Improving Pheromone Communication for UAV Swarm Mobility Management

1. University of Luxembourg Multilingual. Personalized. Connected. Improving Pheromone Communication for UAV Swarm Mobility Management Daniel H. Stolfi1 Matthias R. Brust1 Grégoire Danoy1,2 Pascal Bouvry1,2 13th International Conference on Computational Collective Intelligence – ICCCI 2021 September 29th – October 1st, 2021 1 SnT - Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg 2 FSTM/DCS, University of Luxembourg, Luxembourg

2. TABLE OF CONTENTS 1 INTRODUCTION 2 PHEROMONE BASED MOBILITY 3 OPTIMISATION ALGORITHM 4 EXPERIMENTAL RESULTS AND CONCLUSIONS

3. TABLE OF CONTENTS 1 INTRODUCTION 2 PHEROMONE BASED MOBILITY 3 OPTIMISATION ALGORITHM 4 EXPERIMENTAL RESULTS AND CONCLUSIONS 1/19

4. UAV: UNMANNED AERIAL VEHICLE UAVs, also known as drones, can be fixed wings or multi-rotors. They are usually equipped with: I Video (infrared) cameras I Radio communication devices I Sensors (pollution, radiation, etc.) I Actuators Advantages I Explore different areas I High speed I Excellent communication capabilities Disadvantages I Reduced flight time I Cannot carry larger payloads Photo by Daniel H. Stolfi Source: indiamart.com 2/19

5. UAV – APPLICATIONS UAVs are widely used in many applications: Goods transportation Road traffic surveillance Fire fighting Environmental monitoring Wildfire monitoring Farming Architecture surveillance Internet delivery Homeland security To herd a flock of birds away from an airport (!) Surveillance systems Source: procurious.com Photo by Tyler Casey on unsplash.com 3/19

7. SURVEILLANCE APPROACH Our approach to surveillance missions comprises: Multi-swarms of Unmanned Aerial Vehicles Unpredictable trajectories Maximising area coverage Maximising intruder detections Pheromone based mobility Mobility models analysed: CROMM (Chaotic Rössler Mobility Model) → Pure chaotic CACOC (Chaotic Ant Colony Optimisation for Coverage) → CROMM + Pheromones CACOC+ → Parameterised version of CACOC 5/19

8. CROMM1 Pure chaotic mobility 1 M. Rosalie, G. Danoy, S. Chaumette, and P. Bouvry. “Chaos-enhanced mobility models for multilevel swarms of UAVs”. In: Swarm and Evol. Comput. 41.November 2017 (2018), pp. 36–48. 6/19

9. CACOC MOBILITY MODEL2 Pheromone trails 2 M. Rosalie, G. Danoy, S. Chaumette, and P. Bouvry. “Chaos-enhanced mobility models for multilevel swarms of UAVs”. In: Swarm and Evol. Comput. 41.November 2017 (2018), pp. 36–48. 7/19

10. CACOC+ MOBILITY MODEL3 TABLE: Parameters of CACOC+. Parameter Units Range Pheromone amount (τa) % [1 − 100] Pheromone radius (τr ) cells [0.5 − 2.5] Pheromone scan depth (τd ) cells [1 − 10] Each UAV in the swarm has to know about the pheromone trails in the neighbourhood 3 D. H. Stolfi, M. R. Brust, G. Danoy, and P. Bouvry. “A Cooperative Coevolutionary Approach to Maximise Surveillance Coverage of UAV Swarms”. In: 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC). IEEE, 2020, pp. 1–6. 8/19

11. PHEROMONE SHARING (a) Full pheromone map (b) UAV1’s known map (c) UAV2’s known map FIGURE: Pheromone Sharing 9/19

12. COMMUNICATION SCENARIOS Radio link subject to interferences Maximum communication range Shorter data packets could reduce. . . I packet loss probability I energy consumption Could we keep good coverage rates by optimising the parameters of CACOC+? FIGURE: Four scenarios simulated using ARGoS 10/19

13. COMMUNICATION SCENARIOS Radio link subject to interferences Maximum communication range Shorter data packets could reduce. . . I packet loss probability I energy consumption Could we keep good coverage rates by optimising the parameters of CACOC+? FIGURE: Four scenarios simulated using ARGoS 10/19

15. OPTIMISATION ALGORITHM: GA GAs are efficient method for solving combinatorial optimisation problems Simulates processes present in evolution Operators: I Binary tournament I Uniform crossover I Mutation for integers values I Best individual in offspring replaces the worst individual in population Problem representation: Fitness function: ~ x = {τa1 , τr1 , τd1 , . . . , τaN , τrN , τdN } F(~ x) = # of explored cells # of cells in the scenario N ∈ {2, 4, 6} is the number of UAVs in the swarm 12/19

16. EVALUATION: ARGOS SIMULATOR FIGURE: ARGoS Simulator Simulation of UAV’s dynamics Realistic communication layer 10-metre radio range Simulated packet loss rates 1000 evaluations per GA run 360 runs in total 10 equivalent days of computing time (reduced by parallel evaluations/runs) 13/19

17. EVALUATION: ARGOS SIMULATOR FIGURE: ARGoS Simulator Simulation of UAV’s dynamics Realistic communication layer 10-metre radio range Simulated packet loss rates 1000 evaluations per GA run 360 runs in total 10 equivalent days of computing time (reduced by parallel evaluations/runs) 13/19

19. OPTIMISATION RESULTS Case Study Scenario Fitness Friedman Rank Wilcoxon p-value Avg. SD. Max. 2 UAVs CACOC+ 0.524 0.005 0.538 2.50 0.365 CACOC+.10 0.523 0.006 0.535 2.10 0.325 CACOC+.05 0.525 0.006 0.535 2.68 0.802 CACOC+.00 0.526 0.007 0.539 2.72 — 4 UAVs CACOC+ 0.769 0.006 0.780 2.28 0.087 CACOC+.10 0.771 0.007 0.788 2.70 0.640 CACOC+.05 0.769 0.005 0.782 2.22 0.120 CACOC+.00 0.772 0.006 0.786 2.80 — 6 UAVs CACOC+ 0.885 0.005 0.895 2.48 0.246 CACOC+.10 0.887 0.004 0.897 2.70 — CACOC+.05 0.885 0.004 0.893 2.52 0.133 CACOC+.00 0.884 0.006 0.896 2.30 0.058 Fitness values of each optimisation run for each case study and scenario. 15/19

20. OPTIMISATION RESULTS Case Study Scenario Fitness Friedman Rank Wilcoxon p-value Avg. SD. Max. 2 UAVs CACOC+ 0.524 0.005 0.538 2.50 0.365 CACOC+.10 0.523 0.006 0.535 2.10 0.325 CACOC+.05 0.525 0.006 0.535 2.68 0.802 CACOC+.00 0.526 0.007 0.539 2.72 — 4 UAVs CACOC+ 0.769 0.006 0.780 2.28 0.087 CACOC+.10 0.771 0.007 0.788 2.70 0.640 CACOC+.05 0.769 0.005 0.782 2.22 0.120 CACOC+.00 0.772 0.006 0.786 2.80 — 6 UAVs CACOC+ 0.885 0.005 0.895 2.48 0.246 CACOC+.10 0.887 0.004 0.897 2.70 — CACOC+.05 0.885 0.004 0.893 2.52 0.133 CACOC+.00 0.884 0.006 0.896 2.30 0.058 Fitness values of each optimisation run for each case study and scenario. 15/19

21. OPTIMISATION RESULTS Case Study 2 UAV 4 UAV 6 UAV CROMM 13.3% 43.5% 52.2% CACOC 46.5% 71.0% 85.8% CACOC.10 46.5% 68.7% 85.8% CACOC.05 46.5% 71.6% 84.2% CACOC.00 38.8% 70.2% 80.0% CACOC+ 53.8% 78.0% 89.5% CACOC+.10 53.5% 78.8% 89.7% CACOC+.05 53.5% 78.2% 89.3% CACOC+.00 53.9% 78.6% 89.6% Coverage values 16/19

22. OPTIMISATION RESULTS Case Study 2 UAV 4 UAV 6 UAV CROMM 13.3% 43.5% 52.2% CACOC 46.5% 71.0% 85.8% CACOC.10 46.5% 68.7% 85.8% CACOC.05 46.5% 71.6% 84.2% CACOC.00 38.8% 70.2% 80.0% CACOC+ 53.8% 78.0% 89.5% CACOC+.10 53.5% 78.8% 89.7% CACOC+.05 53.5% 78.2% 89.3% CACOC+.00 53.9% 78.6% 89.6% Coverage values 16/19

23. INTERFERENCES AND PACKET LOSS (a) 2 UAVs (b) 4 UAVs (c) 6 UAVs FIGURE: Area Coverage vs. Packet Loss Probability 17/19

24. INTERFERENCES AND PACKET LOSS (a) 2 UAVs (b) 4 UAVs (c) 6 UAVs FIGURE: Area Coverage vs. Packet Loss Probability 17/19

25. CONCLUSIONS AND FUTURE WORK Conclusions CACOC+ improved CACOC and CROMM in all scenarios CACOC+ adapted the UAVs’ behaviour to deal with an incomplete pheromone map CACOC performance degraded with the reduction of the transmitted data CROMM (pure chaotic) cannot compete with pheromone-based mobility Future Work Include more UAVs in the study Use a specific parameter for each UAV Alternative collision avoidance algorithm Validation of trajectories using real drones 18/19

26. QUESTIONS? Daniel H. Stolfi daniel.stolfi@uni.lu https://hunted.gforge.uni.lu/ https://pcog.uni.lu/ https://wwwen.uni.lu/snt/ https://wwwen.uni.lu/ Improving Pheromone Communication for UAV Swarm Mobility Management Daniel H. Stolfi1, Matthias R. Brust1, Grégoire Danoy1,2, and Pascal Bouvry1,2 1 SnT - Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg 2 FSTM/DCS, University of Luxembourg, Luxembourg References: M. Rosalie, G. Danoy, S. Chaumette, and P. Bouvry. “Chaos-enhanced mobility models for multilevel swarms of UAVs”. In: Swarm and Evol. Comput. 41.November 2017 (2018), pp. 36–48 D. H. Stolfi, M. R. Brust, G. Danoy, and P. Bouvry. “A Cooperative Coevolutionary Approach to Maximise Surveillance Coverage of UAV Swarms”. In: 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC). IEEE, 2020, pp. 1–6 D. H. Stolfi, M. R. Brust, G. Danoy, and P. Bouvry. “Optimizing the Performance of an Unpredictable UAV Swarm for Intruder Detection”. In: Optimization and Learning. Cham: Springer International Publishing, 2020, pp. 37–48 D. H. Stolfi, M. R. Brust, G. Danoy, and P. Bouvry. “Competitive Evolution of a UAV Swarm for Improving Intruder Detection Rates”. In: 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, May 2020, pp. 528–535 This work relates to Department of Navy award N62909-18-1-2176 issued by the Office of Naval Research. The United States Government has a royalty-free license throughout the world in all copyrightable material contained herein. This work is partially funded by the joint research programme UL/SnT-ILNAS on Digital Trust for Smart-ICT. 19/19

Improving Pheromone Communication for UAV Swarm Mobility Management

Improving Pheromone Communication for UAV Swarm Mobility Management

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