Towards Goal-Oriented Semantic Signal Processing: Applications and Future Challenges

TL;DR

This paper introduces a formal graph-based semantic language and goal filtering method to enable goal-oriented signal processing, enhancing real-time semantic information extraction for diverse applications and sensor networks.

Contribution

It presents a novel semantic signal processing framework that is adaptable for various applications and addresses semantic communication in sensor networks.

Findings

Demonstrated a graph-based semantic language for signals.

Proposed goal filtering for goal-oriented processing.

Applied framework to multiple use cases.

Abstract

Advances in machine learning technology have enabled real-time extraction of semantic information in signals which can revolutionize signal processing techniques and improve their performance significantly for the next generation of applications. With the objective of a concrete representation and efficient processing of the semantic information, we propose and demonstrate a formal graph-based semantic language and a goal filtering method that enables goal-oriented signal processing. The proposed semantic signal processing framework can easily be tailored for specific applications and goals in a diverse range of signal processing applications. To illustrate its wide range of applicability, we investigate several use cases and provide details on how the proposed goal-oriented semantic signal processing framework can be customized. We also investigate and propose techniques for communications where sensor data is semantically processed and semantic information is exchanged across a sensor network.