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Artificial intelligence and game theory controlled autonomous UAV swarms

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Abstract

Autonomous unmanned aerial vehicles (uavs) operating as a swarm can be deployed in austere environments, where cyber electromagnetic activities often require speedy and dynamic adjustments to swarm operations. Use of central controllers, uav synchronization mechanisms or pre-planned set of actions to control a swarm in such deployments would hinder its ability to deliver expected services. We introduce artificial intelligence and game theory based flight control algorithms to be run by each autonomous uav to determine its actions in near real-time, while relying only on local spatial, temporal and electromagnetic (em) information. Each uav using our flight control algorithms positions itself such that the swarm maintains mobile ad-hoc network (manet) connectivity and uniform asset distribution over an area of interest. Typical tasks for swarms using our algorithms include detection, localization and tracking of mobile em transmitters. We present a formal analysis showing that our algorithms can guide a swarm to maintain a connected manet, promote a uniform network spreading, while avoiding overcrowding with other swarm members. We also prove that they maintain manet connectivity and, at the same time, they can lead a swarm of autonomous uavs to follow or avoid an em transmitter. Simulation experiments in opnet modeler verify the results of formal analysis that our algorithms are capable of providing an adequate area coverage over a mobile em source and maintain manet connectivity. These algorithms are good candidates for civilian and military applications that require agile responses to the changes in dynamic environments for tasks such as detection, localization and tracking mobile em transmitters.

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Notes

  1. Throughout the paper the term manet refers to the mobile ad hoc network established among autonomous uavs in a swarm.

References

  1. Altshuler Y, Pentland A, Bruckstein A (2018) Swarms and network intelligence in search. Springer, Berlin

    Book  Google Scholar 

  2. Alzenad M, El-Keyi A, Lagum F, Yanikomeroglu H (2017) 3-D placement of an unmanned aerial vehicle base station (UAV-BS) for energy-efficient maximal coverage. IEEE Wirel Commun Lett 6(4):434–437

    Article  Google Scholar 

  3. Bardhan R, Bera T, Sundaram S (2017) A decentralized game theoretic approach for team formation and task assignment by autonomous unmanned aerial vehicles. In: International conference on unmanned aircraft systems (ICUAS), pp 432–437

  4. Bucaille I, Hethuin S, Rasheed T, Munari A, Hermenier R, Allsopp S (2013) Rapidly deployable network for tactical applications: aerial base station with opportunistic links for unattended and temporary events absolute example. In: IEEE military communications conference (MILCOM), pp 1116–1120

  5. Carvalho J, Jucá M, Menezes A, Olivi L, Marcato A, dos Santos A (2017) Autonomous UAV outdoor flight controlled by an embedded system using Odroid and ROS. Controlo 2016:423–437

    Google Scholar 

  6. Cetin O, Yilmaz G (2016) Real-time autonomous UAV formation flight with collision and obstacle avoidance in unknown environment. J Intell Robot Syst 84(1–4):415–433

    Article  Google Scholar 

  7. Cho J, Kim J (2018) Performance comparison of heuristic algorithms for UAV deployment with low power consumption. In: International conference on information and communication technology convergence (ICTC). IEEE, New York, pp 1067–1069

  8. Clark A, Sun K, Bushnell L, Poovendran R (2015) A game-theoretic approach to IP address randomization in decoy-based cyber defense. In: International conference on decision and game theory for security, pp 3–21

  9. Das S, Tripathi S (2018) Adaptive and intelligent energy efficient routing for transparent heterogeneous ad-hoc network by fusion of game theory and linear programming. Appl Intell 48(7):1825–1845

    Article  Google Scholar 

  10. Davis D, Chung T, Clement M, Day M (2016) Consensus-based data sharing for large-scale aerial swarm coordination in Lossy communications environments. In: International conference on intelligent robots and systems (IROS), pp 3801–3808

  11. Du Q, Faber V, Gunzburger M (1999) Centroidal Voronoi tessellations: applications and algorithms. SIAM Rev 41(4):637–676

    Article  MathSciNet  Google Scholar 

  12. Eldosouky AR, Ferdowsi A, Saad W (2019) Drones in distress: a game-theoretic countermeasure for protecting UAVs against GPS spoofing. IEEE Internet Things J 7:2840–2854

    Article  Google Scholar 

  13. Elhoseny M, Yuan X, Yu Z, Mao C, El-Minir H, Riad A (2015) Balancing energy consumption in heterogeneous wireless sensor networks using genetic algorithm. IEEE Commun Lett 19(12):2194–2197

    Article  Google Scholar 

  14. Findeis D, Balchanos M, Vachtsevanos G, Mavris D (2019) Modeling and simulation of UAV swarm formation control in response to wind gusts. In: AIAA Scitech 2019 Forum, p 1571

  15. Fudenberg D, Tirole J (1991) Game theory. The MIT Press, New York

    MATH  Google Scholar 

  16. Garapati K, Roldán J, Garzón M, del Cerro J, Barrientos A (2017) A game of drones: game theoretic approaches for multi-robot task allocation in security missions. In: Iberian robotics conference, pp 855–866

  17. Gundry S, Zou J, Urrea E, Sahin C, Kusyk J, Uyar M (2012) Analysis of emergent behavior for GA-based topology control mechanism for self-spreading nodes in MANETs. Adv Intell Model Simul Artif Intell Based Models Tech Scalable Comput 422:155–183

    Google Scholar 

  18. Gundry S, Zou J, Uyar M, Sahin C, Kusyk J (2015) Differential evolution-based autonomous and disruption tolerant vehicular self-organization in MANETs. Ad Hoc Netw 25(B):454–471

    Article  Google Scholar 

  19. Ji Z, Liu K (2008) Multi-stage pricing game for collusion-resistant dynamic spectrum allocation. IEEE J Sel Areas Commun 26(1):182–191

    Article  Google Scholar 

  20. Karaboga D, Okdem S, Ozturk C (2012) Cluster based wireless sensor network routing using artificial bee colony algorithm. Wireless Netw 18(7):847–860

    Article  Google Scholar 

  21. Kusyk J, Sahin C, Zou J, Gundry S, Uyar M, Urrea E (2013) Game theoretic and bio-inspired optimization approach for autonomous movement of MANET nodes. In: Zelinka I, Snášel V, Abraham A (eds) Handbook of optimization. Intelligent systems reference library, vol 38. Springer, Berlin, Heidelberg

    Google Scholar 

  22. Kusyk J, Urrea E, Sahin C, Uyar M (2012) Game theory and genetic algorithm based approach for self positioning of autonomous nodes. Int J Ad Hoc Sensor Wirel Netw 16:93–118

    Google Scholar 

  23. Kusyk J, Uyar M, Ma K, Ma K, Budhu K, Samoylov E, Plishka J, Bertoli G, Boksiner J (2019) Game theory and biology inspired flight control for autonomous UAVs operating in contested environments. In: 40th IEEE Sarnoff symposium, pp 1–6

  24. Kusyk J, Uyar M, Ma K, Ma K, Plishka J, Bertoli G, Boksiner J (2019) AI and game theory based autonomous UAV swarm for cyber security. In: IEEE military communications conference (MILCOM), pp 1–6

  25. Kusyk J, Uyar M, Sahin C (2018) Survey on evolutionary computation methods for cyber security of mobile ad hoc networks. Evol Intell 10:95–117

    Article  Google Scholar 

  26. Kusyk J, Zou J, Gundry S, Sahin C, Uyar M (2013) Performance metrics for self-positioning autonomous MANET nodes. J Cybersecurity Mob 2:151–173

    Google Scholar 

  27. Mahler RP (2007) Statistical multisource-multitarget information fusion, vol 685. Artech House, Norwood

    MATH  Google Scholar 

  28. Merwaday A, Guvenc I (2015) UAV assisted heterogeneous networks for public safety communications. In: Wireless communications and networking conference workshops (WCNCW), pp 329–334

  29. Mousavi S, Afghah F, Ashdown J, Turck K (2019) Use of a quantum genetic algorithm for coalition formation in large-scale UAV networks. Ad Hoc Netw 87:26–36

    Article  Google Scholar 

  30. Mozaffari M, Saad W, Bennis M, Debbah M (2016) Unmanned aerial vehicle with underlaid device-to-device communications: performance and tradeoffs. IEEE Trans Wirel Commun 15(6):3949–3963

    Article  Google Scholar 

  31. Nash J (1950) Equilibrium points in $n$-person games. Proc Natl Acad Sci USA 36:48–49

    Article  MathSciNet  Google Scholar 

  32. OPNET Modeler, Riverbed Tech., Inc. https://www.riverbed.com/products/steelcentral/opnet.html/. Accessed 01 Nov 2019

  33. Reina D, Ruiz P, Ciobanu R, Toral S, Dorronsoro B, Dobre C (2016) A survey on the application of evolutionary algorithms for mobile multihop ad hoc network optimization problems. Int J Distrib Sensor Netw 12:2082496

    Article  Google Scholar 

  34. Sahin C, Gundry S, Uyar M (2012) Markov chain analysis of self-organizing mobile nodes. J Intell Robot Syst 67(2):133–153

    Article  Google Scholar 

  35. Sampedro C, Bavle H, Sanchez-Lopez J, Rodríguez-Ramos RFS, Alejandro M, Martin Campoy P (2016) A flexible and dynamic mission planning architecture for UAV swarm coordination. In: International conference on unmanned aircraft systems (ICUAS), pp 355–363

  36. Sasikala E, Nandhakumar N (2015) An intelligent technique to detect jamming attack in wireless sensor networks (WSNs). Int J Fuzzy Syst 17:76–83

    Article  Google Scholar 

  37. Sedjelmaci H, Senouci S, Ansari N (2017) Intrusion detection and ejection framework against lethal attacks in UAV-aided networks: a Bayesian game-theoretic methodology. IEEE Trans Intell Transp Syst 18(5):1143–1153

    Article  Google Scholar 

  38. Shakhatreh H, Khreishah A, Chakareski J, Salameh H, Khalil I (2016) On the continuous coverage problem for a swarm of UAVs. In: 37th IEEE Sarnoff symposium, pp 1–6

  39. Sherman T, Boskovich S (2019) UAV swarm mapping using a fully distributed control approach. In: AIAA Scitech 2019 forum, p 2287

  40. Silva Arantes J, Silva Arantes M, Motta Toledo CF, Júnior OT, Williams BC (2017) Heuristic and genetic algorithm approaches for UAV path planning under critical situation. Int J Artif Intell Tools 26(01):1760008

    Article  Google Scholar 

  41. Smyrnakis M, Kladis G, Aitken J, Veres S (2016) Distributed selection of flight formation in UAV missions. J Appl Math Bioinform 6(3):93–124

    MATH  Google Scholar 

  42. Srivastava D, Pakkar R, Langrehr A, Yamane C (2019) Adaptable UAV swarm autonomy and formation platform. In: IEEE aerospace conference. IEEE, New York, pp 1–6

  43. Systems Tool Kit (STK). http://www.agi.com/products/stk/. Accessed 01 Nov 2019

  44. Thakoor O, Garg J, Nagi R (2019) Multiagent UAV routing: a game theory analysis with tight price of anarchy bounds. IEEE Trans Autom Sci Eng 17:100–106

    Article  Google Scholar 

  45. Vesterstrom J, Thomsen R (2004) A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: Proceedings of the 2004 congress on evolutionary computation, vol 2, pp 1980–1987

  46. Von Neumann J, Morgenstern O (2007) Theory of games and economic behavior. Princeton University Press, Princeton

    MATH  Google Scholar 

  47. Von Stackelberg H (2010) Market structure and equilibrium. Springer, Berlin

    MATH  Google Scholar 

  48. Wu H, Li H, Xiao R, Liu J (2018) Modeling and simulation of dynamic ant colony’s labor division for task allocation of UAV swarm. Physica A 491:127–141

    Article  MathSciNet  Google Scholar 

  49. Yue L, Chen H (2019) Unmanned vehicle path planning using a novel ant colony algorithm. EURASIP J Wirel Commun Netw 2019(1):136

    Article  Google Scholar 

  50. Zou J, Gundry S, Kusyk J, Uyar M, Sahin C (2013) 3D genetic algorithms for underwater sensor networks. Int Ad Hoc Ubiquitous Comput 13:10–22

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by a Grant from US Army Combat Capabilities Development Command (CCDC)—Command Control Communication Computers Cyber Intelligence Surveillance Reconnaissance (C5ISR) Center D01 W911SR-14-2-0001-0014. The contents of this document represent the views of the authors and are not necessarily the official views of, or endorsed by, the US Government, Department of Defense, Department of the Army or US Army CCDC C5ISR Center.

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Authors and Affiliations

  1. NYC College of Technology, City University of New York, Brooklyn, NY, 11201, USA

    Janusz Kusyk

  2. The City College of New York, City University of New York, New York, NY, 10031, USA

    M. Umit Uyar, Kelvin Ma, Eltan Samoylov & Ricardo Valdez

  3. I2WD, US Army CCDC - C5ISR Center, Aberdeen Proving Ground, MD, 21005, USA

    Joseph Plishka, Sagor E. Hoque, Giorgio Bertoli & Jeffrey Boksiner

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  1. Janusz Kusyk
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  2. M. Umit Uyar
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Correspondence to M. Umit Uyar.

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Kusyk, J., Uyar, M.U., Ma, K. et al. Artificial intelligence and game theory controlled autonomous UAV swarms. Evol. Intel. 14, 1775–1792 (2021). https://doi.org/10.1007/s12065-020-00456-y

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