Self-organizing hierarchies in sensor and communication networks

Area of Science:

• Aerospace Engineering
• Complex Systems Science
• Biomimetic Engineering

Background:

• Aerospace vehicles require advanced damage detection and response systems.
• Existing systems lack the adaptability for diverse impact energies and self-repair capabilities.
• Hierarchical multicellular networks offer a novel paradigm for robust sensing and communication.

Purpose of the Study:

• To investigate the self-organization of impact boundaries and networks in a hierarchical multicellular system.
• To identify emergent properties at different hierarchical levels for self-monitoring and self-repair.
• To quantify the spatiotemporal robustness of these self-organizing hierarchies.

Main Methods:

• Modeling a hierarchical multicellular sensing and communication network.
• Analyzing self-organization of impact boundaries and networks.
• Employing graph-theoretic and information-theoretic techniques (e.g., Shannon entropy).
• Investigating the role of memory (hysteresis) in emergent behaviors.

Main Results:

• Distinct higher-order emergent properties identified at each hierarchical level.
• Memory (hysteresis) is crucial for retaining desirable emergent behavior across levels.
• Quantitative measurements revealed phase transitions between chaotic and ordered dynamics.
• Self-organizing hierarchies demonstrate spatiotemporal robustness.

Conclusions:

• Hierarchical multicellular networks exhibit desirable emergent properties for self-monitoring and self-repairing aerospace vehicles.
• Memory is a key factor in achieving robust and ordered emergent behavior.
• Graph and information theory provide effective tools for analyzing complex adaptive systems.