REFERENCES
1. Alsamhi SH, Almalki FA, Afghah F, et al. Drones' edge intelligence over smart environments in B5G: blockchain and federated learning synergy. IEEE Trans on Green Commun Netw 2022;6:295-312.
2. Saif A, Dimyati K, Noordin KA, et al. UAV and relay cooperation based on RSS for extending smart environments coverage area in B5G. Res Square 2022; doi: 10.21203/rs.3.rs-2002265/v1.
3. Alsamhi SH, Almalki FA, AL-Dois H, et al. Multi-drone Edge Intelligence and SAR smart wearable devices for emergency communication. Wirel Commun Mob Comput 2021;2021:1-12.
4. Alsamhi SH, Shvetsov AV, Kumar S, et al. UAV computing-assisted search and rescue mission framework for disaster and harsh environment mitigation. Drones 2022;6:154.
5. Alsamhi SH, Shvetsov AV, Shvetsova SV, et al. Blockchain-empowered security and energy efficiency of drone swarm consensus for environment exploration. IEEE Trans on Green Commun Netw 2022:1.
6. Alsamhi SH, Shvetsov AV, Kumar S, et al. Computing in the sky: a survey on intelligent ubiquitous computing for UAV-assisted 6G Networks and industry 4.0/5.0. Drones 2022;6:177.
7. Gopi SP, Magarini M, Alsamhi SH, Shvetsov AV. Machine learning-assisted adaptive modulation for optimized drone-user communication in B5G. Drones 2021;5:128.
8. Saif A, Dimyati K, Noordin KA, et al. Energy-efficient tethered UAV deployment in B5G for smart environments and disaster recovery. In: 2021 1st International Conference on Emerging Smart Technologies and Applications (eSmarTA). IEEE; 2021. pp. 1–5.
9. Heuser R. The data says: mobile traffic by day and time; 2015. Accessed: 2022-09-30. Available from: https://www.seoclarity.net/blog/mobile-seo-by-day-and-time-11890/. [Last accessed on 31 Dec 2022].
10. KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. New York, NY, USA: Association for Computing Machinery; 2011.
11. Xin L, Wang P, Chan CY, et al. Intention-aware long horizon trajectory prediction of surrounding vehicles using dual LSTM networks. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC). Maui, Hawaii, USA; 2018. pp. 1441–46.
12. Yu Y, Si X, Hu C, Zhang J. A review of recurrent neural networks: LSTM cells and network architectures. Neural Computation 2019;31:1235-70.
13. Bertsimas D, Tsitsiklis JN. Introduction to linear optimization. vol. 6. Athena Scientific Belmont, MA; 1997. Available from: https://linux.ime.usp.br/~dfrever/programs/Documents/INTRODUCTION%20TO%20LINEAR%20OPTIMIZATION(errata).pdf. [Last accessed on 31 Dec 2022].
14. Pirnay H, López-Negrete R, Biegler LT. Optimal sensitivity based on IPOPT. Math Prog Comp 2012;4:307-31.
15. Jo HS, Sang YJ, Xia P, Andrews JG. Heterogeneous cellular networks with flexible cell association: A comprehensive downlink SINR analysis. IEEE Trans Wireless Commun 2012;11:3484-95.
16. Kato N, Fadlullah Z, Tang F, et al. Optimizing space-air-ground integrated networks by artificial intelligence. IEEE Wireless Commun 2019;26:140-7.
17. Wang Y, Xu Y, Zhang Y, Zhang P. Hybrid satellite-aerial-terrestrial networks in emergency scenarios: a survey. China Commun 2017;14:1-13.
18. Zola A, Lewis S. What is a small cell? - definition from techtarget. com. TechTarget; 2022. Accessed: 2022-09-30. Available from: https://www.techtarget.com/searchnetworking/definition/small-cell. [Last accessed on 31 Dec 2022].
19. Chang Z, Guo W, Guo X, Ristaniemi T. Machine learning-based resource allocation for multi-UAV communications system. In: 2020 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE; 2020. pp. 1–6.
20. Sun Y, Xu D, Ng DWK, Dai L, Schober R. Optimal 3D-trajectory design and resource allocation for solar-powered UAV communication systems. IEEE Trans Commun 2019;67:4281-98.
21. Sun Y, Ng DWK, Xu D, Dai L, Schober R. Resource allocation for solar powered UAV communication systems. In: 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE; 2018. pp. 1–5.
22. Qiu H, Zheng Q, Msahli M, et al. Topological Graph Convolutional Network-Based Urban Traffic Flow and Density Prediction. IEEE Trans Intell Transport Syst 2021;22:4560-9.
23. Yang P, Cao X, Yin C, et al. Proactive Drone-Cell Deployment: Overload Relief for a Cellular Network Under Flash Crowd Traffic. IEEE Trans Intell Transport Syst 2017;18:2877-92.
24. Liu B, Sheng Y, Shao X, Ji Y. A Grid and Vehicle Density Prediction-Based Communication Scheme in Large-scale Urban Environments. In: 2021 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM); 2021. pp. 22–25.
25. Jabbarpour MR, Malakooti H, Taheri M, Noor RM. The comparative analysis of velocity and density in VANET using prediction-based intelligent routing algorithms. In: Second International Conference on Future Generation Communication Technologies (FGCT 2013);2013. pp. 54–58.
26. Liu C, Feng W, Wang J, Chen Y, Ge N. Aerial Small Cells Using Coordinated Multiple UAVs: An Energy Efficiency Optimization Perspective. IEEE Access 2019;7:122838-48.
27. Zhang G, Yan H, Zeng Y, Cui M, Liu Y. Trajectory Optimization and Power Allocation for Multi-Hop UAV Relaying Communications. IEEE Access 2018;6:48566-76.
28. Hua M, Yang L, Pan C, Nallanathan A. Throughput Maximization for Full-Duplex UAV Aided Small Cell Wireless Systems. IEEE Wireless Commun Lett 2020;9:475-9.
29. Pulp. Accessed: 2022-09-30. Available from: https://pypi.org/project/PuLP/. [Last accessed on 31 Dec 2022].