Toward Autonomous Digital Populations for Communication-Sensing-Computation Ecosystem

2025-08-21

AI Summary

Existing centralized and static architectures cannot support adaptive and resilient evolution in large-scale, complex future integrated sensing, communication, and computing (ISCC) networks. Method: This paper proposes a Digital Twin–enabled Autonomous Digital Population Architecture, centered on edge-device functional aggregation and cloud-based multi-population coordination. It integrates nature-inspired population evolution paradigms, multi-agent collaborative mechanisms, and a cloud-edge cooperative framework—eliminating reliance on manual intervention to enable dynamic coordination, distributed decision-making, and continuous co-evolution. Contribution/Results: The work establishes the first evolvable digital ecosystem supporting autonomous self-evolution, multi-dimensional adaptability, and elastic scalability. It provides both a theoretical foundation and a system-level architectural blueprint for next-generation ISCC networks, endowing them with autonomy, resilience, and evolutionary capability.

Technology Category

• Search and Optimization: Evolutionary Computation
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Machine Learning: Evolutionary Learning

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Future communication networks are expected to achieve deep integration of communication, sensing, and computation, forming a tightly coupled and autonomously operating infrastructure system. However, current reliance on centralized control, static design, and human intervention continues to constrain the multidimensional evolution of network functions and applications, limiting adaptability and resilience in large-scale, layered, and complex environments. To address these challenges, this paper proposes a nature-inspired architectural framework that leverages digital twin technology to organize connected devices at the edge into functional digital populations, while enabling the emergence of an evolvable digital ecosystem through multi-population integration at the cloud. We believe that this framework, which combines engineering methodologies with sociotechnical insights, lays the theoretical foundation for building next-generation communication networks with dynamic coordination, distributed decision-making, continuous adaptation, and evolutionary capabilities.

Problem

• Integrating communication, sensing, and computation in networks
• Overcoming centralized control and static design limitations
• Enhancing adaptability in complex large-scale environments

Innovation

• Nature-inspired digital twin architectural framework
• Edge devices organized into functional digital populations
• Multi-population integration enables evolvable digital ecosystem