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State-of-the-art emergency navigation approaches are designed to evacuatecivilians during a disaster based on real-time decisions using a pre-definedalgorithm and live sensory data. Hence, casualties caused by the poor decisionsand guidance are only apparent at the end of the evacuation process and cannotthen be remedied. Previous research shows that the performance of routingalgorithms for evacuation purposes are sensitive to the initial distribution ofevacuees, the occupancy levels, the type of disaster and its as well itslocations. Thus an algorithm that performs well in one scenario may achieve badresults in another scenario. This problem is especially serious inheuristic-based routing algorithms for evacuees where results are affected bythe choice of certain parameters. Therefore, this paper proposes asimulation-based evacuee routing algorithm that optimises evacuation by makinguse of the high computational power of cloud servers. Rather than guidingevacuees with a predetermined routing algorithm, a robust Cognitive PacketNetwork based algorithm is first evaluated via a cloud-based simulator in afaster-than-real-time manner, and any "simulated casualties" are then re-routedusing a variant of Dijkstra's algorithm to obtain new safe paths for them toexits. This approach can be iterated as long as corrective action is stillpossible.

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