# A Survey of Algorithms and Systems for Evacuating People in Confined Spaces
## 4 Conclusions and Challenges
### Emerging Challenges and Opportunities
The sunburst chart summarises the potential research directions from the system design, computing pattern, energy utilisation, behaviour modelling and optimisation, data analysis and cyber-security perspectives. future research can be directed toward optimising the setting of parameters from a systemic point of view by utilising proper queueing network models or diffusion models [116]. Differentiated service (DiffServ) mechanisms could be also developed to optimise the packet behaviours and satisfy the QoS requirements of different categories of cognitive packets that are associated with diverse classes of evacuees. Furthermore, parameter optimisation algorithms or even parameter-free algorithms can be developed by gaining experience from iterations of various simulated scenarios with the aid of machine learning as in other applications [167], including deep learning [168]. Compared with emergency drills, computer simulations are commonly utilised to investigate the effectiveness of evacuation wayfinding algorithms due to their time efficiency, repeatability and cost efficiency. However, many human behaviours during emergency evacuations have been ignored in the simulations. Hence, future research can also be directed to develop various human-computer interfaces for simulators to generate an artificial hazard environment with virtual reality and augmented reality technologies [7]. By evacuating volunteers from the artificial hazard environment, their emergent behaviours can be collected and empirical collective behaviour models of human beings can be developed from the actual crowd measurements. However, this will require extensive validation and further studies. One trend in emergency management is to develop artificial intelligence-aided algorithms to improve path-finding and resource allocation during standard or emergent evacuations. However, as a typical category of cyber-physical systems, the intelligence of evacuees, as well as possible pro-social behaviours such as helpfulness and a sense of duty have been excluded in the previous algorithms. As a result, the robustness of these algorithms cannot be ensured due to the high likelihood that evacuees do not follow the instructions. Moreover, unnecessary efforts have been dedicated to the use of AI, while in fact, many tasks can be easily accomplished by evacuees in the system via using human intelligence. For instance, the identification of a fire source location could take significant effort when using AI-based algorithms, which involves the installation of cameras and the use of computer vision-related technologies. However, it would take no time or cost for evacuees to identify a fire and report to authorities or the emergency response system by using smart phones. In attempting to incentivise evacuees to conduct cooperative behaviours and avoid destructive behaviours, one future research direction could be dedicated to the investigation of reward mechanisms to integrate the human intelligence of evacuees into the emergency response systems to improve the efficiency, adaptiveness and robustness of these systems. Thus, the link between human psychology and emergency response will require much further work in the coming years. On the other hand, since most of the previous multi-robot rescue systems only followed simple coordination rules and lacked explicit teamwork models or goals [169], multi-agent technologies can also be utilised to model and develop various cooperative strategies, with the aid of queueing network models such as G-networks [170; 171] or genetic algorithms [138; 172]. The rapid development of cloud technologies has facilitated the advancement of cloud-enabled evacuation wayfinding systems that consist of front-end portable devices and back-end cloud severs.