On Collaboration in Distributed Parameter Estimation With Resource Constraints

Effective resource allocation in sensor networks, IoT systems, and distributed computing is essential for applications such as environmental monitoring, surveillance, and smart infrastructure. Sensors or agents must optimize their resource allocation to maximize the accuracy of parameter estimation....

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Bibliographic Details
Published in:IEEE eTransactions on network and service management 2024-10, p.1-1
Main Authors: Chen, Yu-Zhen Janice, Menasche, Daniel S., Towsley, Don
Format: Article
Language:English
Subjects:
Collaboration
Correlation
Data collection
Distributed databases
Distributed Parameter Estimation
Estimation
Fisher Information
Kalman Filter
Multi-Armed Bandit (MAB)
Optimization
Parameter estimation
Resource management
Sensor Selection
Sequential Estimation
Vectors
Vertically Partitioned Data
Wireless sensor networks
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Summary:Effective resource allocation in sensor networks, IoT systems, and distributed computing is essential for applications such as environmental monitoring, surveillance, and smart infrastructure. Sensors or agents must optimize their resource allocation to maximize the accuracy of parameter estimation. In this work, we consider a group of sensors or agents, each sampling from a different variable of a multivariate Gaussian distribution and having a different estimation objective. We formulate a sensor or agent's data collection and collaboration policy design problem as a Fisher information maximization (or Cramer-Rao bound minimization) problem. This formulation captures a novel trade-off in energy use, between locally collecting univariate samples and collaborating to produce multivariate samples. When knowledge of the correlation between variables is available, we analytically identify two cases: (1) where the optimal data collection policy entails investing resources to transfer information for collaborative sampling, and (2) where knowledge of the correlation between samples cannot enhance estimation efficiency. When knowledge of certain correlations is unavailable, but collaboration remains potentially beneficial, we propose novel approaches that apply multi-armed bandit algorithms to learn the optimal data collection and collaboration policy in our sequential distributed parameter estimation problem. We illustrate the effectiveness of the proposed algorithms, DOUBLE-F, DOUBLE-Z, UCB-F, UCB-Z, through simulation.
ISSN:1932-4537
1932-4537
DOI:10.1109/TNSM.2024.3468997