## 2 Literature Review  
In the paper, we investigate the resource allocation problem for emergency management, which covers the areas of evacuation navigation, distributed simulation, analytical performance modelling and the RNN-based algorithms. We discuss the related work in each of these areas below. A distributed navigation algorithm proposed in [6] is applied in mobile networks to guide people to a exit safely during an emergency with a 2D layout, and the work is extended to a 3D indoor environment in [7]. In [8] is presented a distributed algorithm to find safe paths for evacuees taking into account predictions of the movement of the hazards, but there are some shortcomings addressed in [9]. The distributed building evacuation simulator used in our simulations is firstly proposed in [10] and particularly in [11], and it can reduce the simulation time required for large-scale systems and facilitate the modelling of the agents. In [12, 13] is discussed the use of analytical models based on graph and queueing theories, which provide fast estimates for the conditions in frequently changing circumstances. The authors in [2] present a distributed decision support system for building evacuation based on the distributed simulator. Regarding evacuating civilians quickly and safely, [14] illustrates that routing emergency evacuees with a cognitive packet network (CPN) [15, 16, 17] can alleviate slow convergence time for large and fast-changing networks, and [18] proposes the use of Opportunistic Communications (oppcomms), which can provide emergency support when a stable communication infrastructure is unavailable. The above work attempts to direct the evacuees to get out of danger quickly and avoid injury; however, people might be seriously injured and obstructed by the hazards in real emergency environments. Thus, a system that can efficiently search for the injured civilians and coordinate rescue is necessary for emergency management. In [19] is designed efficient and intelligent algorithms to search for the mines autonomously in static environments. In [20, 21], the authors address a robot-sensor network to identify the locations of possible victims for providing rescue of those trapped people. A distance-sensitive service discovery algorithm is illustrated in [22], which combines the concepts of Voronoi polygons [23] and the quorum-based approaches [24] and guarantees the closest tasks (e.g., victims) being executed with very high probability. By considering the energy issue, [25] explores two algorithms to dispatch mobile resources (e.g., emergency units) to visit task locations in an energy-balanced way. Furthermore, [26] takes into account the situation where people may be trapped with no escape path, due to congestion, and designs a scheme for allocating resources to eliminate key dangerous areas in order to ease congestion and increase the number of rescued people. Furthermore, the authors in [27] consider that a resource may fail in executing a task in dynamic hazard environments and address a task assignment algorithm with a probabilistic successful execution outcome. The algorithm is on the basis of the RNN techniques, so that it is able to solve the resource allocation problems accurately and in real-time. The random neural network is powerful in obtaining desired input-output mappings through learning and has been widely used in solving real world problems. In [28], the research work based on the RNN techniques is surveyed, and the properties and applications of it are described. The author shows that the RNN has been applied into diverse engineering areas, which reflects its prominence as a modelling and learning tool. The theory and applications of the RNN are introduced below. The excitatory and inhibitory signals concept of the RNN is first introduced in [29], where each neuron has an active potential, which will be affected by the input signals from other neurons and the outside world.