research-article

Delay aware scheduling in UAV‐enabled OFDMA mobile edge computing system

Authors:

Tingting YangAuthors Info & Claims

IET Communications, Volume 14, Issue 18

Pages 3203 - 3211

https://doi.org/10.1049/iet-com.2020.0274

Published: 29 October 2020 Publication History

Abstract

In infrastructure‐less scenarios such as rural environments, wild emergency response, military applications and disaster relief, unmanned aerial vehicles (UAVs) are capable of providing enhanced mobile edge computing (MEC) services for ground users. Although small latency is the most important advantage of MEC system, how to provide delay aware scheduling in UAV‐enabled MEC system still remains unsolved. In this study, the authors investigate the delay aware scheduling problem in UAV‐enabled orthogonal frequency division multiple access (OFDMA) MEC system and formulate two non‐convex optimisation problems. Moreover, they consider uplink and downlink architecture with characteristics in different UAV‐ground links and traffic load. Furthermore, they propose two novel multi‐stages resource allocation algorithms, i.e. the JSPA‐T and JSPA‐F algorithms with respect to downlink transmit power allocation and sub‐carrier assignment. The mathematical frameworks with duality theory based alternative search optimisation and successive approximation method are proposed. The simulation results validate the performance improvement of the proposed solutions as well as the fast converge behaviour and small computational complexity.

10 References

[1]

Zhang Q., Cheng L., Boutaba R.: ‘Cloud computing: state‐of‐theart and research challenges’, J. Internet Services Appl., 2010, 1, (1), pp. 7–18

[2]

ETSI:‘Mobile‐edge computing introductory technical white paper’, White Paper, Mobile‐edge Computing Industry Initiative. [Online]. Available at https://portal.etsi.org/portals/0/tbpages/mec/docs/mobile‐edgecomputing‐introductorytechnicalwhitepaperv1

[3]

Zhang J., Zhou L., Tang Q. et al.: ‘Stochastic computation offloading and trajectory scheduling for UAV‐assisted mobile edge computing’, IEEE Internet Things J., 2018, 6, (2), pp. 3688–3699

[4]

Wu Q., Zeng Y., Zhang R.: ‘Joint trajectory and communication design for multi‐UAV enabled wireless networks’, IEEE Trans. Wirel. Commun., 2018, 17, (3), pp. 2109–2121

[5]

Mao Y., You C., Zhang J. et al.: ‘A survey on mobile edge computing: the communication perspective’, IEEE Commun. Surv. Tutorials, 2017, 19, (4), pp. 2322–2358

[6]

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., 2017, 67, (3), pp. 2049–2063

[7]

Gupta L., Jain R., Vaszkun G.: ‘Survey of important issues in UAV communication networks’, IEEE Commun. Surv. Tutorial, 2015, 18, (2), pp. 1123–1152

[8]

Wu Q., Zhang R.: ‘Common throughput maximization in UAV‐enabled OFDMA systems with delay consideration’, IEEE Trans. Commun., 2018, 66, (12), pp. 6614–6627

[9]

Duan R., Wang J., Jiang C. et al.: ‘The transmit‐energy vs computation‐delay trade‐off in gateway‐selection for heterogenous cloud aided multi‐UAV systems’, IEEE Trans. Commun., 2018, 67, (4), pp. 3026–3039

[10]

Zhang X., Duan L.: ‘Fast deployment of UAV networks for optimal wireless coverage’, IEEE Trans. Mob. Comput., 2018, 18, (3), pp. 588–601

[11]

Yuan X., Feng Z., Xu W. et al.: ‘Capacity analysis of UAV communications: cases of random trajectories’, IEEE Trans. Veh. Technol., 2018, 67, (8), pp. 7564–7576

[12]

Li J., Han Y.: ‘A traffic service scheme for delay minimization in multi‐layer uav networks’, IEEE Trans. Veh. Technol., 2018, 67, (6), pp. 5500–5504

[13]

Rosati S., Kruzelecki K., Heitz G. et al.: ‘Dynamic routing for flying ad hoc networks’, IEEE Trans. Veh. Technol., 2015, 65, (3), pp. 1690–1700

[14]

Bujari A., Palazzi C.E., Ronzani D.: ‘A comparison of stateless position‐based packet routing algorithms for FANETs’, IEEE Trans. Mob. Comput., 2018, 17, (11), pp. 2468–2482

[15]

Aadil F., Raza A., Khan M. et al.: ‘Energy aware cluster‐based routing in flying ad‐hoc networks’, Sensors, 2018, 18, (5), p. 1413

[16]

Hu L., Hu F., Kumar S.: ‘Moth and ant inspired routing in hierarchical airborne networks with multi‐beam antennas’, IEEE Trans. Mob. Comput., 2018, 18, (4), pp. 910–922

[17]

Sbeiti M., Goddemeier N., Behnke D. et al.: ‘Paser: secure and efficient routing approach for airborne mesh networks’, IEEE Trans. Wirel. Commun., 2015, 15, (3), pp. 1950–1964

[18]

Li K., Ni W., Wang X. et al.: ‘Energy‐efficient cooperative relaying for unmanned aerial vehicles’, IEEE Trans. Mob. Comput., 2015, 15, (6), pp. 1377–1386

[19]

‘Secretive DOD office reveals swarming Perdix UAVs’. Available at https://www.flightglobal.com/news/articles/secretive‐dod‐office‐reveals‐swarming‐perdix‐uavs‐432993/, accessed 10 January 2017

[20]

Xu J., Zeng Y., Zhang R.: ‘UAV‐enabled wireless power transfer: trajectory design and energy optimization’, IEEE Trans. Wirel. Commun., 2018, 17, (8), pp. 5092–5106

[21]

Ren J., Yu G., Cai Y. et al.: ‘Latency optimization for resource allocation in mobile‐edge computation offloading’, IEEE Trans. Wirel. Commun., 2018, 17, (8), pp. 5506–5519

[22]

Ding Z., Xu J., Dobre O.A. et al.: ‘Joint power and time allocation for NOMA‐MEC offloading’, IEEE Trans. Veh. Technol., 2019, 68, (6), pp. 6207–6211

[23]

Lyu X., Tian H., Zhang P. et al.: ‘Multiuser joint task offloading and resource optimization in proximate clouds’, IEEE Trans. Veh. Technol., 2017, 66, (4), pp. 3435–3447

[24]

Hu Q., Cai Y., Yu G. et al.: ‘Joint offloading and trajectory design for UAV‐enabled mobile edge computing systems’, IEEE Internet Things J., 2019, 6, (2), pp. 1879–1892

[25]

Zhou F., Wu Y., Hu R.Q. et al.: ‘Computation rate maximization in UAV‐enabled wireless powered mobile‐edge computing systems’, IEEE J. Sel. Areas Commun., 2018, 36, (9), pp. 1927–1941

[26]

Sun H., Zhou F., Hu R.Q.: ‘Joint offloading and computation energy efficiency maximization in a mobile edge computing system’, IEEE Trans. Veh. Technol., 2019, 68, (3), pp. 3052–3056

[27]

You C., Huang K., Chae H. et al.: ‘Energy‐efficient resource allocation for mobile‐edge computation offloading’, IEEE Trans. Wirel. Commun., 2016, 16, (3), pp. 1397–1411

[28]

Chen X., Jiao L., Li W. et al.: ‘Efficient multi‐user computation offloading for mobile‐edge cloud computing’, IEEE Trans. Netw., 2016, 24, pp. 2795–2808

[29]

Guo S., Xiao B., Yang Y. et al.: ‘Energy‐efficient dynamic offloading and resource scheduling in mobile cloud computing’. Proc. Int. IEEE Int. Conf. Computer and Communications(INFOCOM), San Francisco, CA, USA, 2016, pp. 1–9

[30]

Sheng Z., Mahapatra C., Leung V. et al.: ‘Energy efficient cooperative computing in mobile wireless sensor networks’, IEEE Trans. Cloud Comput., 2015, 6, (1), pp. 114–126

[31]

Du Y., Yang K., Wang K. et al.: ‘Joint resources and workflow scheduling in UAV‐enabled wirelessly‐powered MEC for IoT systems’, IEEE Trans. Veh. Technol., 2019, 68, (10), pp. 10187–10200

[32]

Huang S., Li L., Pan Q. et al.: ‘Fine‐grained task offloading for UAV via MEC‐enabled networks’. IEEE 30th Int. Symp. on Personal, Indoor and Mobile Radio Communications (PIMRC Workshops), Istanbul, Turkey, 2019, pp. 1–6

[33]

Yang Z., Pan C., Wang K. et al.: ‘Energy efficient resource allocation in UAV‐enabled mobile edge computing networks’, IEEE Trans. Wirel. Commun., 2019, 18, (9), pp. 4576–4589

[34]

Zhang X., Zhong Y., Liu P. et al.: ‘Resource allocation for a UAV‐enabled mobile‐edge computing system: computation efficiency maximization’, IEEE Access, 2019, 7, pp. 113345–113354

[35]

Ferrer A.J., Marquĺĺs J.M., Jorba J.: ‘Towards the decentralised cloud: survey on approaches and challenges for mobile, ad hoc, and edge computing’, ACM Comput. Surv., 2019, 51, (6), pp. 1–36

[36]

Hu J., Jiang M., Zhang Q. et al.: ‘Joint optimization of UAV position, time slot allocation, and computation task partition in multiuser aerial mobile‐edge computing systems’, IEEE Trans. Veh. Technol., 2019, 68, pp. 7231–7235

[37]

Choi J.P., Chan V.W.S.: ‘Optimum power and beam allocation based on traffic demands and channel conditions over satellite downlinks’, IEEE Trans. Wirel. Commun., 2005, 4, (6), pp. 2983–2993

[38]

Ding G., Wu Q., Wang J.: ‘Sensing confidence levelbased joint spectrum and power allocation in cognitive radio networks’, Wirel. Pers. Commun., 2013, 72, (1), pp. 283–298

[39]

Yu W., Raymond L.: ‘Dual methods for nonconvex spectrum optimization of multicarrier systems’, IEEE Trans. Commun., 2006, 54, (7), pp. 1310–1322

[40]

Cormen T.H., Leiserson C.E., Rivest R.L.: ‘Introduction to algorithms’ (MIT Press, Cambridge, MA, United States, 2009, 3rd edn.)

Cited By

Wang HCui FNi MZhou T(2024)Key technologies of end-side computing power network based on multi-granularity and multi-level end-side computing power schedulingJournal of Computational Methods in Sciences and Engineering10.3233/JCM-24732424:2(1157-1171)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/JCM-247324
Du RWang JGao Y(2024)Computing offloading and resource scheduling based on DDPG in ultra-dense edge computing networksThe Journal of Supercomputing10.1007/s11227-023-05816-w80:8(10275-10300)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s11227-023-05816-w

Recommendations

Mobile cloud computing with a UAV‐mounted cloudlet: optimal bit allocation for communication and computation

Mobile cloud computing relieves the tension between computation‐intensive mobile applications and battery‐constrained mobile devices by enabling the offloading of computing tasks from mobiles to a remote processors. This study considers a mobile cloud ...
Downlink resource allocation in OFDMA wireless networks under power amplifier non‐linearity

In this study, the physical layer resource allocation in orthogonal frequency‐division multiple access (OFDMA) systems is studied by considering the non‐linear effects of power amplifier (PA). The non‐linearity produces cross‐correlation among the ...
Joint optimisation of UAV grouping and energy consumption in MEC‐enabled UAV communication networks

This study presents a mobile edge computing (MEC)‐enabled UAV communication system, where a number of UAVs are served by terrestrial base stations (TBSs) equipped with computation resource in the non‐orthogonal multiple access manner. Each UAV has to ...

Comments

Information & Contributors

Information

Published In

cover image IET Communications

IET Communications Volume 14, Issue 18

November 2020

216 pages

EISSN:1751-8636

DOI:10.1049/cmu2.v14.18

Issue’s Table of Contents

© 2021 The Institution of Engineering and Technology.

Publisher

John Wiley & Sons, Inc.

United States

Publication History

Published: 29 October 2020

Author Tags

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 04 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wang HCui FNi MZhou T(2024)Key technologies of end-side computing power network based on multi-granularity and multi-level end-side computing power schedulingJournal of Computational Methods in Sciences and Engineering10.3233/JCM-24732424:2(1157-1171)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/JCM-247324
Du RWang JGao Y(2024)Computing offloading and resource scheduling based on DDPG in ultra-dense edge computing networksThe Journal of Supercomputing10.1007/s11227-023-05816-w80:8(10275-10300)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s11227-023-05816-w

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

Media

Figures

Other

Tables

View Issue’s Table of Contents