On Collaboration in Distributed Parameter Estimation with Resource Constraints
This work addresses resource-constrained parameter estimation for applications like environmental monitoring and smart infrastructure, offering incremental improvements in policy design for distributed systems.
The paper tackles the problem of optimizing resource allocation for parameter estimation in distributed sensor networks by formulating a Fisher information maximization problem that balances local sampling and collaboration. It analytically identifies conditions under which collaboration is beneficial and proposes multi-armed bandit algorithms, such as DOUBLE-F and UCB-Z, to learn optimal policies when correlation knowledge is unavailable, demonstrating effectiveness through simulation.
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.