The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization
Open accessAbstract
In this paper, we study unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) with the objective to optimize computation offloading with minimum UAV energy consumption. In the considered scenario, a UAV plays the role of an aerial cloudlet to collect and process the computation tasks offloaded by ground users. Given the service requirements of users, we aim to maximize UAV energy efficiency by jointly optimizing the UAV trajectory, the user transmit power, and computation load allocation. The resulting optimization problem corresponds to nonconvex fractional programming, and the Dinkelbach algorithm and the successive convex approximation (SCA) technique are adopted to solve it. Furthermore, we decompose the problem into multiple subproblems for distributed and parallel problem solving. To cope with the case when the knowledge of user mobility is limited, we adopt a spatial distribution estimation technique to predict the location of ground users so that the proposed approach can still be applied. Simulation results demonstrate the effectiveness of the proposed approach for maximizing the energy efficiency of UAV.
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Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000016 · Cited by 17,075
A Survey on Mobile Edge Computing: The Communication Perspective
Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2745201 · Cited by 5,607
On Nonlinear Fractional Programming
Dinkelbach, W. (1967) · Management Science · doi:10.1287/mnsc.13.7.492 · Cited by 3,139
Energy-Efficient UAV Communication With Trajectory Optimization
Zeng, Y., & Zhang, R. (2017) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2017.2688328 · Cited by 2,164
Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks
Wu, Q., Zeng, Y., & Zhang, R. (2018) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2017.2789293 · Cited by 1,978
Dynamic computation offloading for mobile-edge computing with energy harvesting devices
Mao, Y., Zhang, J., & Letaief, K. B. (2016) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2016.2611964 · Cited by 1,705
Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing
Li, H., Ota, K., & Dong, M. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700202 · Cited by 1,566
Mobile-Edge Computing: Partial Computation Offloading Using Dynamic Voltage Scaling
Wang, Y., Sheng, M., Wang, X., Wang, L., & Li, J. (2016) · IEEE Transactions on Communications · doi:10.1109/tcomm.2016.2599530 · Cited by 1,002
Self-citationJoint Offloading and Computing Optimization in Wireless Powered Mobile-Edge Computing Systems
Wang, F., Xu, J., Wang, X., & Cui, S. (2018) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2017.2785305 · Cited by 922
Space/Aerial-Assisted Computing Offloading for IoT Applications: A Learning-Based Approach
Cheng, X., Lyu, F., Quan, W., Zhou, C., He, H., Shi, W., & Shen, X. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2906789 · Cited by 867
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Cited by
Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey
Azari, M. M., Solanki, S., Chatzinotas, S., Kodheli, O., Sallouha, H., Colpaert, A., Mendoza Montoya, J. F., Pollin, S., Haqiqatnejad, A., Mostaani, A., et al. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3199901 · Cited by 668
Resource Scheduling in Edge Computing: A Survey
Luo, Q., Hu, S., Li, C., Li, G., & Shi, W. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3106401 · Cited by 588
“, things), such as UAVs [40], CAVs [16], AR equipment [41], surveillance cameras for smart city [42], sensors for smart health [43],”
BackgroundMethodInfluentialDistributed resource scheduling in edge computing: Problems, solutions, and opportunities
Sahni, Y., Cao, J., Yang, L., & Wang, S. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109430 · Cited by 38
Metadata
- Authors
- Li, M., Cheng, N., Gao, J., Wang, Y., Zhao, L., & Shen, X.
- Venue
- IEEE Transactions on Vehicular Technology
- Published
- 2020-03
- Volume
- 69
- Issue
- 3
- Pages
- 3424-3438
- ISSN
- 0018-9545 (ISSN-L)
- 1939-9359
- OpenAlex
- W3002033136
- Cited by
- 508
- References
- 34
- FWCI
- 43.40
- Type
- journal article
- Language
- en
- Open access
- green
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- Li, M., Cheng, N., Gao, J., Wang, Y., Zhao, L., & Shen, X. (2020). Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/tvt.2020.2968343
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