Reliability estimation for drone communications by using an MLP-based model

Yıl 2022, Cilt: 6 Sayı: 3, 204 - 210, 15.12.2022

A F M Shahen Shah , Muhammet Ali Karabulut

https://doi.org/10.35860/iarej.1162019

Cited By: 2

Öz

Unmanned aerial vehicles (UAVs) or drones have been widely employed in both military and civilian tasks due to their reliability and low cost. UAVs ad hoc networks also acknowledged as flying ad-hoc networks (FANETs), are multi-UAV systems arranged in an ad hoc manner. In order to maintain consistent and effective communication, reliability is a prime concern in FANETs. This paper presents an analytical framework to estimate the reliability of drones’ communication in FANETs. The proposed system takes into account the reliability of communications in FANETs, including channel fading. The suggested analytical investigation is used to generate a dataset, then an artificial neural network (ANN) based multi-layer perceptron (MLP) model is used to estimate the reliability of drones’ communication. Moreover, to define the best MLP model with hidden layers, the correlation coefficient (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are obtained. Moreover, numerical results are presented which verify analytical studies.

Anahtar Kelimeler

ANN, Drone communication, FANETs, MLP, Reliability

Kaynakça

• 1. A. F. M. S. Shah, A Survey From 1G to 5G Including the Advent of 6G: Architectures, Multiple Access Techniques, and Emerging Technologie, in Proc. of IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC). 2022. Las Vegas, NV, USA: p. 1117-1123.
• 2. A. F. M. S. Shah, Haci Ilhan and Ufuk Tureli, Designing and Analysis of IEEE 802.11 MAC for UAVs Ad Hoc Networks, in Proc. of IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). 2019. New York, USA: 2019. p. 0934-0939.
• 3. Shah A. F. M. S. , Karabulut M. A. Optimization of drones communication by using meta-heuristic optimization algorithms, Sigma Journal of Engineering and Natural Sciences. 2022. 40(1): p. 108-117.
• 4. Surzhik D. I., G. S. Vasilyev and O. R. Kuzichkin, Development of UAV trajectory approximation techniques for adaptive routing in FANET networks, 7th International Conference on Control, Decision and Information Technologies (CoDIT), 2020. p. 1226-1230.
• 5. Bhardwaj V. and N. Kaur, An efficient routing protocol for FANET based on hybrid optimization algorithm, International Conference on Intelligent Engineering and Management (ICIEM), 2020. p. 252-255.
• 6. AlKhatieb A., E. Felemban and A. Naseer, Performance Evaluation of Ad-Hoc Routing Protocols in FANETs, IEEE Wireless Communications and Networking Conference Workshops (WCNCW), 2020. p. 1-6.
• 7. Lakew D. S., U. Sa’ad, N. -N. Dao, W. Na and S. Cho, Routing in Flying Ad Hoc Networks: A Comprehensive Survey, in IEEE Communications Surveys & Tutorials, 2020. 22(2): p. 1071-1120.
• 8. Akdemir B., Karabulut M. A. and Ilhan H., Performance of Deep Learning Methods in DF Based Cooperative Communication Systems, IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), 2021. p. 1-6.
• 9. Karabulut M. A., Shah A. F. M. S. and Ilhan H., Performance Optimization by Using Artificial Neural Network Algorithms in VANETs, 42nd International Conference on Telecommunications and Signal Processing (TSP), 2019. Budapest, Hungary: p. 633-636.
• 10. Belbachir A., J. Escareno, E. Rubio, and H. Sossa, Preliminary results on UAV-based forest fire localization based on decisional navigation, in Proceedings of the Workshop on Research, Education and Development of Unmanned Aerial Systems, 2015. p. 377–382.
• 11. Zhao Z. and T. Braun, Topology Control and Mobility Strategy for UAV Ad-hoc Networks: A Survey, in Proceedings of the ERCIM eMobility and MobiSense Workshop, 2013. p. 1-6.
• 12. Gu W., Valavanis K.P., Rutherford, M.J. et al. UAV Model-based Flight Control with Artificial Neural Networks: A Survey. J Intell Robot Syst, 2020. 100: p. 1469–1491.
• 13. Sanna G., S. Godio and G. Guglieri, Neural Network Based Algorithm for Multi-UAV Coverage Path Planning, International Conference on Unmanned Aircraft Systems (ICUAS), 2021. p. 1210-1217.
• 14. Liu H., K. Fan and B. He, Acoustic Source Localization for Anti-UAV Based on Machine Learning in Wireless Sensor Networks, 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2020. p. 1142-1147.
• 15. Bahramnejad S., Movahhedinia N. A reliability estimation framework for cognitive radio V2V communications and an ANN-based model for automating estimations, Computing, 2022. 104: p. 1923–1947. 16. Benrhaiem W, Senhaji HA. Bayesian networks-based reliable broadcast in vehicular networks, Veh Commun, 2020. 21: p. 1–13.
• 17. Hassija V. et al., Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey, in IEEE Communications Surveys & Tutorials, 2021. 23(4): p. 2802-2832.
• 18. Faisal SM, Zaidi T. Implementation of ACO in VANETs with detection of faulty node, Indian J Sci Technol 2021. 14(19): p. 1598–1614.
• 19. G. Sun, D. Qin, T. Lan and L. Ma, Research on Clustering Routing Protocol Based on Improved PSO in FANET, in IEEE Sensors Journal, 2021. 21(23): p. 27168-27185.
• 20. Bhandari S., X. Wang and R. Lee, Mobility and Location-Aware Stable Clustering Scheme for UAV Networks, in IEEE Access, 2020. 8: p. 106364-106372.
• 21. Ghaleb FA, Zainal A, Rassam MA, Mohammed F. An effective misbehavior detection model using artificial neural network for vehicular ad hoc network applications, IEEE Conference on Application, Information and Network Security, 2017. p. 13–18.
• 22. Bagherlou H, Ghaffari A. A routing protocol for vehicular ad hoc networks using simulated annealing algorithm and neural networks, J Supercomput, 2018. 74: p. 2528–2552.
• 23. Jindal A, Aujla GS, Kumar N, Chaudhary R, Obaidat MS, You I. SeDaTiVe: SDN-enabled deep learning architecture for network traffic control in vehicular cyber-physical systems, IEEE Netw, 2018. 32(6): p. 66–73.
• 24. Gismalla M. S. M. et al., Survey on Device to Device (D2D) Communication for 5GB/6G Networks: Concept, Applications, Challenges, and Future Directions, in IEEE Access, 2022. 10: pp. 30792-30821.
• 25. Karabulut M A, Shahen S A F M, Ilhan H. Performance optimization by using artificial neural network algorithms in VANETs, International Conference on Telecommunications and Signal Processing, 2019. p. 633–636.
• 26. Challita, U. Ferdowsi, A. Chen, M. Saad, W. Machine learning for wireless connectivity and security of cellular-connected UAVs, IEEE Wirel. Commun. 2019, 26, 28–35.
• 27. Ren M., J. Li, Song L., Li H. and Xu T., MLP-Based Efficient Stitching Method for UAV Images, in IEEE Geoscience and Remote Sensing Letters, 2022. 19: p. 1-5.
• 28. Braga J. R. G., Velho H. F. C., Conte G., P. Doherty and É. H. Shiguemori. An image matching system for autonomous UAV navigation based on neural network, 14th International Conference on Control, Automation, Robotics and Vision (ICARCV), 2016. p. 1-6.
• 29. Shan L., Miura R., Kagawa T., Ono F., H. -B. Li and Kojima F., Machine Learning-Based Field Data Analysis and Modeling for Drone Communications, in IEEE Access, 2019. 7: p. 79127-79135.
• 30. Munaye Y.Y., Lin H-P., Adege A.B., Tarekegn G.B. UAV Positioning for Throughput Maximization Using Deep Learning Approaches, Sensors. 2019; 19(12): 2775.
• 31. Wang Y. Robot algorithm based on neural network and intelligent predictive control, Amb Intel Hum Comp, 2020. 11: p. 6155–6166.
• 32. Taud H., Mas J. Multilayer Perceptron (MLP). In: Camacho Olmedo, M., Paegelow, M., Mas, JF., Escobar, F. (eds) Geomatic Approaches for Modeling Land Change Scenarios. Lecture Notes in Geoinformation and Cartography. Springer, Cham. 2018.