# Integrating Decentralized Indoor Evacuation with Information Depositories in the Field
## 2 Related Work
### Evacuation Optimization  
Compared with evacuation models and crowd dynamics, evacuation optimization has received less attention from researchers. A recent review of optimization models for evacuation problems has been provided [15].  
The purpose of evacuation optimization is to improve the evacuation process, aiming at two objectives: reducing the number of deaths and accelerating the evacuation process [7]. Research efforts for evacuation optimization fall into two categories: either to improve the evacuation process by improving the environmental factors (e.g., optimizing the room structures, finding the optimal number, width and placement of doors), or by improving the path choice of evacuees [58].  
Optimization models gravitate to graphs to represent the paths available to pedestrian evacuees [59; 60]. A graph is defined by a set of nodes representing rooms or decision points, and edges representing doors, corridors, or other paths connecting the spaces represented by nodes [61]. Edges are associated with weights, which can be the distance between the connected nodes or the time required to travel between the connected nodes. A time-expanded graph includes a graph and states of nodes and edges at different time steps [59; 62]. Optimization of path choices has been studied either by optimizing the flow of pedestrians (or vehicles) based on evacuation graph, or by finding evacuation paths with minimum cost algorithms such as [PERSON]'s, A*, Ant Colony Optimization, D*, or D* Lite.  
For path planning, evacuees may either have the global knowledge of the graph or only have a limited view of the graph. All the knowledge is time stamped and may have become out of date due to the evolution of the disaster, thus introducing uncertainty to the knowledge. [PERSON] and [PERSON] [2] addressed the uncertainty of knowledge by developing a fading memory model which trusted less the older knowledge. They found that the fading memory model generally improved the evacuation process. However, such a model also poses a risk of devaluing knowledge that is still valid. Another research that considers the impact of the dynamics of disasters on the evacuation process was conducted by [PERSON] et al. [63]. They considered both the stationary environment during a normal situation and the predictable spatial change caused by the activated fire safety facilities during emergency situations, such as a fire rolling shutter that would be shut off during emergency situations.  
[PERSON] et al. [1] and [PERSON] et al. [62] addressed the evacuation optimization problem by suggesting a centralized evacuation framework incorporating multiple sensors and mobile phones. Such a centralized system carries out computations on a central server and makes decisions based on real-time awareness of the state of the environment, thus producing optimal evacuation paths. However, such centralized systems share a common shortcoming: They require system infrastructure and expect that their infrastructure is not affected by the disaster.  
In contrast to evacuation models aiming for realistic simulation of crowd dynamics, evacuation optimization aims to investigate efficient evacuation strategies, which is to be addressed in this paper. In particular, this paper focuses on evacuation strategies in a decentralized manner. Further, this paper prioritizes the number of successful evacuees over minimizing evacuation times, since not all the evacuees are guaranteed to be successfully evacuated.