A Cloud-Based Urban Monitoring System by Using a Quadcopter and Intelligent Learning Techniques

[1] U. Lee, B. Zhou, M. Gerla, E. Magistretti, P. Bellavista, A. Corradi, “Mobeyes: smart mobs for urban monitoring with a vehicular sensor network,” IEEE Wireless Communications, vol. 13, no. 5, 2006, pp. 52–57. DOI: 10.1109/WC-M.2006.250358 Search in Google Scholar

[2] P. J. Urban, G. Vall-Llosera, E. Medeiros, S. Dahlfort, “Fiber plant manager: An OTDR-and OTM-based PON monitoring system,” IEEE Communications Magazine, vol. 51, no. 2, 2013, pp. S9–S15. DOI: 10.1109/MCOM.2013.6461183 Search in Google Scholar

[3] A. del Amo, A. Martínez-Gracia, A. A. Bayod-Rújula, J. Antoñanzas, “An innovative urban energy system constituted by a photovoltaic/thermal hybrid solar installation: Design, simulation and monitoring,” Applied Energy, vol. 186, 2017, pp. 140–51. DOI: 10.1016/j.apenergy.2016.07.011 Search in Google Scholar

[4] I. Sa, P. Corke, “Vertical infrastructure inspection using a quadcopter and shared autonomy control,” Field and Service Robotics. Springer, 2014, pp. 219–32. DOI: 10.1007/978-3-642-40686-7_15 Search in Google Scholar

[5] X. Song, K. Mann, E. Allison, S.-C. Yoon, H. Hila, A. Muller, C. Gieder, “A quadcopter controlled by brain concentration and eye blink”, Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB), Philadelphia, PA, 3-3 Dec. 2016, pp. 1–4. DOI: 10.1109/SPMB.2016.7846875 Search in Google Scholar

[6] D. E. Chang, Y. Eun, “Global Chartwise Feedback Linearization of the Quadcopter with a Thrust Positivity Preserving Dynamic Extension,” IEEE Transactions on Automatic Control, vol. 62, no. 9, 2017, pp. 4747–52. Search in Google Scholar

[7] C. Yang, Q. Huang, Z. Li, K. Liu, F. Hu, “Big Data and cloud computing: innovation opportunities and challenges,” International Journal of Digital Earth, vol. 10, no. 1, 2017, pp. 13–53. Search in Google Scholar

[8] M. Sookhak, A. Gani, M. K. Khan, R. Buyya, “Dynamic remote data auditing for securing big data storage in cloud computing,” Information Sciences, vol. 380, 2017, pp. 101–16. DOI: 10.1016/j.ins.2015.09.004 Search in Google Scholar

[9] Y. Zhang, M. Qiu, C. -W. Tsai, M. M. Hassan, A. Alamri, “Health-CPS: Healthcare cyber-physical system assisted by cloud and big data,” IEEE Systems Journal, vol. 11, no. 1, 2017, pp. 88–95. DOI: 10.1109/JSYST.2015.2460747 Search in Google Scholar

[10] J. L. Schnase, D. Q. Duffy, G. S. Tamkin, D. Nadeau, J. H. Thompson, C. M. Grieg, M. A. McInerney, W. P. Webster, “MERRA analytic services: meeting the big data challenges of climate science through cloud-enabled climate analytics-as-a-service,” Computers, Environment and Urban Systems, vol. 61, 2017, pp. 198–211. DOI: 10.1016/j.compenvurbsys.2013.12.003 Search in Google Scholar

[11] Nakamura J., Image sensors and signal processing for digital still cameras, CRC Press, Boca Raton, 2016. Search in Google Scholar

[12] Y. LeCun, Y. Bengio, G. Hinton, “Deep learning”, Nature, vol. 521, no. 7553, 2015, pp. 436–44. DOI: 10.1038/nature14539 Search in Google Scholar

[13] Goodfellow I., Bengio Y., Courville A., Deep Learning, MIT Press, 2016. Search in Google Scholar

[14] W. Samek, A. Binder, G. Montavon, S. Lapuschkin, K. -R. Müller, “Evaluating the visualization of what a deep neural network has learned,” IEEE transactions on neural networks and learning systems, vol. PP, no. 99, 2016, pp. 1–14. DOI: 10.1109/TNNLS.2016.2599820 Search in Google Scholar

[15] F. J. Cabrerizo, F. Chiclana, R. Al-Hmouz, A. Morfeq, A. S. Balamash, E. Herrera-Viedma, “Fuzzy decision making and consensus: challenges,” Journal of Intelligent & Fuzzy Systems, vol. 29, no. 3, 2015, pp. 1109–1118. DOI: 10.3233/IFS-151719 Search in Google Scholar

[16] N. A. Korenevskiy, “Application of fuzzy logic for decision-making in medical expert systems,” Biomedical Engineering, vol. 49, no. 1, 2015, pp. 46–49. DOI: 10.1007/s10527-015-9494-x Search in Google Scholar

[17] T. Runkler, S. Coupland, R. John, “Interval type-2 fuzzy decision making,” International Journal of Approximate Reasoning, vol. 80, 2017, pp. 217–224. DOI: 10.1016/j.ijar.2016.09.007 Search in Google Scholar

[18] Z. Hao, Z. Xu, H. Zhao, R. Zhang, “Novel intuitionistic fuzzy decision making models in the framework of decision field theory,” Information Fusion, vol. 33, 2017, pp. 57–70. DOI: 10.1016/j.inffus.2016.05.001 Search in Google Scholar

[19] Devroye L., Györfi L., Lugosi G., A probabilistic theory of pattern recognition, Springer Science & Business Media: Berlin, 2013, vol. 31. Search in Google Scholar

[20] C. Bielza, P. Larrañaga, “Discrete Bayesian network classifiers: a survey,” ACM Computing Surveys (CSUR), vol. 47, no. 1, 2014, Article No. 5. DOI: 10.1145/2576868 Search in Google Scholar

[21] F. Calabrese, M. Colonna, P. Lovisolo, D. Parata, C. Ratti, “Real-time urban monitoring using cell phones: A case study in Rome,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 1, 2011, pp. 141–151. DOI: 10.1109/TITS.2010.2074196 Search in Google Scholar

[22] K. Abraham, S. Pandian, “A low-cost mobile urban environmental monitoring system,” Proceedings of the 4th IEEE International Conference on Intelligent Systems Modelling & Simulation (ISMS), Bangkok, Thailand, 29-31 Jan. 2013, pp. 659–664. DOI: 10.1109/ISMS.2013.76 Search in Google Scholar

[23] J. Lee, Z. Zhong, B. Du, S. Gutesa, K. Kim, “Low-cost and energy-saving wireless sensor network for real-time urban mobility monitoring system,” Journal of Sensors, vol. 2015, 2015, pp. 1–8. DOI: 10.1155/2015/685786 Search in Google Scholar

[24] K. B. Shaban, A. Kadri, E. Rezk, “Urban air pollution monitoring system with forecasting models,” IEEE Sensors Journal, vol. 16, no. 8, 2016, pp. 2598–2606. DOI: 10.1109/JSEN.2016.2514378 Search in Google Scholar

[25] W. Zhao, T. H. Go, “Quadcopter formation flight control combining MPC and robust feedback linearization,” Journal of the Franklin Institute, vol. 351, no. 3, 2014, pp. 1335–1355. DOI: 10.1016/j.jfranklin.2013.10.021 Search in Google Scholar

[26] J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Networks, vol. 61, 2015, pp. 85–117. DOI: 10.1016/j.neunet.2014.09.003 Search in Google Scholar

[27] M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.-M. Jodoin, H. Larochelle, “Brain tumor segmentation with deep neural networks,” Medical Image Analysis, vol. 35, 2017, pp. 18–31. DOI: 10.1016/j.media.2016.05.004 Search in Google Scholar

[28] C.-C. Hsu, K. -S. Wang, H.-Y. Chung, S.-H. Chang, “A study of visual behavior of multidimensional scaling for kernel perceptron algorithm,” Neural Computing and Applications, vol. 26, no. 3, 2015, pp. 679–691. DOI: 10.1007/s00521-014-1746-2 Search in Google Scholar

[29] Ross T. J., Fuzzy logic with engineering applications, John Wiley & Sons, 2009. Search in Google Scholar

[30] M. Cococcioni, L. Foschini, B. Lazzerini, F. Marcelloni, “Complexity reduction of Mamdani Fuzzy Systems through multi-valued logic minimization,” Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Singapore, 12–15 Oct. 2008, pp. 1782–1787. DOI: 10.1109/ICSMC.2008.4811547 Search in Google Scholar

[31] M. S. Gharajeh, S. Khanmohammadi, “Dispatching Rescue and Support Teams to Events Using Ad Hoc Networks and Fuzzy Decision Making in Rescue Applications,” Journal of Control and Systems Engineering, vol. 3, no. 1, 2015, pp. 35–50. DOI:10.18005/JCSE0301003 Search in Google Scholar

[32] M. S. Gharajeh, S. Khanmohammadi, “DFRTP: Dynamic 3D Fuzzy Routing Based on Traffic Probability in Wireless Sensor Networks,” IET Wireless Sensor Systems, vol. 6, no. 6, 2016, pp. 211–219. DOI: 10.1049/iet-wss.2015.0008 Search in Google Scholar

[33] S. Khanmohammadi, M. S. Gharajeh, “A Routing Protocol for Data Transferring in Wireless Sensor Networks Using Predictive Fuzzy Inference System and Neural Node,” Ad Hoc & Sensor Wireless Networks, vol. 38, no. 1–4, 2017, pp. 103–124. Search in Google Scholar

[34] M. S. Gharajeh, “FSB-System: A Detection System for Fire, Suffocation, and Burn Based on Fuzzy Decision Making, MCDM, and RGB Model in Wireless Sensor Networks,” Wireless Personal Communications, vol. 105, no. 4, 2019, pp. 1171–1213. DOI: 10.1007/s11277-019-06141-3 Search in Google Scholar

[35] M. S. Gharajeh, “Implementation of an Autonomous Intelligent Mobile Robot for Climate Purposes,” International Journal of Ad Hoc and Ubiquitous Computing, vol. 31, no. 3, 2019, pp. 200–218. DOI: 10.1504/IJAHUC.2019.10022345 Search in Google Scholar

[36] M. S. Gharajeh, “A Knowledge and Intelligence-based Strategy for Resource Discovery on IaaS Cloud Systems,” International Journal of Grid and Utility Computing, vol. 12, no. 2, 2021, pp. 205–221. DOI: 10.1504/IJGUC.2021.114819 Search in Google Scholar

[37] M. S. Gharajeh, H. B. Jond, “Speed Control for Leader-Follower Robot Formation Using Fuzzy System and Supervised Machine Learning,” Sensors, vol. 21, no. 10, 2021, Article ID 3433. DOI: 10.3390/s21103433 Search in Google Scholar

[38] B. C. Ko, K.-H. Cheong, J.-Y. Nam, “Early fire detection algorithm based on irregular patterns of flames and hierarchical Bayesian Networks,” Fire Safety Journal, vol. 45, no. 4, 2010, pp. 262–270. DOI: 10.1016/j.firesaf.2010.04.001 Search in Google Scholar

[39] B. U. Töreyin, Y. Dedeoğlu, U. Güdükbay, A. E. Cetin, “Computer vision based method for real-time fire and flame detection,” Pattern Recognition Letters, vol. 27, no. 1, 2006, pp. 49–58. DOI: 10.1016/j.patrec.2005.06.015 Search in Google Scholar

[40] B. C. Ko, K.-H. Cheong, J.-Y. Nam, “Fire detection based on vision sensor and support vector machines,” Fire Safety Journal, vol. 44, no. 3, 2009, pp. 322–329. DOI: 10.1016/j.firesaf.2008.07.006 Search in Google Scholar