Observability Transitions in Networks with Betweenness Preference

Area of Science:

• Network science
• Complex systems analysis
• Information theory

Background:

• Network observability is crucial for state determination.
• Current sensor placement strategies often rely on local network information.
• Understanding observability transitions is key to network analysis.

Purpose of the Study:

• To analyze network observability transitions using a betweenness-based sensor placement strategy.
• To compare the effectiveness of betweenness-based placement against random and degree-based strategies.
• To investigate the relationship between network topology and observability.

Main Methods:

• Numerical simulations were employed to study network observability.
• The size of the largest observable component (LOC) was computed.
• Observability transitions were analyzed for different sensor placement strategies.

Main Results:

• Betweenness-based sensor placement yields a larger LOC during observability transitions.
• This improved performance was observed in both model and real-world networks.
• The strategy outperforms random and degree-based sensor placement methods.

Conclusions:

• Betweenness centrality is an effective metric for sensor placement to maximize network observability.
• The findings highlight the importance of global network topology in observability.
• This research provides insights into optimizing sensor networks for state monitoring.