Cloud Enabled Emergency Navigation Using Faster-than-real-time Simulation

TL;DR

This paper introduces a cloud-enabled, simulation-based evacuation routing system that iteratively optimizes paths for evacuees during disasters, reducing casualties by adapting routes in real-time based on simulated outcomes.

Contribution

It presents a novel cloud-based simulation framework that enhances emergency evacuation routing by iteratively refining paths using a combination of cognitive network algorithms and Dijkstra's algorithm.

Findings

Improved evacuation routing accuracy through simulation-based optimization.

Enhanced adaptability of evacuation plans in varying disaster scenarios.

Reduction in simulated casualties compared to traditional methods.

Abstract

State-of-the-art emergency navigation approaches are designed to evacuate civilians during a disaster based on real-time decisions using a pre-defined algorithm and live sensory data. Hence, casualties caused by the poor decisions and guidance are only apparent at the end of the evacuation process and cannot then be remedied. Previous research shows that the performance of routing algorithms for evacuation purposes are sensitive to the initial distribution of evacuees, the occupancy levels, the type of disaster and its as well its locations. Thus an algorithm that performs well in one scenario may achieve bad results in another scenario. This problem is especially serious in heuristic-based routing algorithms for evacuees where results are affected by the choice of certain parameters. Therefore, this paper proposes a simulation-based evacuee routing algorithm that optimises evacuation by making use of the high computational power of cloud servers. Rather than guiding evacuees with a predetermined routing algorithm, a robust Cognitive Packet Network based algorithm is first evaluated via a cloud-based simulator in a faster-than-real-time manner, and any "simulated casualties" are then re-routed using a variant of Dijkstra's algorithm to obtain new safe paths for them to exits. This approach can be iterated as long as corrective action is still possible.