Motivated by the Internet-of-things and sensor networks for cyber-physical systems, the problem of dynamic sensor activation for the centralized tracking of an i.i.d. time-varying process is examined. The trade-off is between energy efficiency, which decreases with the number of active sensors, and fidelity, which increases with the number of active sensors. The problem of minimizing the time-averaged mean-squared error over infinite horizon is examined under the constraint of the mean number of active sensors. The proposed methods artfully combine Gibbs sampling and stochastic approximation for learning, in order to create a high performance, low-complexity, energy efficient tracking mechanisms with active sensor selection. Centralized tracking of i.i.d. process with known distribution as well as an unknown parametric distribution are considered. For an i.i.d. process with known distribution, convergence to the global optimal solution with high probability is proved. The main challenge of the i.i.d. case is that the process has a distribution parameterized by a known or unknown parameter which must be learned. One key theoretical result proves that the proposed algorithm for tracking an i.i.d. process with unknown parametric distribution converges to local optima, while requiring very limited computational resource. Numerical results show the efficacy of the proposed algorithms and also suggest that global optimality is in fact achieved in some cases.

中文翻译：

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用于集中跟踪 IID 过程的动态传感器子集选择

受物联网和信息物理系统传感器网络的推动，研究了用于集中跟踪 iid 时变过程的动态传感器激活问题。权衡是在能量效率（随着有源传感器的数量增加而降低）和保真度（随着有源传感器的数量增加而增加）之间进行权衡。在活动传感器平均数量的约束下，研究了在无限范围内最小化时间平均均方误差的问题。所提出的方法巧妙地结合了吉布斯采样和随机逼近进行学习，以创建具有主动传感器选择的高性能、低复杂度、节能的跟踪机制。考虑了具有已知分布和未知参数分布的 iid 过程的集中跟踪。对于已知分布的 iid 过程，证明了收敛到全局最优解的概率很高。iid 案例的主要挑战是该过程具有由必须学习的已知或未知参数参数化的分布。一个关键的理论结果证明，所提出的用于跟踪具有未知参数分布的 iid 过程的算法收敛到局部最优，同时需要非常有限的计算资源。数值结果显示了所提出算法的有效性，并且还表明在某些情况下实际上实现了全局最优。情况是该过程具有由必须学习的已知或未知参数参数化的分布。一个关键的理论结果证明，所提出的用于跟踪具有未知参数分布的 iid 过程的算法收敛到局部最优，同时需要非常有限的计算资源。数值结果显示了所提出算法的有效性，并且还表明在某些情况下实际上实现了全局最优。情况是该过程具有由必须学习的已知或未知参数参数化的分布。一个关键的理论结果证明，所提出的用于跟踪具有未知参数分布的 iid 过程的算法收敛到局部最优，同时需要非常有限的计算资源。数值结果显示了所提出算法的有效性，并且还表明在某些情况下实际上实现了全局最优。