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Edge Learning via Message Passing: Distributed Estimation Framework Based on Gaussian Mixture Model
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DOI：10.1109/JIOT.2024.3432114.png)
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摘要
En 中文
To leverage distributed data communication and learning in sensor networks effectively, edge learning (EL) methods have garnered significant attention. In the realm of distributed sensor networks, achieving consensus estimation of interested variables stands as a pivotal challenge. To address this challenge using EL methods, several approaches have been proposed combining message passing (MP) algorithms. In this article, we first describe the distributed consensus algorithm based on MP and summarize the sampling-based and parameter-based representation of the beliefs exchanged in the distributed MP algorithm. To improve the accuracy of estimation while retaining the low-complexity advantage of the parametric representation method, we propose a distributed consensus framework based on the Gaussian mixture model (GMM) MP. We approximate and keep the form beliefs as GMM in the iterations. Two different simulation scenarios are performed to shed light on the proposed distributed consensus estimation framework, i.e., static target localization and dynamic target tracking. Finally, simulation results show the performance advantages of the algorithm proposed.
Keyword:
Estimation
Robot sensing systems
Location awareness
Accuracy
Signal processing algorithms
Message passing
Internet of Things
Consensus algorithm
distributed estimation
edge learning (EL)
factor graph
Gaussian mixture model (GMM)
message passing (MP)
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
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