More Tolerant Reconstructed Networks Using Self-Healing against Attacks in Saving Resource

Area of Science:

• Complex network infrastructure systems
• Network resilience and sustainability

Background:

• Critical infrastructure networks (power, communication, transport) are vulnerable to disasters and attacks.
• Traditional recovery methods often fail to address underlying vulnerabilities.

Purpose of the Study:

• To propose a novel network reconstruction strategy focusing on enhancing loops for improved sustainability.
• To develop self-healing methods for network repair inspired by statistical physics.

Main Methods:

• Implemented node removal to transform tree-like structures into enhanced loops.
• Utilized approximate calculations inspired by statistical physics to address the combinatorial optimization problem.
• Developed self-healing algorithms for adaptive network reconstruction.

Main Results:

• Achieved higher network robustness and efficiency compared to conventional healing methods.
• Demonstrated resource savings in links and ports through optimized reconstruction.
• Showcased increased network tolerance, especially when damaged links are reusable or compensated.

Conclusions:

• Network reconstruction using self-healing with adaptive capacity offers significant potential for enhancing resilience.
• The proposed methods provide a sustainable alternative to empirical recovery of vulnerable network infrastructures.