Mobile QR Code QR CODE

REFERENCES

1 
M. Żarski, B. Wójcik, K. Książek, and J. A. Miszczak, “Finicky transfer learning—A method of pruning convolutional neural networks for cracks classification on edge devices,” Computer‐Aided Civil and Infrastructure Engineering, Vol. 37, No. 4, pp. 500-515. 2022.DOI
2 
Z.T. Njitacke, et al., “Hamiltonian energy computation and complex behavior of a small heterogeneous network of three neurons: circuit implementation,” Nonlinear Dynamics, Vol. 107, No. 3, pp. 2867-2886. 2022.DOI
3 
L. Chen, et al., “Intelligent ubiquitous computing for future UAV-enabled MEC network systems,” Cluster Computing, Vol. 25, No. 4, pp. 2417-2427. 2022.DOI
4 
Y. Chen, et al., “Dynamic task offloading for mobile edge computing with hybrid energy supply,” Tsinghua Science and Technology, Vol. 28, No. 3, pp. 421-432. 2022.DOI
5 
M, Kamel, et al., “Distributed Address Table (DAT): A decentralized model for end-to-end communication in IoT,” Peer-to-Peer Networking and Applications, Vol. 15, No. 1, pp. 178-193. 2022.DOI
6 
E. Mustafa, et al., “Joint wireless power transfer and task offloading in mobile edge computing: a survey,” Cluster Computing, Vol. 25, No. 4, pp. 2429-2448. 2022.DOI
7 
T. Do-Duy, et al., “Digital Twin-Aided Intelligent Offloading with Edge Selection in Mobile Edge Computing,” IEEE Wireless Communications Letters, Vol. 11, No. 4, pp. 806-810. 2022.DOI
8 
M. Jiang, et al., “A multi-intersection vehicular cooperative control based on end-edge-cloud computing,” IEEE Transactions on Vehicular Technology, Vol. 71, No. 3, pp. 2459-2471. 2022.DOI
9 
L. U. Khan, et al., “Digital-twin-enabled 6G: Vision, architectural trends, and future directions. IEEE Communications Magazine, Vol. 60, No. 1, pp. 74-80. 2022.DOI
10 
M. K. Hasan, et al., “A review on security threats, vulnerabilities, and counter measures of 5G enabled Internet-of-Medical-Things,” IET Communications, Vol. 16, No. 5, pp. 421-432. 2022.DOI
11 
L. Wang, Y. Wang, “Supply chain financial service management system based on block chain IoT data sharing and edge computing,” Alexandria Engineering Journal, Vol. 61, No. 1, pp. 147-158. 2022.DOI
12 
F. Alqahtani, M. Al-Maitah, and O. Elshakankiry, “A proactive caching and offloading technique using machine learning for mobile edge computing users,” Computer Communications, Vol. 181, pp. 224-235. 2022.DOI
13 
R. Rajavel, et al., “IoT-based smart healthcare video surveillance system using edge computing,” Journal of Ambient Intelligence and Humanized Computing, Vol. 13, No. 6, pp. 3195-3207. 2022.DOI
14 
J. Talusan, et al., “Route Planning Through Distributed Computing by Road Side Units,” IEEE Access, Vol. 8, pp. 176134-176148. 2020.URL
15 
M. Morimoto, et al., Generalization techniques of neural networks for fluid flow estimation,” Neural Computing and Applications, Vol. 34, No. 5, pp. 3647-3669. 2022.DOI
16 
Z. Liu, et al., “Robust Edge Computing in UAV Systems via Scalable Computing and Cooperative Computing,” IEEE Wireless Communications, Vol. 28, No. 5, pp. 36-42. 2021.DOI
17 
M.A. Kumar, and S. Kanthalakshmi, “H∞Control law for line of sight stabilization in two-axis gimbal system,” Journal of Vibration and Control, Vol. 28, No. 1-2, pp. 182-191. 2022.DOI
18 
S. Rezvani, et al., “Optimal Power Allocation in Downlink Multicarrier NOMA Systems: Theory and Fast Algorithms,” IEEE Journal on Selected Areas in Communications, Vol. 40, No. 4, pp. 1162-1189. 2022.DOI