journal article·2018-10-17Open access

Context-Aware Caching Distribution and UAV Deployment: A Game-Theoretic Approach

Tao Fang, Hua Tian, Xiaobo Zhang, Xueqiang Chen, Xinhong Shao, Yuli Zhang

Applied Sciences· Volume 8· Issue 10

Abstract

This paper investigates the problem of the optimal arrangement for both unmanned aerial vehicles’ (UAVs’) caching contents and service locations in UAV-assisted networks based on the context awareness, which considers the influence between users and environment. In the existing work, users within the coverage of UAVs are considered to be served perfectly, which ignores the communication probability caused by line-of-sight (LOS) and non-line-of-sight (NLOS) links. However, the links are related to the deployment of UAVs. Moreover, the transmission overhead should be taken into account. To balance the tradeoff between these two factors, we design the ratio of users’ probability and transmission overhead as the performance measure mechanism to evaluate the performance of UAV-assisted networks. Then, we formulate the objective for maximizing the performance of UAV-assisted networks as a UAV-assisted caching game. It is proved that the game is an exact potential game with the performance of UAV-assisted networks serving as the potential function. Next, we propose a log-linear caching algorithm (LCA) to achieve the Nash equilibrium (NE). Finally, related simulation results reflect the great performance of the proposed algorithm.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: UAV Applications and Optimization · Opportunistic and Delay-Tolerant Networks · Caching and Content Delivery

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

College of Communications Engineering, Army Engineering University of PLA, Nanjing 210000, China

Funding

National Natural Science Foundation of China · Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province

References (33)

Show source-provided references
1. Self-organization as a supporting paradigm for military UAV relay networks10.1109/LCOMM.2016.2524405 ↗
2. Ma’sum, M.A., Arrofi, M.K., Jati, G., Arifin, F., Kurniawan, M.N., Mursanto, P., and Jatmiko, W. (2013, January 28–29). Simulation of intelligent unmanned aerial vehicle (UAV) for military surveillance. Proceedings of the 2013 International Conference on Advanced Computer Science and Information Systems, Bali, Indonesia.10.1109/ICACSIS.2013.6761569 ↗
3. Giese, S., Carr, D., and Chahl, J. (2013, January 23–26). Implications for unmanned systems research of military UAV mishap statistics. Proceedings of the 2013 IEEE Intelligent Vehicles Symposium, Gold Coast, QLD, Australia.10.1109/IVS.2013.6629628 ↗
4. Johnson, C.W. (2008, January 20–22). Military risk assessment in counter insurgency operations: Case study in the retrieval of a UAV, Nr Sangin, Afghanistan, 11 June 2006. Proceedings of the 2008 3rd IET International Conference on System Safety, Birmingham, UK.10.1049/cp:20080725 ↗
5. Chen, Z., Luo, J., Jiang, H., and Shi, W. (2009, January 20–24). Design & application of MIS on UAV quality & reliability for total life span. Proceedings of the 2009 8th International Conference on Reliability, Maintainability and Safety, Chengdu, China.10.1109/ICRMS.2009.5270014 ↗
6. Cecil, J. (2018, January 23–26). A conceptual framework for supporting UAV based cyber physical weather monitoring activities. Proceedings of the 2018 Annual IEEE International Systems Conference, Vancouver, BC, Canada.10.1109/SYSCON.2018.8369588 ↗
7. Zhou, S., Wang, Y., Ye, S., Zhu, G., and Cheng, L. (2011, January 28–29). The intelligent wind detection system of weather UAV based on the multi-mode variable structure. Proceedings of the 2011 Fourth International Conference on Intelligent Computation Technology and Automation, Shenzhen, China.
8. Zhang, W., Yu, H., Yan, Z., and Xu, J. (2017, January 27–29). Study on disaster monitoring technology of mountain fire based on UAV transmission line inspection. Proceedings of the 2017 IEEE International Conference on Unmanned Systems, Beijing, China.10.1109/ICUS.2017.8278377 ↗
9. Yuan, C., Liu, Z., and Zhang, Y. (2015, January 9–12). UAV-based forest fire detection and tracking using image processing techniques. Proceedings of the 2015 International Conference on Unmanned Aircraft Systems, Denver, CO, USA.10.1109/ICUAS.2015.7152345 ↗
10. Ji, X., Hua, Q., Li, C., Tang, J., Wang, A., Chen, X., and Fang, D. (2017, January 16–19). 2-OptACO: An improvement of ant colony optimization for UAV path in disaster rescue. Proceedings of the 2017 International Conference on Networking and Network Applications, Kathmandu, Nepal.10.1109/NaNA.2017.16 ↗
11. Nakadai, K., Kumon, M., Okuno, H.G., Hoshiba, K., Wakabayashi, M., Washizaki, K., Ishiki, T., Gabriel, D., Bando, Y., and Morito, T. (2017, January 24–28). Development of microphone-array-embedded UAV for search and rescue task. Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vancouver, BC, Canada.10.1109/IROS.2017.8206494 ↗
12. Wireless communications with unmanned aerial vehicles: Opportunities and challenges10.1109/MCOM.2016.7470933 ↗
13. Overcoming Endurance Issue: UAV-Enabled Communications with Proactive Caching10.1109/JSAC.2018.2844979 ↗
14. Caching in the sky: Proactive deployment of cache-enabled unmanned aerial vehicles for optimized quality-of-experience10.1109/JSAC.2017.2680898 ↗
15. Fang, T., Tian, H., Yang, Y., Liu, X., Wu, D., and Chen, X. (2017, January 27–30). An influence factor based caching node selection algorithm in D2D networks. Proceedings of the 2017 IEEE 17th International Conference on Communication Technology (ICCT), Chengdu, China.10.1109/ICCT.2017.8359748 ↗
16. A game-theoretic approach for optimal distributed cooperative hybrid caching in D2D networks10.1109/LWC.2017.2776920 ↗
17. Caching UAV assisted secure transmission in hyper-dense networks based on interference alignment10.1109/TCOMM.2018.2792014 ↗
18. Chen, M., Saad, W., and Yin, C. (2017, January 4–8). Liquid state machine learning for resource allocation in a network of cache-enabled LTE-U UAVs. Proceedings of the GLOBECOM 2017—2017 IEEE Global Communications Conference, Singapore.10.1109/GLOCOM.2017.8254746 ↗
19. Fang, T., Tian, H., Yang, Y., Zhang, X., Zhang, Y., and Xu, Y. (2018). Location-aware emergency content delivery in UAV-assisted networks: A game-theoretic approach. IEEE Access, submitted.
20. Cha, M., Kwak, H., Rodriguez, P., Ahn, Y.Y., and Moon, S. (2007, January 24–26). I tube, you tube, everybody tubes: Analyzing the world’s largest user generated content video system. Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, San Diego, CA, USA.10.1145/1298306.1298309 ↗
21. (2013, April 17). UMassTraceRepository. Available online: http://traces.cs.umass.edu/index.php/network/network.
22. Opportunistic spectrum access in cognitive radio networks: Global optimization using local interaction games10.1109/JSTSP.2011.2176916 ↗
23. Optimal caching in 5G networks with opportunistic spectrum access10.1109/TWC.2018.2825351 ↗
24. Optimal LAP altitude for maximum coverage10.1109/LWC.2014.2342736 ↗
25. A one-leader multi-follower bayesian-stackelberg game for anti-jamming transmission in UAV communication networks10.1109/ACCESS.2018.2828033 ↗
26. Jia, L., Xu, Y., Sun, Y., Feng, S., and Anpalagan, A. (2018, May 31). Stackelberg game approaches for anti-jamming defence in wireless networks. Available online: https://arxiv.org/abs/1805.12308.
27. Dynamic spectrum access in time-varying environment: Distributed learning beyond expectation optimization10.1109/TCOMM.2017.2734768 ↗
28. Distributed demand- aware channel-slot selection for multi-UAV networks: A game-theoretic learning approach10.1109/ACCESS.2018.2811372 ↗
29. Liu, D., Xu, Y., Wang, J., Xu, Y., Anpalagan, A., Wu, Q., Wang, H., and Shen, L. (arXiv, 2018). Self-organizing relay selection in UAV communication networks: A matching game perspective, arXiv.10.1109/MWC.001.1700434 ↗
30. Zhang, Y., Xu, Y., Anpalagan, A., Wu, Q., Xu, Y., Sun, Y., Feng, S., and Luo, Y. (2018). Context-aware group buying in ultra-dense small cell networks: Unity is strength. IEEE Wirel. Commun., in press.10.1109/MWC.001.1800012 ↗
31. Potential games10.1006/game.1996.0044 ↗
32. Demand-aware multichannel opportunistic spectrum access: A local interaction game approach With reduced information exchange10.1109/TVT.2014.2369484 ↗
33. Opportunistic spectrum access using partially overlapping channels: Graphical game and uncoupled learning10.1109/TCOMM.2013.072913.120881 ↗