Demonstrating Software Reusability: Simulating Emergency Response Network Agility with a Graph-Based Simulator

Abstract

This research validates the re-engineering of a neural network simulator to implement other graph-based scenarios. Most of the simulator’s components were abstracted to increase reusability and maintainability through strategic refactoring decisions. This paper demonstrates how the simulator, developed at the University of Washington Bothell, can be adapted for other graph-based problems. By separating the neurospecific components from the core architecture of the simulator, this research verifies its functionality as reusable software. The scenario used to test the new architecture is the resilience of the US’s Next-Generation 911 (NG-911) system in the face of a crisis. Existing research acknowledges that both neural networks and emergency response networks are complex networks that exhibit self-organizing behavior. Initial results from this small-scale test-case demonstrate that when a crisis destroys critical parts of emergency response infrastructure, NG-911 can reroute calls to keep communities connected with resources. This supports the conjecture that self-organizing patterns will emerge from the interconnected events of a full-scale network simulation. The success of this configuration provides evidence that the simulator can serve a broad spectrum of graph-based scenarios. Its growth potential is further substantiated by the simulator’s improved long-term maintainability and overall software quality.