Integrated Communication and Control Systems: A Data Significance Perspective

TL;DR

This paper explores semantic scheduling strategies for integrated communication and control systems, emphasizing data significance to improve estimation accuracy in resource-constrained, time-critical IoT environments.

Contribution

It introduces novel algorithms for semantic scheduling that analyze data significance and derive bounds on estimation accuracy, demonstrating their advantages over traditional methods.

Findings

Semantic scheduling improves estimation accuracy.

Integrated control and communication policies outperform traditional approaches.

Proposed algorithms reduce weighted mean squared errors.

Abstract

The interconnected smart devices and industrial internet of things devices require low-latency communication to fulfill control objectives despite limited resources. In essence, such devices have a time-critical nature but also require a highly accurate data input based on its significance. In this paper, we investigate various coordinated and distributed semantic scheduling schemes with a data significance perspective. In particular, novel algorithms are proposed to analyze the benefit of such schemes for the significance in terms of estimation accuracy. Then, we derive the bounds of the achievable estimation accuracy. Our numerical results showcase the superiority of semantic scheduling policies that adopt an integrated control and communication strategy. In essence, such policies can reduce the weighted sum of mean squared errors compared to traditional policies.