Abstract
With the popularization of mobile wireless networks and Internet of Things (IoT) technologies, energy-hungry and delay-intensive applications continue to surge. Due to the limited computing power and battery capacity, mobile terminals rarely satisfy the increasing demands of application services. Mobile Edge Computing (MEC) deploys communication and computing resources near the network edge closing to the user side, which effectively reduces devices’ energy consumption and enhances system performance. However, the application of MEC needs infrastructures that can deploy edge services, and is limited by the geographical environment. UAV-assisted MEC has better flexibility and communication Line-of-Sight (LoS), which expands service scope while improving the versatility of MEC. Meanwhile, the dynamic task arrival rate, channel condition, and environmental factors pose challenges for task offloading and resources allocation strategy. In this paper, we jointly optimize UAV deployment, frequency scaling, and task scheduling to minimize energy consumption for devices while ensuring system stability in the long term. Due to the dynamic and randomness of task arrival rate and wireless channel, the original problem is defined as a stochastic optimization problem. The Drone Placement and Online Task oFFloading (DPOTFF) algorithm is designed to decouple the original problem into several sub-problems and solve them within a limited time complexity. It is also proved theoretically that the DPOTFF can obtain close-to-optimal energy consumption while ensuring system stability. The effectiveness and reliability of the algorithm are also verified by simulation and comparative experiments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Jeong, S., Simeone, O., Kang, J.: Mobile edge computing via a UAV-mounted cloudlet: optimization of bit allocation and path planning. IEEE Trans. Veh. Technol. 67(3), 2049–2063 (2018). https://doi.org/10.1109/TVT.2017.2706308
Hu, Q., Cai, Y., Yu, G., Qin, Z., Zhao, M., Li, G.Y.: Joint offloading and trajectory design for UAV-enabled mobile edge computing systems. IEEE Internet Things J. 6(2), 1879–1892 (2019). https://doi.org/10.1109/JIOT.2018.2878876
Yang, J., Yang, Q., Kwak, K.S., Rao, R.R.: Power-delay tradeoff in wireless powered communication networks. IEEE Trans. Veh. Technol. 66(4), 3280–3292 (2017). https://doi.org/10.1109/TVT.2016.2587101
Messous, M.-A., Sedjelmaci, H., Houari, N., Senouci, S.-M.: Computation offloading game for an UAV network in mobile edge computing. In: IEEE International Conference on Communications (ICC), vol. 2017, pp. 1–6 (2017). https://doi.org/10.1109/ICC.2017.7996483
Dinh, T.Q., Tang, J., La, Q.D., Quek, T.Q.S.: Offloading in mobile edge computing: task allocation and computational frequency scaling. IEEE Trans. Commun. 65(8), 3571–3584 (2017). https://doi.org/10.1109/TCOMM.2017.2699660
Sun, Y., Zhou, S., Xu, J.: EMM: energy-aware mobility management for mobile edge computing in ultra dense networks. IEEE J. Sel. Areas Commun. 35(11), 2637–2646 (2017). https://doi.org/10.1109/JSAC.2017.2760160
Jiang, Z., Mao, S.: Energy delay tradeoff in cloud offloading for multi-core mobile devices. IEEE Access 3, 2306–2316 (2015). https://doi.org/10.1109/ACCESS.2015.2499300
Mao, Y., Zhang, J., Letaief, K.B.: Joint task offloading scheduling and transmit power allocation for mobile-edge computing systems. In: IEEE Wireless Communications and Networking Conference (WCNC), vol. 2017, pp. 1–6 (2017). https://doi.org/10.1109/WCNC.2017.7925615
Liu, C., Bennis, M., Poor, H.V.: Latency and reliability-aware task offloading and resource allocation for mobile edge computing. In: IEEE Globecom Workshops (GC Wkshps), vol. 2017, pp. 1–7 (2017). https://doi.org/10.1109/GLOCOMW.2017.8269175
Little, J.D.C., Graves, S.C.: Little’s law. In: Chhajed, D., Lowe, T.J. (eds.) Building Intuition. International Series in Operations Research & Management Science, vol. 115 (2008). Springer, Boston. https://doi.org/10.1007/978-0-387-73699-0_5
Neely, M.: Stochastic Network Optimization with Application to Communication and Queueing Systems, Morgan & Claypool (2010)
Wu, D., Sun, X., Ansari, N.: An FSO-based drone assisted mobile access network for emergency communications. IEEE Trans. Netw. Sci. Eng. 7(3), 1597–1606 (2020). https://doi.org/10.1109/TNSE.2019.2942266
You, C., Huang, K., Chae, H., Kim, B.: Energy-efficient resource allocation for mobile-edge computation offloading. IEEE Trans. Wirel. Commun. 16(3), 1397–1411 (2017). https://doi.org/10.1109/TWC.2016.2633522
Shi, W., et al.: Multiple drone-cell deployment analyses and optimization in drone assisted radio access networks. IEEE Access 6, 12518–12529 (2018). https://doi.org/10.1109/ACCESS.2018.2803788
Zhou, F., Wu, Y., Sun, H., Chu, Z.: UAV-enabled mobile edge computing: offloading optimization and trajectory design. In: IEEE International Conference on Communications (ICC), vol. 2018, pp. 1–6 (2018). https://doi.org/10.1109/ICC.2018.8422277
Mao, Y., Zhang, J., Letaief, K.B.: Dynamic computation offloading for mobile-edge computing with energy harvesting devices. IEEE J. Sel. Areas Commun. 34(12), 3590–3605 (2016). https://doi.org/10.1109/JSAC.2016.2611964
Bi, S., Zhang, Y.J.: Computation rate maximization for wireless powered mobile-edge computing with binary computation offloading. IEEE Trans. Wirel. Commun. 17(6), 4177–4190 (2018). https://doi.org/10.1109/TWC.2018.2821664
Wang, T., et al.: Mobile edge-enabled trust evaluation for the Internet of Things. Inf. Fusion 75, 90–100 (2021)
Wang, T., Wang, P., Cai, S., Ma, Y., Liu, A., Xie, M.: A unified trustworthy environment establishment based on edge computing in industrial IoT. IEEE Trans. Ind. Inf. 16(9), 6083–6091 (2020). https://doi.org/10.1109/TII.2019.2955152
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Lu, Y., Chen, X., Zhao, F., Chen, Y. (2022). Energy Efficient Deployment and Task Offloading for UAV-Assisted Mobile Edge Computing. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13156. Springer, Cham. https://doi.org/10.1007/978-3-030-95388-1_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-95388-1_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-95387-4
Online ISBN: 978-3-030-95388-1
eBook Packages: Computer ScienceComputer Science (R0)