TY - JOUR
T1 - Distributed Clock Parameter Tracking for Highly Dynamic Multi-UAV Networks-Enabled Industrial IoT
AU - Jin, Xin
AU - Yang, Xuanhe
AU - Pan, Gaofeng
AU - Wang, Shuai
AU - Niyato, Dusit
AU - An, Jianping
N1 - Publisher Copyright:
© 2025 IEEE. All rights reserved,
PY - 2025
Y1 - 2025
N2 - With the increasing demands of the Industrial Internet of Things (IIoT), highly dynamic multi-unmanned aerial vehicle (UAV) networks are becoming indispensable to IIoT due to their flexibility, cost-effectiveness, robust safety measures, and real-time data collection capabilities. Accurate time synchronization is crucial for coordinated missions of multi-UAV networks, yet the time-varying nature of clock parameters and the rapid movements of UAVs pose significant challenges to achieving precise synchronization. This article introduces new state and observation models for clock and velocity parameters and proposes a Doppler and timestamp-based distributed algorithm for tracking clock parameters using the Kalman filter. To evaluate the performance of the proposed algorithm, we derive the Bayesian Cramér–Rao lower bound and conduct numerical simulations. The results of the simulations demonstrate that our algorithm surpasses existing methods in terms of accuracy in tracking clock parameters.
AB - With the increasing demands of the Industrial Internet of Things (IIoT), highly dynamic multi-unmanned aerial vehicle (UAV) networks are becoming indispensable to IIoT due to their flexibility, cost-effectiveness, robust safety measures, and real-time data collection capabilities. Accurate time synchronization is crucial for coordinated missions of multi-UAV networks, yet the time-varying nature of clock parameters and the rapid movements of UAVs pose significant challenges to achieving precise synchronization. This article introduces new state and observation models for clock and velocity parameters and proposes a Doppler and timestamp-based distributed algorithm for tracking clock parameters using the Kalman filter. To evaluate the performance of the proposed algorithm, we derive the Bayesian Cramér–Rao lower bound and conduct numerical simulations. The results of the simulations demonstrate that our algorithm surpasses existing methods in terms of accuracy in tracking clock parameters.
KW - Distributed clock parameters tracking
KW - highly dynamic multi-unmanned aerial vehicle (UAV) networks
KW - Industrial Internet of Things (IIoT)
KW - Kalman filter
UR - http://www.scopus.com/inward/record.url?scp=85218900157&partnerID=8YFLogxK
U2 - 10.1109/TII.2025.3538119
DO - 10.1109/TII.2025.3538119
M3 - Article
AN - SCOPUS:85218900157
SN - 1551-3203
VL - 21
SP - 4210
EP - 4220
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 5
ER -