# A Survey of Algorithms and Systems for Evacuating People in Confined Spaces
## 3 Evacuation Wayfinding Algorithms
### On-Line Evacuation Wayfinding Algorithms
#### 3.2.6 Routing Protocol-Based Algorithms
Since Oppcomms are susceptible to malicious attacks such as flooding or denial of service, an extended study [149] proposed a defence mechanism that used a combination of identity-based signatures (IBS) and content-based message verification to detect malicious nodes. However, network routing protocol-based algorithms normally make decisions based on the collected sensory information rather than the predicted situation of a path. Therefore, when evacuees traverse that path, the situation could have changed owing to the highly dynamic nature of an evacuation process, which normally induces a delayed feedback loop between living sensory data and routing decisions. Similar to [97], the research in [98] also borrowed the concept of the cognitive packet network to calculate evacuation paths for evacuees with the aid of an on-site WSN in a distributed manner; however, rather than using the m-sensible routing algorithm, the random neural network (RNN) [150; 151; 152] and its learning algorithm [153] were used as in decision-making for the SPs to explore the environment; each sensor node in the WSN was considered as a CPN node, in which an RNN was deployed to direct the passing-by SPs and a mailbox was used to store the discovered paths and the associated QoS measurements; the RNN consisted of neurons that were associated with each potential forwarding direction of SPs; each neuron possessed an excitation probability to indicate the quality of the forwarding direction, and the neuron with the highest excitation probability corresponded to the optimal forwarding direction; when an evacuation process began, CPN nodes continuously sent out SPs or relay SPs from other CPN nodes; when a SP reached a CPN node, it could either select the forwarding direction corresponding to the most excited neuron or drift randomly to search for new routes; as a SP arrives at an exit, an ACK would be generated to backtrack the discovered route in a loop-free manner; when an ACK reaches a CPN node, the training process of the local RNN would be triggered, and the excitation probability of each neuron would be updated based on the learning mechanism of the RNN; the discovered routes would be stored in the local mailbox and sorted by quality; evacuees in the vicinity of a sensor node always would be transferred the top-ranked route as their evacuation route; since each SP could gain \"experience\" from previous SPs, the CPN could rapidly discover the optimal or near-optimal evacuation routes by emitting very few packets [154]. The studies in [99; 155] extended the work in [98] and made use of the feature of CPN to develop a multi-path routing algorithm for different categories of evacuees (prime-aged people, aged people, children or ill people and disabled people in electric-powered wheelchairs) with respect to their specific requirements; since each SP could search a distinct path based on its pre-defined goal function, during the evacuation process, various types of SPs were sent out to search distance-oriented paths, time-oriented paths, safety-oriented paths and energy efficiency-oriented paths for the associated evacuees. On top of the work in [155], the work in [156] designed a cooperative strategy that divided evacuees into health-oriented evacuees and evacuation time-oriented evacuees and could adjust the routing strategy of evacuees when their \"virtual health value\" fulfilled a certain condition; the use of the strategy was proven to be more sensitive and adaptive to sudden changes in the hazard environment such as abrupt congestion or injury of civilians.