@Article{cmc.2024.048020,
AUTHOR = {Jiangang Lu, Ruifeng Zhao, Zhiwen Yu, Yue Dai, Kaiwen Zeng},
TITLE = {RL and AHP-Based Multi-Timescale Multi-Clock Source Time Synchronization for Distribution Power Internet of Things},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {78},
YEAR = {2024},
NUMBER = {3},
PAGES = {4453--4469},
URL = {http://www.techscience.com/cmc/v78n3/55935},
ISSN = {1546-2226},
ABSTRACT = {Time synchronization (TS) is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things (IoT). Multi-clock source time synchronization (MTS) has significant advantages of high reliability and accuracy but still faces challenges such as optimization of the multi-clock source selection and the clock source weight calculation at different timescales, and the coupling of synchronization latency jitter and pulse phase difference. In this paper, the multi-timescale MTS model is conducted, and the reinforcement learning (RL) and analytic hierarchy process (AHP)-based multi-timescale MTS algorithm is designed to improve the weighted summation of synchronization latency jitter standard deviation and average pulse phase difference. Specifically, the multi-clock source selection is optimized based on Softmax in the large timescale, and the clock source weight calculation is optimized based on lower confidence bound-assisted AHP in the small timescale. Simulation shows that the proposed algorithm can effectively reduce time synchronization delay standard deviation and average pulse phase difference.},
DOI = {10.32604/cmc.2024.048020}
}