Value-of-Task-Driven Co-Design of Sensing and Transmission for Industrial Joint Monitoring

Abstract

In industrial joint monitoring, a large number of distributed sensors can provide rich measurement data for remote state estimation. However, limited network resources often introduce significant communication latency, which degrades the timeliness and reliability of estimation. Existing approaches typically optimize either information utility or transmission delay in isolation, failing to explicitly capture their coupled impact on estimation performance. To bridge this gap, we formulate a joint sensing-transmission scheduling problem and propose a novel Value-of-Task (VoT) metric, which captures the trade-off between information utility and transmission delay. Based on this metric, a VoT-driven sensing-transmission co-design framework is developed to adaptively optimize sensor selection and network resource allocation. Simulation results verify that the proposed method outperforms approaches that optimize either information utility or transmission delay alone. Moreover, it improves longterm estimation accuracy and enhances operational reliability in resource-constrained industrial wireless systems.