TY - GEN
T1 - Enhancing UAV Network Efficiency through 6G+ Enabled Federated Learning Algorithms and Energy optimization Techniques
AU - Andreou, Andreas
AU - Mavromoustakis, Constandinos X.
AU - Batalla, Jordi Mongay
AU - Markakis, Evangelos
AU - Bourdena, Athina
AU - Mastorakis, George
AU - Song, Houbing
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This study presents an innovative approach to enhancing the efficiency of Unmanned Aerial Vehicles (UAV) in IoT networks. Employing UAVs as flying relays focuses on their role in data collection and support for terrestrial cellular networks. The central innovation lies in the application of Federated Learning (FL), which processes data while ensuring user privacy and reducing communication overhead. Addressing the challenge of UAVs' limited battery life, which restricts sustained FL operations, we introduce the Enhanced UAV Network optimization Algorithm with Adaptive Spatial Play (ENUO-ASP). ENUOASP incorporates a modified Particle Swarm optimization (PSO) technique to determine optimal UAV placements, enhancing data collection by focusing on the Signal-to-Interference Ratio (SINR). Additionally, the paper utilizes the Deep Deterministic Policy Gradient (DDPG) method for dynamic resource allocation, optimizing energy consumption and reducing link latency between the UAV network and users. The findings indicate that the ENUO algorithm outperforms existing methods by achieving higher data rates and balanced SINR. Furthermore, the ASP resource allocation strategy improves FL execution, significantly lowering latency and energy use. This research contributes to the UAV-enabled communication field, offering a more efficient and performance-driven solution for advanced IoT applications.
AB - This study presents an innovative approach to enhancing the efficiency of Unmanned Aerial Vehicles (UAV) in IoT networks. Employing UAVs as flying relays focuses on their role in data collection and support for terrestrial cellular networks. The central innovation lies in the application of Federated Learning (FL), which processes data while ensuring user privacy and reducing communication overhead. Addressing the challenge of UAVs' limited battery life, which restricts sustained FL operations, we introduce the Enhanced UAV Network optimization Algorithm with Adaptive Spatial Play (ENUO-ASP). ENUOASP incorporates a modified Particle Swarm optimization (PSO) technique to determine optimal UAV placements, enhancing data collection by focusing on the Signal-to-Interference Ratio (SINR). Additionally, the paper utilizes the Deep Deterministic Policy Gradient (DDPG) method for dynamic resource allocation, optimizing energy consumption and reducing link latency between the UAV network and users. The findings indicate that the ENUO algorithm outperforms existing methods by achieving higher data rates and balanced SINR. Furthermore, the ASP resource allocation strategy improves FL execution, significantly lowering latency and energy use. This research contributes to the UAV-enabled communication field, offering a more efficient and performance-driven solution for advanced IoT applications.
KW - deep RL
KW - Federated Learning
KW - IoT
KW - Network Coverage optimization
KW - Resource Allocation
KW - UAV
UR - http://www.scopus.com/inward/record.url?scp=85199989318&partnerID=8YFLogxK
U2 - 10.1109/IWCMC61514.2024.10592391
DO - 10.1109/IWCMC61514.2024.10592391
M3 - Conference contribution
AN - SCOPUS:85199989318
T3 - 20th International Wireless Communications and Mobile Computing Conference, IWCMC 2024
SP - 192
EP - 197
BT - 20th International Wireless Communications and Mobile Computing Conference, IWCMC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2024
Y2 - 27 May 2024 through 31 May 2024
ER -