Massive Twinning to Enhance Emergent Intelligence
Siyu Yuan, Bin Han, Dennis Krummacker, and Hans D. Schotten

TL;DR
This paper proposes massive twinning in 6G to reduce data traffic and improve emergent intelligence performance in industrial IoT scenarios, addressing scalability and privacy concerns.
Contribution
It introduces a novel massive twinning approach to enhance emergent intelligence by leveraging 6G capabilities, reducing data traffic and improving scalability.
Findings
Massive twinning significantly reduces data traffic in EI.
Enhanced EI performance with improved scalability and privacy.
Applicable to 6G-enabled industrial IoT scenarios.
Abstract
As a complement to conventional AI solutions, emergent intelligence (EI) exhibits competitiveness in 6G IIoT scenario for its various outstanding features including robustness, protection to privacy, and scalability. However, despite the low computational complexity, EI is challenged by its high demand of data traffic in massive deployment. We propose to leverage massive twinning, which 6G is envisaged to support, to reduce the data traffic in EI and therewith enhance its performance.
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Taxonomy
TopicsIoT and Edge/Fog Computing · Molecular Communication and Nanonetworks · Modular Robots and Swarm Intelligence
