sust. Energ.
Rev. 79 (2017) 1099–1107, https://doi.org/10.1016/j.rser.2017.05.134.
[25] B.-A. Schuelke-Leech, B. Barry, M. Muratori, B.J. Yurkovich, Big data issues and opportu-
nities for electric utilities. Renew. Sust. Energ. Rev. 52 (2015) 937–947, https://doi.org/
10.1016/j.rser.2015.07.128.
[26] A.A. Munshi, Y.A.-R.I. Mohamed, Big data framework for analytics in smart grids. Electr.
Power Syst. Res. 151 (2017) 369–380, https://doi.org/10.1016/j.epsr.2017.06.006.
[27] M. Marjani, et al., Big IoT data analytics: architecture, opportunities, and open research chal-
lenges. IEEE Access 5 (2017) 5247–5261, https://doi.org/10.1109/ACCESS.2017.2689040.
[28] H. Li, Enabling Secure and Privacy Preserving Communications in Smart Grids, Springer,
Cham, 2014.