TY - GEN
T1 - Enhanced AAV Visibility for Maritime Surveillance via 3D Voronoi Deployment and Hybrid Optimization
AU - Andreas, Andreou
AU - Mavromoustakis, Constandinos X.
AU - Mastorakis, George
AU - Bourdena, Athina
AU - Markakis, Evangelos
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - This paper presents a framework designed to significantly enhance the performance and visibility of Autonomous Aerial Vehicles (AAVs) deployed for maritime border surveillance. Traditional approaches often struggle with limited coverage and inefficiencies due to static deployment and isolated optimization strategies. To address these issues, the proposed approach integrates 3D Voronoi-based node deployment with hybrid optimization techniques. Specifically, it combines a modified Genetic Algorithm (GA) to strategically refine sensor node placements, along with a Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm enhanced by Particle Swarm Optimization (PSO) for dynamically optimizing AAV trajectories and computational task allocations. Simulation results demonstrate that the proposed method achieves significantly enhanced area coverage, reduced energy consumption, improved task completion rates, and overall better Quality of Service (QoS) compared to conventional methods.
AB - This paper presents a framework designed to significantly enhance the performance and visibility of Autonomous Aerial Vehicles (AAVs) deployed for maritime border surveillance. Traditional approaches often struggle with limited coverage and inefficiencies due to static deployment and isolated optimization strategies. To address these issues, the proposed approach integrates 3D Voronoi-based node deployment with hybrid optimization techniques. Specifically, it combines a modified Genetic Algorithm (GA) to strategically refine sensor node placements, along with a Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm enhanced by Particle Swarm Optimization (PSO) for dynamically optimizing AAV trajectories and computational task allocations. Simulation results demonstrate that the proposed method achieves significantly enhanced area coverage, reduced energy consumption, improved task completion rates, and overall better Quality of Service (QoS) compared to conventional methods.
KW - Autonomous Aerial Vehicles
KW - Genetic Algorithm
KW - IoT
KW - Maritime Surveillance
KW - Optimization
KW - PSO
KW - TD3
KW - Voronoi Diagrams
UR - https://www.scopus.com/pages/publications/105026756914
U2 - 10.1109/CAMAD67323.2025.11229894
DO - 10.1109/CAMAD67323.2025.11229894
M3 - Conference contribution
AN - SCOPUS:105026756914
T3 - IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD
BT - 2025 IEEE 30th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 30th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2025
Y2 - 14 October 2025 through 16 October 2025
ER -
