Spectrum Allocation in Cognitive Radio based Traffic Monitoring System Using Machine Learning
Keywords:
vehicle tracking, traffic management, cognitive radio networks, spectrum allocationAbstract
Vehicle tracking and Traffic Monitoring is essential as it forms the main dimension of a smart city. Globally, during the last decade the number of automobiles in roadways has increased drastically. Traffic monitoring in such a high traffic density era is significantly difficult in various developing countries. Hence, the work focuses on regulating traffic jams by tracking the vehicle and transmitting the data to the regulating authorities in shorter duration with the help of Cognitive Radio technology. The CR technology is very useful for effective traffic monitoring to transmit the traffic management parameters by exploiting Primary User’s (PU) spectrum. For spectrum detection and allocation for high-speed transmission of traffic parameters, various tree related machine learning algorithms like random forest, decision trees and XGBoost are used, examined and compared for better results. Of these, random forest gives high accurate prediction of available spectrum and allocation. On applying the model, we ensure that timely delivery of traffic monitoring information can help in better traffic management and vehicle tracking.
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R. Janani, K. Renuka, A. Aruna, et al., “Iot in smart cities: A contemporary survey,” Global Transitions Proceedings, vol. 2, no. 2, pp. 187–193, 2021.
P. Bellini, P. Nesi, and G. Pantaleo, “Iot-enabled smart cities: A review of concepts, frameworks and key technologies,” Applied Sciences, vol. 12, no. 3, p. 1607, 2022.
C. S. Lai, L. L. Lai, and Q. H. Lai, “Smart city,” in Smart Grids and Big Data Analytics for Smart Cities, pp. 1–171, Springer, 2021.
M. Derawi, Y. Dalveren, and F. A. Cheikh, “Internet-of-Things-based smart transportation systems for safer roads,” in Proc. IEEE 6th World Forum Internet Things (WF-IoT), 2020, pp. 1–4.
Peppa MV, Bell D, Komar T, Xiao W. Urban traffic flow analysis based on deep learning car detection from cctv image series. Int Arch Photogramm Remote Sens Spat Inf Sci. 2018;42(4):565–72. https://doi.org/10.5194/isprsarchives-XLII-4-499-2018.
Fedorov A, Nikolskaia K, Ivanov S, Shepelev V, Minbaleev A. Traffic flow estimation with data from a video surveillance camera. J Big Data. 2019. https://doi.org/10.1186/s40537-019-0234-z.
M.A. Rahman, A.T. Asyhari, M.Z.A. Bhuiyan, Q.M. Salih, K.Z.B. Zamli, L-CAQ: joint link-oriented channel-availability and channel-quality based channel selection for mobile cognitive radio networks J. Netw. Comput. Appl., 113 (2018), pp. 26-35
S. Tyagi, S. Tanwar, N. Kumar, J.J. Rodrigues Cognitive radio-based clustering for opportunistic shared spectrum access to enhance lifetime of wireless sensor network Pervasive Mob. Comput., 22 (2015), pp. 90-112
M. Rahim, M. Rahman, M.A. Rahman, A.J.M. Muzahid, S.F. Kamarulzaman, et al. A framework of iot-enabled vehicular noise intensity monitoring system for smart city International Conference on Innovative Technology, Engineering and Science, Springer (2020), pp. 194-205
J. Si, H. Huang, Z. Li, B. Hao, and R. Gao, “Performance analysis of adaptive modulation in cognitive relay network with cooperative spectrum sensing,” IEEE Trans. Commun., vol. 62, no. 7, pp. 2198–2211, Jul. 2014.
P. Goswami et al., “AI based energy efficient routing protocol for intelligent transportation system,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 2, pp. 1670–1679, Feb. 2022.
Y. Liu, B. Tian, S. Chen, F. Zhu, and K. Wang,” A survey of vision-based vehicle detection and tracking techniques in its,”in Proc. IEEE Int. Conf. Veh. Electron. Saf., July 2020.
N. Sammaknejad, Y. Zhao, and B. Huang, “A review of the expectation maximization algorithm in data-driven process identification,” Journal of Process Control, vol. 73, pp. 123-136, 2019.
N. Senthilkumaran, and S. Vaithegi, S, “Image Segmentation by Using Thresholding Techniques for Medical Images,” Computer Science and Engineering: An International Journal, vol. 6, no. 1, 2016.
N. Piperigkos, A. S. Lalos, K. Berberidis, and C. Anagnostopoulos, “Cooperative multi-modal localization in connected and autonomous vehicles,” in 2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS), pp. 1–5, IEEE, 2020.
G.-M. Hoang, B. Denis, J. Harri, and D. T. Slock, “Robust data fusion for ̈cooperative vehicular localization in tunnels,” in 2017 IEEE Intelligent Vehicles Symposium (IV), pp. 1372–1377, IEEE, 2017.
Zhang F, Li C, Yang F. Vehicle detection in urban traffic surveillance images based on convolutional neural networks with feature concatenation. Sensors. 2019;19(3):594. https://doi.org/10.3390/s19030594.
Rathore MM, Son H, Ahmad A, Paul A. Real-time video processing for traffic control in smart city using Hadoop ecosystem with GPUs. Soft Comput. 2018;22(5):1533–44. https://doi.org/10.1007/s00500-017-2942-7.
Alipio, Melchizedek I., Kathlyn Mae T. Peñalosa, and Julioh Roscoe C. Unida. "In-store customer traffic and path monitoring in small-scale supermarket using UWB-based localization and SSD-based detection." Journal of Ambient Intelligence and Humanized Computing 14.5 (2023): 4955-4969.
Mittal, Usha, Priyanka Chawla, and Rajeev Tiwari. "EnsembleNet: A hybrid approach for vehicle detection and estimation of traffic density based on faster R-CNN and YOLO models." Neural Computing and Applications 35.6 (2023): 4755-4774.
Ghasemi Darehnaei, Zeinab, et al. "Ensemble deep learning using faster r-cnn and genetic algorithm for vehicle detection in uav images." IETE Journal of Research 69.8 (2023): 5102-5111.
Lin, Z.; An, K.; Niu, H.; Hu, Y.; Chatzinotas, S.; Zheng, G.; Wang, J. SLNR-based secure energy efficient beamforming in multibeam satellite systems. IEEE Trans. Aerosp. Electron. Syst. 2022.
Chuang, C.L.; Chiu, W.Y.; Chuang, Y.C. Dynamic multiobjective approach for power and spectrum allocation in cognitive radio networks. IEEE Syst. J. 2021, 15, 5417–5428.
Alhussien, N.; Gulliver, T.A. Joint Resource and Power Allocation for Clustered Cognitive M2M Communications Underlaying Cellular Networks. IEEE Trans. Veh. Technol. 2022, 71, 8548–8560.
Anumandla, K.K.; Sabat, S.L.; Peesapati, R.; AV, P.; Dabbakuti, J.K.; Rout, R. Optimal spectrum and power allocation using evolutionary algorithms for cognitive radio networks. Internet Technol. Lett. 2021, 4, e207.
[26] Sumathi, D.; Manivannan, S. Stochastic approach for channel selection in cognitive radio networks using optimization techniques. Telecommun. Syst. 2021, 76, 167–186.
Kaur, A.; Kumar, K. Energy-efficient resource allocation in cognitive radio networks under cooperative multi-agent model-free reinforcement learning schemes. IEEE Trans. Netw. Serv. Manag. 2020, 17, 1337–1348.
Ostovar, A.; Zikria, Y.B.; Kim, H.S.; Ali, R. Optimization of resource allocation model with energy-efficient cooperative sensing in green cognitive radio networks. IEEE Access 2020, 8, 141594–141610.
Aslam, Muhammad Muzamil, et al. "Beyond6G-consensus traffic management in CRN, applications, architecture and key challenges." 2021 IEEE 11th International Conference on Electronics Information and Emergency Communication (ICEIEC) 2021 IEEE 11th International Conference on Electronics Information and Emergency Communication (ICEIEC). IEEE, 2021.
Ravish, Roopa, and Shanta Ranga Swamy. "Intelligent traffic management: A review of challenges, solutions, and future perspectives." Transport and Telecommunication Journal 22.2 (2021): 163-182.
[Aslam, Muhammad Muzamil, et al. "Consensus performance of traffic management system for cognitive radio network: an agent control approach." Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health: International 2019 Cyberspace Congress, CyberDI and CyberLife, Beijing, China, December 16–18, 2019, Proceedings, Part II 3. Springer Singapore, 2019.
Muhammad Muzamil Aslam, Liping Du, Xiaoyan Zhang, Yueyun Chen, Zahoor Ahmed, Bushra Qureshi, "Sixth Generation (6G) Cognitive Radio Network (CRN) Application, Requirements, Security Issues, and Key Challenges", Wireless Communications and Mobile Computing, vol. 2021, Article ID 1331428, 18 pages, 2021. https://doi.org/10.1155/2021/1331428
Mitra, A.; Bera, B.; Das, A.K.; Jamal, S.S.; You, I. Impact on blockchain-based AI/ML-enabled big data analytics for cognitive Internet of Things environment. Comput. Commun. 2023, 197, 173–185. [CrossRef]
Xu, G.; Khan, A.S.; Moshayedi, A.J.; Zhang, X.; Shuxin, Y. The Object Detection, Perspective and Obstacles In Robotic: A Review. EAI Endorsed Trans. AI Robot. 2022, 1, e13. [CrossRef]
Ragno, L.; Borboni, A.; Vannetti, F.; Amici, C.; Cusano, N. Application of Social Robots in Healthcare: Review on Characteristics, Requirements, Technical Solutions. Sensors 2023, 23, 6820. [CrossRef] [PubMed]
Paramasivam Thuraipandi, Sivagurunathan and Nagarajan, Sathish Kumar. ‘Cooperative Spectrum Sensing Based Hybrid Machine Learning Technique for Prediction of Secondary User in Cognitive Radio Networks’. 1 Jan. 2023: 3959 – 3971.
Ghazizadeh E, Abbasi D, Nezamabadi-pour H., “An enhanced two-phase SVM algorithm for cooperative spectrum sensing in Cognitive Radio Networks”, Int J Commun Syst 2018;32(2): e3856.
Coluccia A, Fascista A, Ricci G., “Spectrum sensing by higher order SVM based detection”, 27th European Signal Processing Conference (EUSIPCO). A Coruna, Spain; IEEE:2019.
Yang, S., Tong, C. Cognitive spectrum sensing algorithm based on an RBF neural network and machine learning. Neural Comput & Applic 35, 25045–25055 (2023). https://doi.org/10.1007/s00521-023-08488-y
Rose, Biji and Aruna Devi, B. ‘Spectrum Sensing in Cognitive Radio Networks Using an Ensemble of Machine Learning Frameworks and Effective Feature Extraction’. 1 Jan. 2023: 10495 – 10509.
Li, Liuwen, Wei Xie, and Xin Zhou. "Cooperative Spectrum Sensing Based on LSTM-CNN Combination Network in Cognitive Radio System." IEEE Access (2023).
Paul, Anal, and Kwonhue Choi. "Deep learning-based selective spectrum sensing and allocation in cognitive vehicular radio networks." Vehicular Communications 41 (2023): 100606.
Ali, M.; Nam, H. Optimization of spectrum utilization in cooperative spectrum sensing. Sensors 2019, 19, 1922. [CrossRef]
Liang, Ying-Chang, et al. "Sensing-throughput tradeoff for cognitive radio networks." IEEE transactions on Wireless Communications 7.4 (2008): 1326-1337.
Geron, A. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 2nd ed.; O’Reilly Media, Inc.: Sebastopol, CA, USA, 2019.
Rokach, L.; Maimon, O., Decision Trees. In Data Mining and Knowledge Discovery Handbook; Maimon, O., Rokach, L., Eds.; Springer: Boston, MA, USA, 2005; pp. 165–192
Dangeti, P. Statistics for Machine Learning, 1st ed.; Packt Publishing, Limited: Birmingham, AL, USA, 2017.
Liu, Y.; Wang, Y.; Zhang, J. New Machine Learning Algorithm: Random Forest. In Proceedings of the Information Computing and Applications, Chengde, China, 14–16 September 2012; Liu, B., Ma, M., Chang, J., Eds.; Springer: Berlin/Heidelberg, Germany, 2012; pp. 246–252.
Natekin, A.; Knoll, A. Gradient boosting machines, a tutorial. Front. Neurorobot. 2013, 7, 21.
Dhieb, N.; Ghazzai, H.; Besbes, H.; Massoud, Y. Extreme Gradient Boosting Machine Learning Algorithm For Safe Auto Insurance Operations. In Proceedings of the 2019 IEEE International Conference on Vehicular Electronics and Safety (ICVES), Cairo, Egypt, 4 September 2019; pp. 1–5.
Hossin, M.; Sulaiman, M.N. A Review on Evaluation Metrics for Data Classification Evaluations. Int. J. Data Min. Knowl. Manag. Process 2015, 5, 1–11.
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