Process-Based Triggering and Accelerated Dual Averaging Algorithm for Dynamic Parameter Estimation

In the large-scale cyber-physical systems, to conserve communication resources holds critical significance, thereby driving extensive research interest toward distributed optimization algorithms with high communication efficiency. This paper investigates the constrained distributed dynamic parameter...

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Veröffentlicht in:IEEE transactions on signal and information processing over networks Jg. 11; S. 683 - 695
Hauptverfasser: Zhou, Yaoyao, Chen, Gang, Chen, Zhenghua
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Accelerated algorithm
Algorithms
Communication
communication delays
constrained distributed dynamic parameter estimation
Convergence
Convex functions
Cyber-physical systems
Delays
Directed graphs
dynamic regret
Dynamic scheduling
Heuristic algorithms
Optimization
Parameter estimation
Performance degradation
process-based triggering
Resource conservation
Time dependence
Training
Vehicle dynamics
ISSN:2373-776X, 2373-7778
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Zusammenfassung:In the large-scale cyber-physical systems, to conserve communication resources holds critical significance, thereby driving extensive research interest toward distributed optimization algorithms with high communication efficiency. This paper investigates the constrained distributed dynamic parameter estimation problem (CDPE) for communication resource conservation, and further considers how to cope with more generally directed communication structure, unavoidable arbitrary bounded communication delays, and diverse update strategies. We introduce a new process-based triggering strategy and develop an efficient Process-based Triggering Accelerated Dual Averaging Algorithm(PTADA). Compared with the traditional time-dependent threshold, the PTADA can well adapt to the dynamic behavior of distributed optimization and save communication resources. Our dynamic bound is linear and is independent of the explicit time horizon. Moreover, we further extend PTADA to address scenarios where gradient information cannot be directly obtained, while ensuring no performance degradation. This extension can make the algorithm more realistic and universal. Finally, a distributed multi-sensor network is set up to verify the effectiveness of the algorithm.
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ISSN:2373-776X
2373-7778
DOI:10.1109/TSIPN.2025.3587414