Innovation based transmission in AI-enabled sensor networks for 6G IoT scenario

Abstract

Goal-oriented signal processing and communications are assured to play a key role in developing the next generation of sensor devices and networks, e.g., 6G IoT networks. A critical task of semantic signal processing is the detection of innovation and transmission scheduling based on the innovation in AI-enabled sensor networks and IoT for 6G. This thesis proposes efficient and optimal sampling and transmission strategies for goal-oriented sensor networks for various data models, investigates their performances both analytically and numerically; and introduces the use of dimensionality reduction algorithms in semantic signal processing and highlights its effectiveness in real life case studies. That is, the proposed methods are explained rigorously and demonstrated through simulations and case studies based on real-world computer vision examples with recorded video signals. Numerical results indicate that the next generation sensor devices and networks can benefit significantly from the proposed methods in terms of energy efficiency and semantic innovation detection performances.