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

Abstract

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.

Joint Optimization for Pedestrian, Information and Energy Flows in Emergency Response Systems With Energy Harvesting and Energy Sharing

“…The comprehensive optimisation of different resource flows in a built environment has also drawn growing attention in the recent years. For instance, the study in [42] proposes an edge-enabled emergency management framework to optimise the information flows and energy flows in public transportation junctions during an evacuation process; the movement directions of evacuees are also optimised for safety purpose but the possible harvesting of human kinetic energy is not taken into consideration. In summary, little research has applied energy efficient operations or EH technologies to ERS.…”

Self-Administered Information Sharing Framework Using Bioinspired Mechanisms

“…Our energy model considers energy consumption during communications and computations. e energy consumption during sensing and communications processes is borrowed from [57], while the energy utilisation of CPU is predicted by an analogy of the code-transformation algorithm introduced in [56]. By using this algorithm, we can calculate the CPU active time with regard to the CPU frequency and the recorded total CPU cycles during the simulation.…”

An Agent-Based Dynamic Framework for Population Evacuation Management

“…• Network clearance time: It is defined as the arrival time of the last evacuee to the shelter or safety zone [6]- [8]. • Total evacuation time: It denotes the sum of the evacuation time of all evacuees [5], [9], [10].…”