A Fire Evacuation and Control System in Smart Buildings Based on the Internet of Things and a Hybrid Intelligent Algorithm

Abstract

Concerns about fire risk reduction and rescue tactics have been raised in light of recent incidents involving flammable cladding systems and fast fire spread in high-rise buildings worldwide. Thus, governments, engineers, and building designers should prioritize fire safety. During a fire event, an emergency evacuation system is indispensable in large buildings, which guides evacuees to exit gates as fast as possible by dynamic and safe routes. Evacuation plans should evaluate whether paths inside the structures are appropriate for evacuations, considering the building’s electric power, electric controls, energy usage, and fire/smoke protection. On the other hand, the Internet of Things (IoT) is emerging as a catalyst for creating and optimizing the supply and consumption of intelligent services to achieve an efficient system. Smart buildings use IoT sensors for monitoring indoor environmental parameters, such as temperature, humidity, luminosity, and air quality. This research proposes a new way for a smart building fire evacuation and control system based on the IoT to direct individuals along an evacuation route during fire incidents efficiently. This research utilizes a hybrid nature-inspired optimization approach, Emperor Penguin Colony, and Particle Swarm Optimization (EPC-PSO). The EPC algorithm is regulated by the penguins’ body heat radiation and spiral-like movement inside their colony. The behavior of emperor penguins improves the PSO algorithm for sooner convergences. The method also uses a particle idea of PSO to update the penguins’ positions. Experimental results showed that the proposed method was executed accurately and effectively by cost, energy consumption, and execution time-related challenges to ensure minimum life and resource causalities. The method has decreased the execution time and cost by 10.41% and 25% compared to other algorithms. Moreover, to achieve a sustainable system, the proposed method has decreased energy consumption by 11.90% compared to other algorithms.

The applications of nature‐inspired algorithms in Internet of Things‐based healthcare service: A systematic literature review

“…Moreover, Mohammadiounotikandi et al 60 introduced a fire evacuation and control system designed for smart buildings, leveraging the IoT and a hybrid intelligent algorithm. Their system was developed to enhance fire safety measures through real-time monitoring and intelligent decision-making.…”

Experimental Study on Pedestrian Behaviors during Fire Emergency Conditions with Minecraft: Case Studies in a Classroom

“…In the event of a fire or other emergency incident, the absence of efficient evacuation management strategies may engender unforeseeable human and property losses [1]. Consequently, the precise and high-fidelity comprehension of pedestrian evacuation behavior during emergency situations assumes paramount significance for the formulation of effective evacuation management strategies, ensuring the safeguarding of human lives and the design of evacuation support systems, exemplified by evacuation signage [2], intelligent evacuation guidance systems [3], and secure egress pathways [4].…”

Combining MAS-GiG Model and Related Problems to Optimization in Emergency Evacuation

“…According to [16], the study constructs a BIM-FDS building fire simulation model to reveal the fire smoke dispersion law under the coupling of the typical building structure and fire protection systems. According to [17], the authors proposed a new way for a smart building fire evacuation and control system based on the IoT to direct individuals along an evacuation route during fire incidents efficiently. In [18], the co-effectiveness of stairwells, walkways, and room doors in reducing total evacuation time was investigated using simulation and machine learning.…”

“…The impact of fires on the evacuation process [17][18][19][20], and the characteristics and behavior of humans during evacuation [21], has been tested. In [22], the authors focused on evacuation plans for buildings with multiple escape routes based on a network model of indoor pathways.…”