In this article we address the optimisation of pheromone communication used for the mobility management of a swarm of Unmanned Aerial Vehicles (UAVs) for surveillance applications. A genetic algorithm is proposed to optimise the exchange of pheromone maps used in the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model which improves the vehicles' routes in order to achieve unpredictable trajectories as well as maximise area coverage. Experiments are conducted using realistic simulations, which additionally permit to assess the impact of packet loss ratios on the performance of the surveillance system, in terms of reliability and area coverage. https://doi.org/10.1007/978-3-030-88081-1_17

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

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

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

14. TABLE OF CONTENTS
1 INTRODUCTION
2 PHEROMONE BASED MOBILITY
3 OPTIMISATION ALGORITHM
4 EXPERIMENTAL RESULTS AND CONCLUSIONS
11/19

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

18. TABLE OF CONTENTS
1 INTRODUCTION
2 PHEROMONE BASED MOBILITY
3 OPTIMISATION ALGORITHM
4 EXPERIMENTAL RESULTS AND CONCLUSIONS
14/19

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