In this work, we study the real-time tracking and reconstruction of an information source with the purpose of actuation. A device monitors the state of the information source and transmits status updates to a receiver over a wireless erasure channel. We consider two models for the source, namely an $N$-state Markov chain and an $N$-state Birth-Death Markov process. We investigate several joint sampling and transmission policies, including a semantics-aware one, and we study their performance with respect to a set of metrics. Specifically, we investigate the real-time reconstruction error and its variance, the cost of actuation error, the consecutive error, and the cost of memory error. These metrics capture different characteristics of the system performance, such as the impact of erroneous actions and the timing of errors. In addition, we propose a randomized stationary sampling and transmission policy and we derive closed-form expressions for the aforementioned metrics. We then formulate two optimization problems. The first optimization problem aims to minimize the time-averaged reconstruction error subject to time-averaged sampling cost constraint. Then, we compare the optimal randomized stationary policy with uniform, change-aware, and semantics-aware sampling policies. Our results show that in the scenario of constrained sampling generation, the optimal randomized stationary policy outperforms all other sampling policies when the source is rapidly evolving. Otherwise, the semantics-aware policy performs the best. The objective of the second optimization problem is to obtain an optimal sampling policy that minimizes the average consecutive error with a constraint on the time-averaged sampling cost. Based on this, we propose a \emph{wait-then-generate} sampling policy which is simple to implement.
翻译:在这项工作中,我们研究了实时跟踪和重建信息来源的实时跟踪和重建工作,目的是激活。一个设备监测信息源的状况,并将状态更新通过无线删除频道传送给接收器。我们考虑两个源的模型,即美元-州Markov链和美元-州出生-死亡Markov进程。我们调查了几个联合取样和传输政策,包括一个语义识别系统,然后我们研究了它们对于一套简单度量的性能。具体地说,我们调查实时重建错误及其差异、动作错误的成本、连续错误和记忆错误的成本。我们考虑的是两个数据源不同的系统性能模型,例如美元-州Markov链和美元-州出生-死亡Markov进程;我们调查了几个联合取样和传输政策,包括语义认知系统,然后我们研究了两个最优化的问题。第一个优化问题旨在将时间平均重建的误差降到最低程度,但有时间-平均成本约束。然后,我们比较了这个最佳随机随机政策,然后将我们最优的测序政策 显示我们最优的测算结果。</s>