Intelligent Emergency Evacuation System for Industrial Environments Using IoT-Enabled WSNs

Abstract

The Industrial Internet of Things (IIoT) uses smart sensors to monitor an industrial environment. These sensors transmit the data through wireless mediums and form wireless sensor networks (WSNs). However, industrial environments are prone to accidents like leakage of harmful gases, fires, and boilers bursting, which is very dangerous for the people working there. Existing emergency evacuation systems suffer from low response time, uneven distribution, and longer or less safe paths. This article presents an intelligent emergency evacuation system (IEES) using Internet of Things (IoT)-enabled WSNs. In this article, hybrid reinforcement learning (RL) and the multiobjective gray wolf optimization (MO-GWO) algorithm are proposed to optimize the evacuation path for each evacuee jointly. Initially, the hardware modules are uniformly deployed in the monitoring environment, and the optimal paths are identified using a RL algorithm. During an emergency, the hardware modules collect real-time data and transmit it to the gateway node for further processing. In addition, safety layers are formed near the hazardous region using the transformed pooling layers with the breadth-first search (TPOOL-BFS) emulations. Finally, optimal paths are computed using the MO-GWO algorithm to find the optimal path for each evacuee. Extensive simulations show that the proposed scheme outperformed the existing state-of-the-art algorithm. © 1963-2012 IEEE.