SCIENTIA SINICA Informationis, Volume 50 , Issue 3 : 307-317(2020) https://doi.org/10.1360/SSI-2019-0186

Cooperative communication based on swarm intelligence: vision, model, and key technology

More info
  • ReceivedAug 28, 2019
  • AcceptedNov 18, 2019
  • PublishedFeb 27, 2020


Inspired by natural swarms, unmanned platform swarms have been applied in military reconnaissance, civil mapping, and so on. Compared with a single unmanned platform, unmanned platform swarms exhibit higher environmental suitability and robustness, as well as better capacities for executing missions. Intelligent and reliable swarm communication is particularly critical for the activities of swarms; nonetheless, the scarce communication resources and the complex environment make swarm communications quite challenging. The existing research on communication in unmanned clusters lacks effectiveness, reliability, safety, and systematic consideration of autonomy, cooperativity, and intelligence. Hence, this paper focuses on the communication networks of unmanned aerial vehicle (UAV) swarms, such as ad hoc mesh networks, combines the swarm intelligence theory and the cognitive radio technology, and proposes a cooperative communication model and a cooperative sensing method based on swarm intelligence for UAV swarms. Finally, future developments are presented.

Funded by

科技创新2030 —“新一代人工智能"重大项目(2018AAA0102303)




[1] Vicsek T, Zafeiris A. Collective motion. Phys Rep, 2012, 517: 71-140 CrossRef Google Scholar

[2] Li W, Wu W J, Wang H M. Crowd intelligence in AI 2.0 era. Front Inf Technol Electron Eng, 2017, 18: 15-43 CrossRef Google Scholar

[3] Daniel P. Gremlins. 2014-06-09. http://www.darpa.mil/program/gremlins. Google Scholar

[4] Office of Naval Research. LOCUST: autonomous, swarming UAVs fly into the future. 2015-04-14. http://www.onr.navy.mil/en/Media-Center/Press-Releases/2015/LOCUST-low-cost-UAV-swarm-ONR.aspx. Google Scholar

[5] Hauert S, Winkler L, Zufferey J C. Ant-based swarming with positionless micro air vehicles for communication relay. Swarm Intell, 2008, 2: 167-188 CrossRef Google Scholar

[6] Zhao N, Lu W, Sheng M. UAV-Assisted Emergency Networks in Disasters. IEEE Wireless Commun, 2019, 26: 45-51 CrossRef Google Scholar

[7] Zhao N, Cheng F, Yu F R. Caching UAV Assisted Secure Transmission in Hyper-Dense Networks Based on Interference Alignment. IEEE Trans Commun, 2018, 66: 2281-2294 CrossRef Google Scholar

[8] Gupta L, Jain R, Vaszkun G. Survey of Important Issues in UAV Communication Networks. IEEE Commun Surv Tutorials, 2016, 18: 1123-1152 CrossRef Google Scholar

[9] Bekmezci , Sahingoz O K, Temel . Flying Ad-Hoc Networks (FANETs): A survey. Ad Hoc Networks, 2013, 11: 1254-1270 CrossRef Google Scholar

[10] Goyal P, Parmar V, Rishi R. Manet: vulnerabilities, challenges, attacks, application. Int J Comp Eng & Manage, 2011, 11: 32--37. Google Scholar

[11] Sahingoz O K. Mobile networking with UAVs: opportunities and challenges. In: Proceedings of IEEE International Conference on Unmanned Aircraft Systems (ICUAS), 2013. 933--941. Google Scholar

[12] Hartenstein H, Laberteaux K. VANET: Vehicular Applications and Inter-networking Technologies. Chichester: Wiley, 2010. 49--431. Google Scholar

[13] Zhou Y, Cheng N, Lu N. Multi-UAV-Aided Networks: Aerial-Ground Cooperative Vehicular Networking Architecture. IEEE Veh Technol Mag, 2015, 10: 36-44 CrossRef Google Scholar

[14] Beni G, Wang J. Swarm intelligence in cellular robotic systems. In: Robots and biological systems: towards a new bionics? Berlin: Springer, 1993. 703--712. Google Scholar

[15] Bonabeau E, Dorigo M, Marco D R D F, et al. Swarm Intelligence: From Natural to Artificial Systems. New York: Oxford University Press, 1999. 271--273. Google Scholar

[16] Kennedy J, Eberhart R C, Shi Y H. Swarm Intelligence. San Francisco: Morgan Kaufmann Publishers, 2001. 5--12. Google Scholar

[17] Grassé P P. La reconstruction du nid et les coordinations interindividuelles chezBellicositermes natalensis etCubitermes sp. la théorie de la stigmergie: Essai d'interprétation du comportement des termites constructeurs. Ins Soc, 1959, 6: 41-80 CrossRef Google Scholar

[18] Parunak H V D. Making swarming happen. In: Proceedings of Swarming and Network-Enabled C4ISR, 2003. 1--18. Google Scholar

[19] Bonabeau E, Dorigo M, Theraulaz G. Inspiration for optimization from social insect behaviour.. Nature, 2000, 406: 39-42 CrossRef PubMed Google Scholar

[20] Duan H B, Qiu H X. Unmanned Aerial Vehicle Swarm Autonomous Control Based on Swarm Intelligence. Beijing: Science Press, 2018. 1--25. Google Scholar

[21] Millonas M M. Swarms, phase transitions, and collective intelligence. Proc Artificial Life, 1993, 101(8): 137--151. Google Scholar

[22] Malone T W, Bernstein M S. Introduction. In: Handbook of Collective Intelligence. Cambridge: MIT Press. 2015. 1--13. Google Scholar

[23] Jing K P, Wang J Q. Research development and prospect of swarm/collective intelligence in foreign countries. Chin High Technol Lett, 2018, 28: 36--47. Google Scholar

[24] Xiao C H. Application of UAV on battlefield reconnaissance. Radio Eng China, 2008, 38: 50--52. Google Scholar

[25] Rosati S, Kru.zelecki K, Traynard L, et al. Speed-aware routing for UAV ad-hoc networks. In: Proceedings of IEEE GLOBECOM 2013, Georgia, 2013. 1367--1373. Google Scholar

[26] Biomo J D M M, Kunz T, St-Hilaire M. Routing in unmanned aerial ad hoc networks: introducing a route reliability criterion. In: Proceedings of the 7th IFIP Wireless and Mobile Networking Conference (WMNC), Vilamoura, 2014. 1--7. Google Scholar

[27] Federal Communications Commission. Facilitating opportunities for flexible, efficient, and reliable spectrum use employing cognitive radio technologies, NPRM & Order, ET Docket No. 03-108, FCC03-322, 2003. Google Scholar

[28] Mitola J, Maguire G Q. Cognitive radio: making software radios more personal. IEEE Pers Commun, 1999, 6: 13-18 CrossRef Google Scholar

[29] Mitola J. Cognitive radio: an integrated agent architecture for software defined radio. Dissertation for Ph.D. Degree. Sweden: Kungliga Tekniska Hogskolan, 2000. Google Scholar

[30] Haykin S. Cognitive radio: brain-empowered wireless communications. IEEE J Sel Areas Commun, 2005, 23: 201-220 CrossRef Google Scholar

[31] Federal Communications Commission. Spectrum Policy Task Force. Rep. ET Docket no. 02--135, 2002. Google Scholar

[32] Ma J, Zhang S, Li H. Sparse Bayesian Learning for the Time-Varying Massive MIMO Channels: Acquisition and Tracking. IEEE Trans Commun, 2019, 67: 1925-1938 CrossRef Google Scholar

[33] Liu X, Jia M, Zhang X. A Novel Multichannel Internet of Things Based on Dynamic Spectrum Sharing in 5G Communication. IEEE Internet Things J, 2019, 6: 5962-5970 CrossRef Google Scholar

[34] Sahai A, Hoven N, Tandra R. Some fundamental limits on cognitive radio. In: Proceedings of Allerton Conference on Communication, Control, and Computing, 2004. 1662--1671. Google Scholar

[35] Merchant K, Revay S, Stantchev G. Deep Learning for RF Device Fingerprinting in Cognitive Communication Networks. IEEE J Sel Top Signal Process, 2018, 12: 160-167 CrossRef Google Scholar

[36] Wang X, Wang X, Mao S. RF Sensing in the Internet of Things: A General Deep Learning Framework. IEEE Commun Mag, 2018, 56: 62-67 CrossRef Google Scholar

[37] Wang G, Yang K. A New Approach to Sensor Node Localization Using RSS Measurements in Wireless Sensor Networks. IEEE Trans Wireless Commun, 2011, 10: 1389-1395 CrossRef Google Scholar

[38] Seifeldin M, Saeed A, Kosba A E. Nuzzer: A Large-Scale Device-Free Passive Localization System for Wireless Environments. IEEE Trans Mobile Comput, 2013, 12: 1321-1334 CrossRef Google Scholar

[39] Moore M G, Davenport M A. Analysis of wireless networks using Hawkes processes. In: Proceedings of IEEE International Workshop on Signal Processing Advances in Wireless Communications, 2016. 1--5. Google Scholar

[40] Laghate M, Cabric D. Learning Wireless Networks' Topologies Using Asymmetric Granger Causality. IEEE J Sel Top Signal Process, 2018, 12: 233-247 CrossRef Google Scholar

[41] Klein L A. Sensor and Data Fusion: a Tool for Information Assessment and Decision Making. Bellingham: SPIE press, 2004. 51--95. Google Scholar

[42] Shen F, Ding G, Wang Z. UAV-Based 3D Spectrum Sensing in Spectrum-Heterogeneous Networks. IEEE Trans Veh Technol, 2019, 68: 5711-5722 CrossRef Google Scholar

[43] Steyvers M, Miller B. Cognition and collective intelligence. In: Handbook of collective intelligence. Cambridge: MIT Press. 2015, 1--16. Google Scholar

[44] Prelec D. A Bayesian truth serum for subjective data.. Science, 2004, 306: 462-466 CrossRef PubMed Google Scholar

[45] Wang G, Kulkarni S R, Poor H V. Aggregating Large Sets of Probabilistic Forecasts by Weighted Coherent Adjustment. Decision Anal, 2011, 8: 128-144 CrossRef Google Scholar

[46] Ding G, Wang J, Wu Q. On the limits of predictability in real-world radio spectrum state dynamics: from entropy theory to 5G spectrum sharing. IEEE Commun Mag, 2015, 53: 178-183 CrossRef Google Scholar

[47] Sun J, Shen L, Ding G. Predictability Analysis of Spectrum State Evolution: Performance Bounds and Real-World Data Analytics. IEEE Access, 2017, 5: 22760-22774 CrossRef Google Scholar

[48] Ding G, Jiao Y, Wang J. Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications. IEEE Commun Surv Tutorials, 2018, 20: 150-182 CrossRef Google Scholar

[49] Sun J, Wang J, Ding G. Long-Term Spectrum State Prediction: An Image Inference Perspective. IEEE Access, 2018, 6: 43489-43498 CrossRef Google Scholar

[50] Yu L, Chen J, Zhang Y. Deep spectrum prediction in high frequency communication based on temporal-spectral residual network. China Commun, 2018, 15: 25-34 CrossRef Google Scholar

[51] Yu L, Chen J, Ding G. Spectrum Prediction Based on Taguchi Method in Deep Learning With Long Short-Term Memory. IEEE Access, 2018, 6: 45923-45933 CrossRef Google Scholar

Copyright 2020  CHINA SCIENCE PUBLISHING & MEDIA LTD.  中国科技出版传媒股份有限公司  版权所有

京ICP备14028887号-23       京公网安备11010102003388号