State-of-the-art emergency navigation approaches , which aim to evacuate civilians during a disaster , commonly make decisions in a real-time fashion with respect to one pre-defined algorithm and living sensory data. Hence, fatalities caused by the inappropriate guidance of an approach can only be unveiled until the end of an evacuation process and is impossible to be remedied. Previous research implies that the performance of routing algorithms for evacuation proposes are sensitive to  initial distribution of evacuees, occupancy rate, disaster type as well as disaster location. In other words, a well performed algorithm in one scenario may achieve bad results in another scenario. This problem is especially serious in heuristic-based routing algorithms  where results are affected by the configuration of certain parameters. Therefore, this paper proposes a simulation-based routing algorithm to realise near-optimal evacuations by making use of the high computational power of cloud servers. Rather than guiding evacuees with a routing algorithm directly , a robust Cognitive Packet Network based algorithm is first evaluated via a cloud-based simulator in a faster-than-real-time manner . Towards all the perished evacuees in the simulation, a variant of Dijkstra's algorithm is then employed to solve optimal paths for them and all the evacuees will finally follow the desired paths to exits. Furthermore, the "tsunami of data" phenomenon caused by simultaneous information exchanges among massive mobile devices and cloud servers is avoided as the proposed algorithm can calculate desired paths only based on the initial situation .
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 .
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