With its growing number of deployed devices and applications, the Internet of Things (IoT) raises significant challenges for network maintenance procedures. In this work we address a problem of active fault detection in an IoT scenario, whereby a monitor can probe a remote device in order to acquire fresh information and facilitate fault detection. However, probing could have a significant impact on the system's energy and communication resources. To this end, we utilize Age of Information as a measure of the freshness of information at the monitor and adopt a semantics-aware communication approach between the monitor and the remote device. In semantics-aware communications, the processes of generating and transmitting information are treated jointly to consider the importance of information and the purpose of communication. We formulate the problem as a Partially Observable Markov Decision Process and show analytically that the optimal policy is of a threshold type. Finally, we use a computationally efficient stochastic approximation algorithm to approximate the optimal policy and present numerical results that exhibit the advantage of our approach compared to a conventional delay-based probing policy.
翻译:在这项工作中,我们处理IOT情景中主动发现错误的问题,通过这种方式,监测器可以探测远程装置,以获得新的信息,便利发现错误。然而,探查可能对系统的能源和通信资源产生重大影响。为此,我们利用信息时代作为监测器信息新鲜度的衡量尺度,并在监测器和远程装置之间采用语义识别通信方法。在语义识别通信中,生成和传递信息的过程被联合处理,以考虑信息的重要性和通信的目的。我们把这一问题发展成一个部分可观测的Markov决策程序,并分析性地表明最佳政策是一种临界类型。最后,我们使用一种计算高效的随机近似算法,以近似最佳政策,并呈现数字结果,表明我们的方法相对于常规的延迟调查政策而言具有优势。