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.

翻译：在传感器网络、物联网系统和分布式计算中，有效的资源分配对于环境监测、安防监控和智能基础设施等应用至关重要。传感器或智能体必须优化其资源分配，以最大化参数估计的精度。本文研究一组传感器或智能体，每个节点从多元高斯分布的不同变量中采样，并具有不同的估计目标。我们将传感器或智能体的数据收集与协作策略设计问题建模为费舍尔信息最大化（或克拉美-罗下界最小化）问题。该模型揭示了资源使用中一种新颖的权衡：是在本地收集单变量样本，还是通过协作生成多变量样本。当变量间的相关性已知时，我们通过解析方法识别出两种情况：（1）最优数据收集策略需要投入资源以传输信息进行协作采样；（2）样本相关性的知识无法提升估计效率。当部分相关性未知但协作仍可能带来收益时，我们提出了创新方法，将多臂老虎机算法应用于序列分布式参数估计问题中，以学习最优的数据收集与协作策略。通过仿真实验，我们验证了所提算法DOUBLE-F、DOUBLE-Z、UCB-F、UCB-Z的有效性。