mlsanityData to 14 Oct 2026

Massive Twinning to Enhance Emergent Intelligence

arXiv 2204.09316 ↗First posted 20 Apr 2022cs.MA cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 3 of its 19 references that are in the map. First posted on arXiv 20 Apr 2022.

Builds on

3 of 19 references.

References · 3

  1. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  2. 20206G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunitiescited by 35
  3. 2017D2D-Based Grouped Random Access to Mitigate Mobile Access Congestion in 5G Sensor Networkscited by 1

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1.

2 papers.

  1. 2025A Survey on Agentic Service Ecosystems: Measurement, Analysis, and Optimizationcited by 1
  2. 2022Trust-Awareness to Secure Swarm Intelligence from Data Injection Attackcited by 0