# A Survey of Algorithms and Systems for Evacuating People in Confined Spaces
## 3 Evacuation Wayfinding Algorithms
### On-Line Evacuation Wayfinding Algorithms
#### 3.2.4 Potential-Maintenance Algorithms  
Potential-based algorithms normally can dynamically develop navigation paths by assigning attractive or repulsive potentials to the exits and hazards, and the evacuees move as a result of the net attraction-repulsion in various directions. For instance, the research in [20] presented a self-organizing sensor network to guide users such as robots, evacuees or unmanned vehicles out of a hazardous environment along the safest paths by using \"artificial potential fields\" [21]: when a sensor detected a hazard, it broadcast emergent messages including sensor ID, number of hops from the arrived sensor (\(N_{h}\)) to other sensors; when a sensor received multiple emergent messages from the same hazardous sensor, it would keep the smallest \(N_{h}\); the potential value of a sensor generated by a hazardous sensor was calculated by \(\frac{1}{N_{h}^{2}}\); hence, the overall potential value of the sensor was computed by summing the potential value generated by each hazardous sensor; in this way, an attractive force was generated by the destination to pull the user, while repulsive forces were generated by the dangerous zones to push the user away from them; the safest path was generated by following the most descending direction of the potential field; experiments were conducted on a test bed with 50 Mote MOT300 sensors [133], and the results indicated that the algorithm could successfully direct the objects to the destination; however, multiple destinations may have a negative impact on the efficiency of reaching the exits as the users move under the actuation of artificial forces.  
Moreover, the convergence time for network stabilization was relatively long due to the effect of data loss, asymmetric connection and network congestion. The study in [18] proposed a temporally-ordered routing algorithm-based [19] multi-path routing protocol to route evacuees to exits through safest paths; a navigation map was manually defined during the deployment process to avoid impractical paths; in the initialisation phase, each sensor was assigned with an altitude with respect to its hops to the nearest exit: sensors nearer to the exits were assigned with smaller altitudes, while sensors farther from the exits were allocated with larger altitudes; when an emergency event was detected, a sensor \(s_{i}\) within the hazardous regions would update its altitude in terms of the altitude of \(s_{i}\) before and after the update, the shortest hop distance between \(s_{i}\) and the hazardous sensor and the shortest hop distance between \(s_{i}\) and the exit; a hazardous region was constructed by sensors within a predefined hop distance from the hazardous sensor; egress routes were generated from sensors with a higher level from the ground; therefore, more elevated hazard sensors could ensure evacuees bypass the hazardous regions. The work in [134] extended the algorithm in [18] to a 3D environment and divided the sensors into normal sensors, exit sensors and stair sensors in terms of location; if no available path to exits could be discovered, evacuees would be directed to rooftops and wait for rescue. However, multiple destinations (exits) may affect the efficiency of reaching the exits as the users moved under the actuation of artificial forces. Moreover, the convergence time for network stabilization was relatively long due to the effect of the information synchronization, asymmetric connection and network congestion.