Cooperative and Energy-Efficient Strategies in Emergency Navigation Using Edge Computing

Nowadays, public transportation junctions (PTJs) such as metro stations and railway stations can involve up to half of urban traffic and account for a large portion of energy usage in urban areas. The tremendous traffic and energy flows in PTJs have induced severe problems in safety and efficiency. Thus, in this paper, we present a comprehensive edge computing enabled navigation framework to aid the emergent evacuation processes within PTJs with respect to energy and safety restrictions. From the energy-saving aspect, due to the resource restraints of the on-site Internet of Things (IoT) based environmental monitoring system, a queueing network model is utilised to balance the energy utilisation and reduce the congestion of the sensing and navigation process during emergency. From the safety aspect, three edge computing aided cooperative strategies are proposed to dynamically assign evacuees into several groups to adapt their course of action with regard to their physical conditions and immediate environments. Simulation results show that the use of the queueing network model can reduce and balance the energy utilisation of the on-site IoT network. Experiments also show that the use of cooperative strategies to adjust the evacuees' category and the associated routing algorithm can achieve higher survival rates.