Integration of Industry 4.0 Technologies in Fire and Safety Management

Abstract

The incorporation of Industry 4.0 has integrated various innovations into fire safety management, thus changing the mode of identifying, assessing, and controlling fire risks. This review aims at how emerging technologies like IoT, AI, cloud technology, and BIM are making changes to fire safety in structural structures. With IoT-enabled sensors, data, and analytics coupled with predictive algorithms for real-time scenarios, fire safety systems have become dynamic systems where early detection, quick response, and risk management can be achieved. In addition, cloud web-based solutions improve the storage of information while providing the predictive aspect for certainty of safety measures. This paper also largely focuses on such activities through the likes of ISO/IEC 30141 and IEEE 802.15.4, thus making a critical role in maintaining effective connectivity between IoT devices, which is necessary for the effective performance of fire safety systems. Furthermore, the implementation issues, including the high costs, the difficulty in scaling up the projects, and the cybersecurity concerns, are considered and compared to the possible solutions, which include upgrading in stages and the possibility of subsidies from the government. The review also points out areas for further study, such as the creation of small cell networks with lower latency, the use of AI to carry out the maintenance of IoTs, and the enhancement of protection mechanisms of systems that are based on the IoTs. In general, this paper highlights the vast possibilities offered by Industry 4.0 technologies to support organizational fire safety management or decrease fire fatalities and improve built environment fire safety.

1. Introduction

A little negligence and poor administration can cause a huge calamity and claim a great deal of human life [1]. A fire hazard is the potential for an accidental or planned fire to threaten both lives and property. So, a proper fire management system is needed in the living and working environment of the human [2]. “Fire safety” describes a set of protocols intended to restrict the amount of property damage caused by deliberate or inadvertent fires, and to minimize their spread and effects [3]. The most basic fire management system is installed in buildings, homes, workplaces, and industries; however, these systems are antiquated or sluggish to operate. The efficiency of existing fire safety measures is eroding with time. In recent times, there has been a noticeable shift in the severity and diversity of fire hazards in buildings due to fast global expansion [4]. As a result, there is increased worry about these hazards. The integration of developing technology to enhance firefighters’ situational awareness is not fully utilized in the current firefighting plan.

With the onset of modernization and the implementation of Industrial Revolution 4.0, rapid digitalization is taking place in almost every sector, including the construction sector. The idea of smart towns and cities, smart infrastructures, and smart houses is on the horizon. Standard living facilities with advanced fire safety features are key points in the smart building concept [5]. Integrating Civil Engineering with IR 4.0 is making all these possible. Fire safety management of residential as well as commercial structures is one of the major attributes of smart buildings. In 2015, the United Nations members adopted the Sustainable Development Agenda 2030, which planned for peace and prosperity for the people in upcoming years. The 17 Sustainable Development Goals, which are aimed to be achieved by 2030, were the major emphasis of this agenda. Fire safety and management in buildings fulfill SDGs 9 and 11 of the UN Agenda 2030 [6].

Design of the modern cities, homes, and buildings includes safety against fire and explosion [7]. As safety is essential to both human survival and property protection, safety from fire is a crucial concern in the modern built environment sector. Planning, designing, and maintaining residential properties should minimize the likelihood of a fire occurring or, if it does, minimize the amount of damage. Therefore, to minimize hazards and attain a suitable degree of safety, design experts and managers of residential facilities must be completely aware of the fire safety rules [8]. This digitalized era helps provide a key advantage of fast detection and transmission of data of any threat to the concerned authority. Infrastructures may be effectively controlled and monitored by utilizing various Industry 4.0 innovations according to their specific qualities and maintaining a high-level safety management system [9]. To analyze this study, the following issues were explored:

• How does the use of contemporary digital technologies in Industry 4.0 contexts improve fire and safety management in indoor and constructed environments? (Findings in Section 4).
• What future work can be conducted based on the current challenges faced? (Findings presented in Section 5).

Global Definition of Industry 4.0: Perspectives from China and the U.S.

Industry 4.0 covers manufacturing and industrial systems, focusing on the disappearance of the cyber world and physical systems, IoT, big data, and AI for enhancing productivity [10]. Even though it is possible to stabilize the key notions of Industry 4.0 that are shared globally, the definition and execution of it may not be the same across each country, particularly the developed industrial countries such as China and the U.S.

In China, Industry 4.0 is in synergy with the government’s “Made in China 2025” strategy that focuses on modernizing the nation’s manufacturing industry through digitization. Chinese manufacturers remain committed to the use of artificial intelligence, robotics, and Internet of Things to increase efficiency and output and to reduce risks [11]. The future real-time monitoring and predictive maintenance systems have paved the way for fire safety management in Chinese industries including manufacturing and logistics, which fall under high-risk Industry 4.0 technologies.

Conversely, the U.S. is much more liberal and going for a more diffused approach to Industry 4.0, much to the technological and innovation origination by the private firms rather than the central government. The U.S. leads the world in incorporating big data and analytics, automation of cloud services, and machine learning into the actual mechanical structures [12]. Concerning the aspect of fire safety management, the US concentrates on the use of big data and the IoT to estimate the occurrence of fire disasters in manufacturing, oil and gases, and public structures. They include fire risk prevention as well as fire department and industrial safety team coordination technologies for a proactive approach to fire risks.

While comparing the business environment in both countries, it can be stated that Industry 4.0 has a central importance to the modernization of fire and safety management via real-time, timely, and automatic detection and suppression. However, the implementation strategies that are described highlight aspects of industrial priorities and the system of governance in each country. It is important to understand these differences from one country to another to come up with mechanisms for fire safety that could be implemented in countries with different industrial practices.

One risk that has the potential to cause enormous loss of lives, possessions, and assets is fire. In today’s sophisticated and technological world, everyone prioritizes safety as a fundamental necessity. Even though there are several fire safety management systems, they are still outdated [13]. While newly constructed houses and buildings have well-planned safety management systems, older structures still lack them. To avoid catastrophic events, high-rise buildings need to have an adequate evacuation plan or system in place to deal with any kind of fire.

The inefficiency of the current fire management system can be attributed to several issues, but Industry 4.0 digital technology can help make it better [14]. The delay in relaying news or alarms to the firefighting department is one of the most difficult problems in today’s fire and safety management. Only when a fire has spread significantly is a warning or alarm sent to the concerned department; up until that point, the area will have suffered significant harm from the fire, including damage to property and even fatalities. To address this latency issue, Industry 4.0 tools have proved quite beneficial. Many fire monitoring systems have been developed, and by spotting even the remote possibility of a fire break, they can provide the appropriate authority and users with early notice [15]. If a fire breaks out, a typical home’s infrastructure lacks the necessary firefighting tools and systems. People’s ignorance and awareness of the tools and methods for putting out tiny fires can cause small fires to become hazards [16]. Faster identification of individuals in burning buildings is crucial in building fire emergency response efforts. The findings showed that indoor location information was one of the most required features when managing building fire events. According to fire statistics, the U.S. experienced 484, 500 building fire incidents in 2011 with a toll of 2460 deaths and 15,635 injuries [17]. First responders act as the initial team that intervenes in building fire emergencies, and one of their critical responsibilities is to find and search for trapped individuals in the buildings. Often, the first responders are not aware of the environment and the location of trapped occupants, coupled with the information that might help in the proper planning of a search route, time, and task force assignments. However, the first responders are required to conduct a thorough search of any indoor spaces that may contain people who are stuck. This is usually a blind and inefficient form of search, which significantly raises the possibility of death and injury of the trapped occupants [18]. Further, for incident commanders who are put in charge of commanding and coordinating operations during emergencies, it is critical to have efficient knowledge of the location of used first responders for safety. As a result, first responders who are not familiar with a certain environment are prone to suffer from secondary casualties, especially when they are called out from their jurisdictions to attend to mutual-aid emergencies. Due to the rise of new and big buildings and less live fire training compared to twenty years ago, first responders’ fire death rate has doubled inside structures, and the primary reason for their death is getting lost or trapped. Identifying the current location of first responders within buildings could minimize such risks and consequently advance emergency response ventures [19].

Industry 4.0 technologies have a high impact on nearly every area and sector of the globe; therefore, incorporating them into fire safety management has the potential to significantly alter the field. With this motivation, a thorough study is conducted to determine the effect of using modern tools for fire and safety management in the built environment and how they help in attaining SDGs to make a sustainable and safe environment for humans.

The key contributions of the study are as follows:

• The study covers various demands of fire and safety management in modern indoor and built environments from the available kinds of literature.
• Examines how the various tools of industry 4.0, i.e., IoT, cloud computing, big data, AI/ML, Edge/Fog, Blockchain, and metaverse deployment in fire and safety management have aided.
• The study also identifies several difficulties and makes some recommendations for further improvements to fire safety management.

The study is organized as follows: The overview of the industry 4.0 technologies used in fire and safety management in indoor and built environments is covered in Section 2. Section 3 elaborates on various related work of Industry 4.0 technologies in fire and safety management. Section 4 discusses some recommendations related to technologies in fire and safety management; based on these, suggestions are provided to improve it. Conclusions including findings, direction, and contributions are provided in Section 5 of this study.

2. Overview of Industry 4.0 Technologies

Industry 4.0 technologies like IoT, cloud computing, big data, artificial intelligence, edge computing, Blockchain, metaverse, and Building Information Modeling, which are used in fire safety management, have been discussed in this study. Figure 1 represents the key technologies that are being widely used for enhancing fire safety management procedures. Also, Table 1 presents recent studies on the technological advancement of various technologies in the proper management of fire and safety procedures.

The Internet of Things (IoT) is the latest example by which daily things are networked and communicated with one another via the Internet. The Internet of Things makes it feasible to integrate physical objects seamlessly with the digital world via smart sensors, RFID tags, phones, and wearable technologies. IoT networks provide a variety of application domains, including environmental monitoring, healthcare, smart cities, military affairs, and intelligent transportation systems [20]. An IoT system comprises a smart system (programs, control devices, sensors, and features like security), a data transmission system (cloud computing, edge computing, fog computing, etc.), a data analytics system (to protect business judgment and enhance decision making), and the human element [21]. Cloud computing refers to both the hardware and operating system software in data centers, which make certain facilities as well as the apps that are offered as services available via the Internet. Cloud storage, cost savings, worldwide remote access, and simplified IT infrastructure and management are just a few of the numerous advantages of cloud computing that are encouraging businesses to use it [22]. Although cloud computing is not a recent development in technology, it is becoming increasingly well-known as a significant and effective influence behind the change in how we maintain data and how we use, access, and provide information services [23]. Society has transformed due to its ability to process massive volumes of data and generate insightful knowledge from it. Big data, a concept, has been used in many different industries, including the construction sector [24]. The rise of both structured and unstructured data quantities is a continuing result of information technology advancement (big data) [25]. Petabytes or exabytes of vast amounts of data are referred to as big data, which are utilized for analytics and research to enhance the quality of life for everyone. It encompasses a wide range of domains and sectors within the technology industry, leading to a multitude of interpretations [26].

The Fourth Industrial and Scientific Revolution, which is focused on using computational techniques to represent human intelligence, is primarily driven by artificial intelligence. It has several applications, which at present mainly focus on skills like voice and picture recognition, logical reasoning, and emotion identification [27]. The ability of a computer system to reduce human labor involved in learning and problem-solving is known as artificial intelligence. Its algorithms are used to match news articles, identify objects in images, transcribe speech to text, forecast user interest, and extract pertinent information from online searches [28]. The term “machine learning algorithm” refers to an algorithm that uses input data to accomplish work without being specifically programmed (i.e., “hard programmed”) to do so. Since these algorithms automatically modify or adapt their design as a result of repetition, it is possible to refer to them as “soft programmed”, meaning that they get better and better at performing the desired goal [29]. Edge computing is a new, modern paradigm for computing that carries out computation near the edge of the network. Moving computation closer to the location of the data source is its fundamental objective [30]. The term used to refer to these technologies is known as edge computing, which refers to the computation of downstream data for cloud services and upstream data for Internet of Things services at the edge of the network [31]. Blockchain is one of the groundbreaking technologies that can make massive changes within almost every sector. The following benefits can be attributed to blockchain technology: reduced cost of transactions, more flexibility, and protection against data fraud and alterations [32]. Blockchain is a number of combined blocks that eliminate the possibility of falsification and destruction and ensure the immutability of records in each of them [33]. The metaverse is an undefined and always-open multiple-user environment in which physical reality and digital virtuality interpenetrate. It is based on the technologies of applying multiple modalities in interaction with virtual objects, virtual environments, and virtual people [34]. The international standards define Building Information Modeling (BIM) as “a digital representation of any built object’s geometry and supporting relational data—as well as shared in a structured electronic format that integrates with established business processes”. BIM is currently in the process of revolutionizing the construction design, delivery, and operational standards as well as design practice modes. Originally derived out of object-based parametric modeling, BIM has evolved to comprise software tools that are widely used in the AEC industries [35].

Table 1. Recent studies on the technological advancements in the management of fire and safety procedures.

S.No.	Technology	Findings	Limitations	References
1	Blockchain Technology	Record Maintenance of the safety management system. The implementation of blockchain technology guarantees enhanced transparency in safety protocols and the safekeeping of vital documents. This study suggests an autonomous self-incited fire detection sensor that is linked to smart buildings and powered by blockchain. The foundation of this system is the use of Ethereum smart contracts to manage and keep track of interactions between service providers, end users, and smart connected devices.	Worries about data security and privacy persist since the use of blockchain technology creates additional difficulties for data protection.	[36,37]
2	Augmented Reality	This study used BIM (Building Information Modeling) to construct the FSE elements so that the required information can be rapidly acquired by fire and safety equipment inspectors. AR provides realistic, immersive simulations that improve emergency response training.	Since AR systems rely so heavily on reliable network connections, interruptions could make training less effective.	[15,38]
3	Artificial Intelligence	This study, by contrasting it with the history of CFD fire modeling, offers a roadmap for the use of AI-based building fire safety engineering. There are guidelines presented for building a trustworthy fire database that includes both numerical and experimental data. Predictive maintenance powered by AI has demonstrated a notable decrease in equipment failure rates, improving overall safety.	There might be significant upfront expenditures involved in putting AI systems into place, which can be difficult for some businesses to afford.	[25,39,40]
4	Internet of Things	The study describes a low-cost system that uses Internet of Things (IoT) sensors to gather data in real-time, such as temperature and the number of individuals at the fire site. The system offers a control panel that presents sensor readings on a single web page from all of the sensors. The technology notifies the building keeper via phone when the accumulated values are above a predetermined threshold, enabling him to instantly alert law enforcement or send out firefighters. Improved real-time fire risk monitoring made possible by IoT devices enables faster reaction times and less damage.	The difficulties in concurrently managing a large number of devices and data limit their scalability, especially in big industrial environments.	[41,42]
5	Drones	Drones improve situational awareness by making it possible to quickly identify fire situations in difficult-to-reach environments.	Drones’ short battery life can be a limitation, limiting how long they can be used for reaction and surveillance.	[43,44]
6	Robotics	In an emergency, robotic devices help ensure a smooth evacuation, especially in large buildings.	There are difficulties in adjusting robotic systems to different contexts, and modification can be necessary for the best results.	[9,45]
7	Cloud-based fire models	Proactive safety measures are aided by the precise simulations for fire spread analysis that cloud-based fire models offer.	The dependence on internet connectivity poses a risk since outages could affect the availability of vital information about fire spread.	[17,34,46]
8	Edge computing	Edge computing processes data locally, which speeds up response times in emergency scenarios.	Because of their limited processing capability, edge devices still face difficulties when it comes to real-time analysis of big datasets.	[47,48,49]
9	Machine Learning	Using historical data, machine learning algorithms help identify possible threats early.	Maintaining the correctness of the model requires constant updates, and its efficacy depends on current and pertinent data being available.	[38,50,51]

Table 1. Recent studies on the technological advancements in the management of fire and safety procedures.

S.No.	Technology	Findings	Limitations	References
1	Blockchain Technology	Record Maintenance of the safety management system. The implementation of blockchain technology guarantees enhanced transparency in safety protocols and the safekeeping of vital documents. This study suggests an autonomous self-incited fire detection sensor that is linked to smart buildings and powered by blockchain. The foundation of this system is the use of Ethereum smart contracts to manage and keep track of interactions between service providers, end users, and smart connected devices.	Worries about data security and privacy persist since the use of blockchain technology creates additional difficulties for data protection.	[36,37]
2	Augmented Reality	This study used BIM (Building Information Modeling) to construct the FSE elements so that the required information can be rapidly acquired by fire and safety equipment inspectors. AR provides realistic, immersive simulations that improve emergency response training.	Since AR systems rely so heavily on reliable network connections, interruptions could make training less effective.	[15,38]
3	Artificial Intelligence	This study, by contrasting it with the history of CFD fire modeling, offers a roadmap for the use of AI-based building fire safety engineering. There are guidelines presented for building a trustworthy fire database that includes both numerical and experimental data. Predictive maintenance powered by AI has demonstrated a notable decrease in equipment failure rates, improving overall safety.	There might be significant upfront expenditures involved in putting AI systems into place, which can be difficult for some businesses to afford.	[25,39,40]
4	Internet of Things	The study describes a low-cost system that uses Internet of Things (IoT) sensors to gather data in real-time, such as temperature and the number of individuals at the fire site. The system offers a control panel that presents sensor readings on a single web page from all of the sensors. The technology notifies the building keeper via phone when the accumulated values are above a predetermined threshold, enabling him to instantly alert law enforcement or send out firefighters. Improved real-time fire risk monitoring made possible by IoT devices enables faster reaction times and less damage.	The difficulties in concurrently managing a large number of devices and data limit their scalability, especially in big industrial environments.	[41,42]
5	Drones	Drones improve situational awareness by making it possible to quickly identify fire situations in difficult-to-reach environments.	Drones’ short battery life can be a limitation, limiting how long they can be used for reaction and surveillance.	[43,44]
6	Robotics	In an emergency, robotic devices help ensure a smooth evacuation, especially in large buildings.	There are difficulties in adjusting robotic systems to different contexts, and modification can be necessary for the best results.	[9,45]
7	Cloud-based fire models	Proactive safety measures are aided by the precise simulations for fire spread analysis that cloud-based fire models offer.	The dependence on internet connectivity poses a risk since outages could affect the availability of vital information about fire spread.	[17,34,46]
8	Edge computing	Edge computing processes data locally, which speeds up response times in emergency scenarios.	Because of their limited processing capability, edge devices still face difficulties when it comes to real-time analysis of big datasets.	[47,48,49]
9	Machine Learning	Using historical data, machine learning algorithms help identify possible threats early.	Maintaining the correctness of the model requires constant updates, and its efficacy depends on current and pertinent data being available.	[38,50,51]

3. Integration of Industry 4.0 Technologies in Fire and Safety Management

3.1. Internet of Things

3.1.1. Introduction of IoT in Fire Safety

The term “Internet of Things” (IoT) refers to physical things (as well as groups of such things) that are equipped with sensors, computing power, applications, and other innovations and may communicate and share data with other systems and devices via the Internet or other communication networks [36]. As devices may interact with one another and flexibly arrange themselves in a group of numerous Internet-connected devices, IoT appears to be more important in the field of firefighter safety management for guaranteeing a secure lifestyle in smart cities. By doing this, we can set up a system for responding to emergencies and quickly implementing preventive actions. Various studies have been performed in this where a variety of fire management systems have been prepared using IoT technology to manage fire outbreaks.

3.1.2. BIM Integration with IoT

To increase building fire safety and rescue effectiveness, a framework has been created that integrates sensor-based Internet of Things (IoT), Building Information Technology (BIM), virtual reality (VR), and augmented reality (AR) [52]. BIM provides a three-dimensional architectural layout such that, when integrated with various sensors supported by IoT, the information related to fire status and the surrounding environment can be known and, according to this, we can perform the search and rescue can be carried out efficiently [39,53,54,55,56]. Building information technology (BIM) and virtual reality (VR) may be combined to create a large-scale, complicated training environment, facilitating the performance of high-risk training that can be applied to actual emergency scenarios [57,58,59]. An IoT-based framework integrating BIM with AR/VR is represented in Figure 2.

3.1.3. BIM and Digital Twin Inclusion

A building indoor safety management system with digital twins (DTs) is described in which the digital twin model contains the unification of a Building Information Modeling System (BIM), Internet of Things (IoT), and Support Vector Machine (SVM), as shown in Figure 3 [60]. With the help of BIM, a 3D model of the building is prepared, which has detailed information related to the building. IoT devices enable state mapping from the real physical world to the idealized digital world by gathering indoor safety status updates and real-time data on interior activities [61]. Support Vector Machine automatically classifies and assesses the amount of indoor danger. Via the internet, data storage and the user interface are perceived [37].

3.1.4. Sensor Networks in Fire Detection

IoT is being used to create a reliable, intelligent fire hazard response system in which necessary information related to the fire is collected by the responder and is further communicated to start immediate rescue operations by using all assists. The Internet of Things connects several sensors, including the Wi-Fi module ESP-32, the flame detection sensor, the smoke detection sensor (MQ-5), the flammable gas detection sensor, and the GPS module, as detailed in Figure 4. The sensors identify the danger and notify nearby police and fire departments by reporting the danger’s location to a cloud service that connects them all [62].

A new technology, NB-IoT, a smart smoke sensor, is used to deal with a cellular-based narrowband Internet of Things that keeps track of the smoke trend all the time as well as whether the level of smoke is above the acceptable limits according to which it will send data to the background server if needed, which will activate the alarms and other equipment such as a broadcasting horn. The appropriate department will then obtain data from the server automatically so that it may take the necessary action. The advantages of NB-IoT smoke sensors over conventional smoke sensors are their capacity to push real-time information in many formats, automated checking, and minimal power consumption [63].

An IoT framework of layered structure for firefighting is prepared according to the firefighting needs, which incorporates a sensing layer, transport layer, service layer, and application layer, as represented in Figure 5. Through sensors, intelligent video, RFID, and other technologies in the sensing layer, data about the variety of objects involved in firefighting safety management—including people, events, surroundings, materials, firefighting goods, firefighting facilities, and firefighting equipment—are collected in real-time. Following this, necessary information collected by sensors is transferred through appropriate transmission and access methods, such as LAN, mobile communication networks, and police special operations. Finally, data sharing integration, smart analysis, and process control are performed at the application layer for use by the relevant authorities based on IoT. Information conversion, service layer network hardware, and device information management are used to carry out these tasks [64].

3.1.5. Real-World Case Studies of IoT in Fire Safety

Several concrete cases describe the use of IoT technologies in the management of fire safety measures. For instance, Singapore’s Smart Nation initiative makes use of IoT sensors to detect fire risks in public housing. These sensors are used to measure environmental conditions like temperature, level of smoke, and even the presence of leaking gases and pass their findings through the control system in real-time [65]. If there are abnormalities, alarms are immediately sounded loudly, conveying the message to the local fire department to act.

Another example is the IoT-based fire detection system that has been deployed in commercial buildings in the United Arab Emirates. It combines the Internet of Things sensors with cloud computing to track fire hazards in different premises at the same time [66]. It has tremendously minimized fire response time, making the number of fire-related fatalities as well as fire-related losses to minimal.

3.2. Cloud Computing

Delivery of computing services like providing servers, databases, storage, networks, and software over the internet is called cloud computing.

Large-scale computational problems are solved by cloud computing by using distributed resources. Users have access to applications and data and can share resources from anywhere and at any time [67]. Cloud computing is effectively used in processing fire data and monitoring fire events [38,68,69]. A study seeks to address these flaws by developing a comprehensive fire risk evaluation index system, as shown in Figure 6; it proposes a unique fire risk prediction approach that uses Map Reduce, a cloud computing tool that supports interpreting data in parallel, to mine rules of association in the temporal domain [70]. An evaluation index method for calculating fire risk effectively and accurately is shown in Figure 6; it consists of two parts: a risk assessment of hazards and a competence evaluation of hazard-reducing factors. The capability assessment of risk factors additionally includes infrastructure fire management and firefighting management, while the evaluation process of risk-causing factors also comprises environmental characteristics and risk-causing features. After combining these, fire risk is evaluated and preventive measures are taken.

3.3. Big Data

Big data relates to amounts of data that are too big or sophisticated for traditional data processing techniques to handle. Data collection, analysis, storage, sharing, updating, searching, information privacy, and data source are some of the problems associated with big data analysis. Data and networks have been continuously digitally scanned thanks to the recent, explosive growth of internet technology and computer technology, giving big data a significant definition and growing its value [71]. Big data has tremendous utility for exploration, particularly when combined with the current Internet of Things. Big data and IoT, collectively, will undoubtedly boost human society’s intellect to another level, and its potential for advancement is endless [72]. Big data helps to calculate the loss rate and breaching danger during the fire offering advisory services for the budgeting process for fires. Big data also developed a process for evaluating a city fire using the data from the city fire statistics [73]. However, the amount of data gathered in the preparatory stage is remarkably low and less credible, which only specifies the importance of sparse data for safety incidents [74]. Safety small data (SSD) is the name given to this form of data, which is also referred to as the SBD’s framework. To forecast air quality in smart cities, the Deep Belief Network with Recurrent LSTM Neural Network (DBN-R-LSTM-NN) is also introduced, with Recurrent LSTM Neural Network (R-LSTM-NN), Deep Belief Network (DBN), and Fire Prediction Index (FPI) being further models [75]. Figure 7 shows the framework for fire detection, wherein the IoT device is made up of sensors, i.e., flame, smoke, and temperature sensors, to measure and communicate the data via node MCU to a cloud server.

A proactive monitoring system for rental-unit fire safety was built using a variety of technologies, including cloud computing and big data, to give the management department decision support based on geographic data [76]. Using a computation perspective, big data helped to address the issues of construction in fire safety measures by inventing a fire-recognition system with a live fire sensor network; a proposed framework for a fire detection outbreak is represented in Figure 7 for better understanding of the proactive monitoring system [77]. The management of fire safety is made possible by big data-driven emergency response administration, which is innovative. Big data has been shown to be quite helpful in building fire safety management [78]. Building management system (BMS) data are gathered and analyzed to anticipate the location, type, and possible fire threat that the building may experience. These data are gathered through fire alarm systems, building structures, and geographic data. The effective accumulation of interoperability is an advantage of “big data” [79].

Big Data and Fire Safety Protocols in the Electric Vehicle Industry

The utilization of big data analytics for the betterment of fire safety management can be better understood concerning the case industry of electric vehicles or EVs. Some examples are the micro-control of batteries in real-time big data by OEMs (Original Equipment Manufacturers) to notify operators of possible signs of severe conditions, such as overheating and short circuits that cause fire. For example, Tesla, through complex data analysis, enables its cars to check themselves and notify the firm when a possible battery failure may happen and, thus, cause fire risks [80].

These lessons from the EV industry can then be borrowed to urban fire safety where data captured from building management systems can be employed in the early detection of fire risks in high-rise buildings. By analyzing the trends in the sensor data that monitor the building, the building managers can come up with possibilities for fire incidents to prevent them from happening.

3.4. Artificial Intelligence

Artificial intelligence is when robots, especially computers, mimic human intelligence. A few components of particular applications of artificial intelligence include machine vision, speaker identification, knowledge-based systems, and natural language generation. Smart buildings are a representation of how modern infrastructure has evolved and how it has been combined with automated control systems, data processing, and AI [81]. To direct the crowd to leave the fire scene quickly and safely, it is crucial to research and create a smart evacuation system that makes use of a smart algorithm to organize the escape plan appropriately in response to the condition of the fire, examines the construction of the building, and obtains information on the fire site [43]. By developing artificial technology, the fire dynamic evacuation path model was applied for fire and safety [82]. An overall procedure of fire safety management using artificial intelligence is represented in Figure 8. According to the perceived significance of the attributes weighed against their statistical significance, a set of attributes is decided. Algorithms involving machine learning may aid in the better selection of crucial attributes [83]. In the occurrence of a disaster graphic information system (GIS), data integrate the live location of the people, a plan for an escape route using an artificial intelligence algorithm is provided to save the lives of the people, and a voice broadcast can be used to guide the people to leave the danger zone soundly in case of emergencies [84].

When a fire occurs, the fire extinguisher arrives at the scene and starts evaluation of the site by capturing images and videos, and all those data are transferred to the cloud server at the off-site location [85]. The cameras installed on drones and firefighters give the Incident Commander (IC) a general idea of how many firefighters and water trucks need to be deployed to the site [86]. It also gives the IC a general idea of the number of casualties at the location and helps to execute the safety procedure as recommended [45]. Artificial intelligence (AI) will be able to recognize the indications of fatigue in firefighters when more training films and/or visuals are added to the datasets and will convey the message to other firefighters if any of the firefighters is feeling that way, which will be helpful to the firefighter on the site as well as other firefighters as they will be able to prevent the accident.

Machine learning (ML) is evolving into a specialized technology for analyzing the productivity and fire tolerance of systems. As represented in Figure 9, the integration of machine learning is to improve fire detection and incidence prediction [87]. Machine learning is not based on some numerical equation or direction to begin a study; rather, it tries to emulate human thought processes whenever there are plenty of inputs available [88]. ML is used to detect fires in two well-known ways: an image-based approach and a sensor-based technique [40]. A framework of machine learning algorithms for fire detection is represented in the fire detection system. To decrease the likelihood of fires, machine learning algorithms can also assist with creative strategies for allocating capital (such as humans, equipment, and vehicles) to optimum potential locations [50,89].

3.5. Edge Computing

A large number of technologies involving the Internet of Things are being used for fire safety management. These technologies collect various data of various types that require strict latency, processing capabilities, and a consistent data processing approach and provide relevant alerts, events, or raw data. Cloud computing provides these benefits but has issues of limited network bandwidth and latency [90]. Through straightforward analysis, the technology known as edge computing may quickly deliver important services using data from a vast number of sources. It increases bandwidth and speeds up reaction time by bringing computing and data storage closer to the sources of the data [91]. Various studies have been performed where various technologies and applications are combined using edge computing to make smart buildings safe from fire risk. An SB112 smart building sensor combines a minimal number of sensors for temperature, humidity, smoke, flame, and CO utilizing a free and open-source edge computing platform and automatic Next Generation (NG) emergency call capability, as shown in Figure 10. Internet of Things devices, a Public Safety Answering Point (PSAP), middleware for smart buildings, and end-to-end operators are all involved. NG automatically calls to a PSAP as soon as a fire is detected. The Distributed Edge Computing Internet of Things (DECIoT)—an edge computing platform with an open-source architecture that ensures safe, adaptable, scalable, and fully regulated information exchange capabilities with other platforms or systems—is integrated with SB112 [92].

Another method involves the development of an ad hoc network of different sensing nodes, each of which is composed of an ESP8266-NODE MCU coupled to a variety of sensors, including CO, temperature, humidity, and smoke sensors, as represented in Figure 11, which portrays high-level architecture system using Node MCU. These nodes monitor their surroundings, identify fires, and send readings to a centralized node powered by a Raspberry Pi computer. This node warns the user and the fire department through an associated 4G module. In addition to calling and sending SMS, customers may also use the SMS service to check on the condition of their homes.

A manual for optimization is created to estimate fire spread and burnt regions while lowering the expense of forest fire containment. Using an IoT edge gateway, wireless sensing devices such as smoke sensors, temperature sensors, and flame sensors are utilized to identify fires and report their data to the appropriate authorities, as shown in Figure 12. An edge gateway-based device provides descriptive and predictive analytics based on the fire profile data, including the burnt area, fire spread area, and fire containment cost [93].

The IoT servers used by the fire authority receive these data and process them to produce an optimal cost for putting out fires. The safety authorities execute an evacuation strategy for those in danger, as per the assessment [94]. This technique can also be used in building fire safety management.

3.6. Blockchain Technology

Blockchain is a distributed, immutable database that simplifies recording transaction data and managing organizational network resources. An asset might be intangible (intellectual property, patents, copyrights, or branding) or physical (a home, car, money, or area of land). Nearly everything of value may be recorded and exchanged on a blockchain network, reducing risk and increasing efficiency for all users. The U.S. Bureau of Labor Statistics reported in 2018 that 66 construction workers pass away annually as a consequence of explosions and fires [95]. Over USD 280 million in direct property damage results from home construction or renovations annually, according to five-year research (2010–2014) by the National Fire Protection Association (NFPA) [48]. Engineering data may be supplied, verified, and approved using blockchain technology, which subsequently produces a permanent record of such information, establishing a knowledge-trail digital archive, choices, and questions raised throughout projects. To perform automatic maintenance and repairs of constructed assets at the period when existence is functioning, a framework based on the combination of BIM, IoT, Distributed Ledger Technology, and smart contracts is developed. A visual representation of Fire sensor failure protection for smart buildings using the blockchain is portrayed in Figure 13. People can be informed or alerted using the blockchain technique in advance of a disaster, and this method also helps to maintain accurate records and track fire safety equipment to ensure that it is in working order to handle the issue [41].

Blockchain technology is being used in many buildings to evaluate portable firefighting equipment (PEE) and perform the tasks of finding and documenting safety inspection data conformity [96]. Several research issues must be overcome before blockchain may be adopted, despite its growing popularity [97]. Blockchain employs a ledger that is accessible to all of the network’s registered users based on a consensus property [98]. The technique is made secure and transparent by progressively abolishing the requirement for a foreign state authenticator. The intent of a blockchain-powered solution combined with a sensing-as-a-service approach seems to be to manage and govern the integrity of smart buildings [99]. Fire sensor and service provider smart contracts are the two possible smart contracts. In the instance of a sensor failure, a fire sensor failure transaction is tracked by the smart contract authorized for fire sensors, which in turn boosts a service request on the network operator smart contract [100]. The service provider generates a ticket to resolve the issue. The representative of the service provider is notified, who thereafter deals with the issue raised. The merging of blockchain technology with smart sensor technology can manage log data effectively and boost the advantages rendered with the assistance of a shared, distributed database [101]. The automation provided by the blockchain and smart contracts can regulate and automate communication between diverse stakeholders, perform ahead with a demand for a third-party administrator on behalf of the service provider, and avoid delays in the routine maintenance of a misconfigured or defective fire-detecting sensor in a building [102].

3.7. Metaverse Technology

A fictitious portrayal of the Internet is called the metaverse, which is singular, ubiquitous, and immersive and is accessible with the use of headgear for both augmented reality and virtual reality. A metaverse is a network of 3D virtual environments mostly utilized for social interaction. Historically, Neal Stephenson’s science fiction publication Snow Crash, issued in 1992, is credited with pioneering the term “Metaverse” [103]. Even though the concept of metaverse has been around for a very long period, the term has been publicly known since it was rebranded to Meta [104]. Metaverse study and development have become a key trend in smart urbanization concerning the creation of believable digital communities using AI systems that are powered by massive amounts of data [46,105]. Metaverse allows people to have virtual interactions and visuals of the fire extinguishing product, which helps the potential customer to experience the product and provides a qualitative and quantitative way of dealing with the product as well as its benefits and ways of use [106]. It also allows people to deal with real-time fire situations or scenarios with the help of digital twins (DTs) so that people will be able to deal with the hazard when the situation occurs [49]. To simulate, train, and respond to fire-related incidents, virtual and augmented reality technologies are used in the metaverse for fire safety management. The study states that it is very helpful for providing training purposes. Users may imitate emergency circumstances in a secure and controlled setting by conducting virtual fire drills. This may include having some form of scenarios such as emergency drills, use of extinguishers, and exercise in evacuation plans. Firefighters, emergency department personnel, and the inhabitants of the buildings that are at risk can enhance and practice themselves through the development of detailed and fully adjusted training programs in the metaverse environment [42]. This means that metaverse technology rises as one of the technologies that assist in post-incident analysis. Such actions cause participants to reconstruct what happened to understand the objective scenario, find out why it happened, and prevent such actions in the future. An escape plan can also be developed by mimicking any fire incident that may happen in an emergency [51].

3.8. Building Information Modeling (BIM)

The construction industry has been pursuing the application of Building Information Modeling (BIM) in the past decades due to their efficiency and ability to save resources throughout designing, planning, and carrying out new features. The application of BIM is most common where there is pre-planning, designing, construction, and integrated project delivery of the buildings and the infrastructures [107].

The influence of BIM in compiling and analyzing data from numerous study areas is on the rise. A new trend in the management of computer technology, facilities, equipment, and fire and safety has been identified. It is feasible to communicate locations and crucial information relevant to emergency decision-making at fire sites in conjunction with the data produced during the operation and management stage when integrating BIM into the area of firefighting and catastrophe prevention. BIM stores the majority of the criteria for creating a fire response because they contain spatial qualities and make information easier to access and utilize [44]. Using BIM software, a 3D object model and an information structure for fire disaster management are created and a further prototype of a BFIMS system that visually represents the information needed at building fire response locations is developed. A BIM-based system architecture is represented in Figure 14. In this process, the first step was deriving the information required that emergency responders must use, either directly or indirectly, from earlier research on fire disasters. The UML was used to create the data structure and specify the relationships between the data by the determined requirements. The second is a BFIMS, which is capable of detecting the location data and voluminous details of the size and intricate internal facilities of the buildings in addition to being developed using the SketchUp Desktop 2024.0 and Ruby programming language. Lastly, the process was validated using the scenario-based case study approach, following interviews with key experts and practitioners; the application of the proposed approach was asserted [108]. Research has presented a fire disaster management procedure that utilizes the Building Information Model and that can update both the fire dynamically as well as the building information at the same time. An application program called Smart Fire Rescue Management (SFRM) was developed with Revit^TM software (Autodesk Revit 2025) to enhance this algorithm. Altogether, the study successfully achieved the purpose of collecting the relevant information that will be necessary in combating fires and created a fire information database. Based on the information above, the system architecture and algorithm were developed. In addition, a successful management strategy was designed to choose and interpret the raw data via the system architecture to supply the information in the 3D/BIM model efficiently. Then, to make the information easier to use, a graphical user interface application tool called “smart fire rescue management” (SFRM) and a case study version of the virtual model were developed. Here, RevitTM was used to program the graphical user interface (GUI) tool. Lastly, a case study was used to assess and validate the proposed system. To assess the feasibility of the SFRM system, a prototype was put into use in a real construction project during a building fire incident [109]. An outline of various technologies for fire and safety management is reflected in

Table 2. Summary of deployment of various technologies for fire and safety management.

S.No	Technology	Significance	Limitations	References
1.	Safety protocols using predictive analytics	This technological advancement helps to identify emerging safety trends and protocols; to be utilized as proactive measures to handle any kind of emergency.	The efficiency and effectiveness of different predictive models rely enormously on the availability of thorough and inclusive historical data, which are equipped with all the process-impacting parameters. Hence, the danger of inaccurate predictions in emerging industries may occur.	[84,85]
2.	Incident prediction through machine learning	Potential hazard identification by applying machine learning algorithms can facilitate early detection based on the algorithm training and the quality and quantity of data on which the algorithm training is executed.	The model accuracy largely depends upon the continuous updating of the model operation. The availability of data also affects the effectiveness of model prediction.	[29,40,87]
3.	Thermal Imaging Technology	Hidden fire sources can be easily detected by thermal imaging technology. This technology can easily point to the exact source with the identification of fire-originating hotspots.	In complex industrial settings, depending upon the type of industry, different kinds of heat sources and hotspots can be found that can interfere with the identification of fire sources, as thermal readings depend upon the heat sensors and infrared rays.	[49,103]
4.	Smart fire extinguishers	Automatic detection and fire suppression is being executed by smart fire extinguishers in sophisticated industries. The main advantage of this technology is its response time.	Existing safety measures compatibility with smart fire extinguishers is a challenging task. Integration of these safety measures requires a comprehensive system upgrade that caters to the requirement of both measures to operate with optimum efficiency.	[48,104], [109]
5.	Human–Machine Interface and Robotics	Safety inspections can be easily executed with the help of human–machine collaborations. Proper checks and examination of essential components are ensured. Robotic systems can also contribute to effective evacuation in emergency scenarios.	The coordination between actions implemented by the collaboration of human–machine expertise requires accurate training and system integration to ensure effective communication and desired results.	[48,96]
6.	IoT Sensors	Enhanced and improved real-time monitoring of fire and hazard risks can be ensured by IoT sensors. The quick response time with real-time information, which plays a crucial role in any emergency, can be obtained by this technological advancement.	The issue of scalability has been a challenging issue; as of now, the challenge of managing a high volume of collected data from a large number of installed devices in large industrial settings needs to be addressed before finalizing monitoring infrastructure through IoT sensors.	[52,64]
7.	Automated emergency response systems	For effective handling of any emergency-like situation, speed and accuracy of the response system play an important role in minimizing the adverse impacts of any disastrous event. Hence, automated response systems provide speed and accuracy in an emergency.	Complex emergency scenarios may require human oversight to handle any kind of altering situations that may require human intervention. However, reliance on automated systems in these kinds of scenarios could be a challenging aspect.	[19,21]
8.	Augmented Reality (AR)	For the effective learning of emergency response training and preparedness, the application of augmented reality can prove to be a milestone in achieving a universal basic training workforce. Immersive and realistic simulation-based training can provide real-world situation experience, and preparedness based on such platforms can be a vital component of emergency training programs.	High-speed and stable network connection are the basic requirements for any kind of training on the augmented reality platform. To provide real-world-like situations, heavy graphics and programming codes run on high-performance systems, thereby requiring uninterrupted high-speed data availability.	[42,47,101]
9.	Networks with wireless sensors	This technology is useful for the process of seamless data transmission in real-time. Sensor nodes are applied to manage and monitor the data transmission. This technology has its advantage in surveillance and security monitoring protocols.	Network reliability is directly proportional to the unhindered flow of signals. Challenges may exist in maintaining the seamless flow of signals, especially in locations with high interference that could obstruct signal transmission.	[14,44,60,103]
10.	Predictive maintenance through artificial intelligence.	Equipment failure in an emergency can be a critical factor in saving lives and averting destruction. A significant reduction in equipment failure rates can be ensured with the help of predictive maintenance through utilizing artificial intelligence technology.	Applying AI for predictive maintenance can be a costly affair for some organizations as not only does it require a highly qualified workforce for its implementation but it also requires a high initial cost for installation, hence posing a financial challenge.	[25,37,71]
11.	Record maintenance through blockchain technology.	Storing critical records and highly classified information related to the safety and security protocols of buildings needs to be ensured by utilizing blockchain technology. High-risk buildings around the globe are always the target of mischievous elements.	Concerns regarding data security and data privacy remained a concerning factor in the community. Data protection challenges are always at the front of any technological advancement.	[31,47,54]
12.	Surveillance through drones	In a challenging environment, quick identification of fire incidents can be detected by surveillance systems through drones. With the usage of this technology, situational awareness is improved, thereby affecting rescue operations.	Limited battery life and restriction in terms of area covered through maneuvering are some of the challenges faced during surveillance through drones. These constraints limit the duration of surveillance and response capabilities.	[40,71,90]
13.	Cybersecurity of safety systems	Targeting safety systems is a challenge in this technologically advanced world. Cyberattacks in various forms target sensitive information and infrastructures. Hence, cybersecurity measures improve protection against any kind of cyber threats.	The implementation of robust cyber-security measures can be a challenging task that requires the current efforts to be more advanced than that of evolving cyber threats and vulnerabilities.	[9,45]
14.	Edge computing for response optimization	The processing of data locally with the help of edge computing reduces response time, thereby resulting in enhanced response optimization. In critical situations, this quick response can be a deciding factor in terms of rescue operations.	The limited processing power of edge devices poses a challenge and, at times, it is very difficult for edge devices to ensure a real-time analysis of a large dataset.	[92,93]

.

4. Fire Safety Incidents in Industry 4.0

The transition towards Industry 4.0 has added new challenges in fire safety management because of the increased dependence on interconnecting devices and automatic systems. Fire incidents primarily tend to happen in highly automated environments due to equipment failure, electrical faults, and problems with the compatibility of the systems in place [110]. Other examples include the cases where the functioning failure of the robots and information technology systems in smart manufacturing plants led to electrical fires. Likewise, the oil and gas industry, which is heavily reliant on IoT and edge computing to automate processes for improving efficiency, has witnessed several fires resulting from integration issues of the systems.

In response to these challenges, it is crucial to recommend specific safety measures:

• Predictive Maintenance: the term Industry 4.0 technologies should be used in the process of deployment of predictive maintenance systems so as to consider the possibility of occurrence of fires and other emergencies beforehand.
• Interoperability Standards: fire safety systems should meet the international interfolding norms so that the fire safety products transfer information seamlessly, which is crucial for current threat management.
• Training for Personnel: IoT devices and sensors require training on their proper use so that employees do not cause a fire through accidental operations.
• Robust Backup Systems: controlling measures for system failure include the use of backup systems and fail-safe instruments to support the automatic fire detection and suppression systems in case of failure.

These recommendations call for closer coupling of effective fire safety solutions with Industry 4.0 technologies that may be used to avoid the occurrence of incidents and the level of harm resulting from fire risks in industrial contexts.

5. Engineering Contributions of Industry 4.0 in Fire Safety Management

The engineering contributions of Industry 4.0 technologies in fire safety management are significant. Now, with the help of IoT, AI integration, and the usage of cloud computing, fire safety systems can predict, detect, and minimize fire hazards at a faster pace than conventional systems [111]. For instance, IoT-enabled sensors can record the real-time temperature and gas level deviation that are shared with an analytics platform in the cloud for analysis. Consequently, this information is applied in the activation of early warning systems and to direct the response teams in fighting fire at the same time.

Moreover, advanced smart innovations that integrate the use of artificial intelligence such as “predictive maintenance systems” have greatly enhanced fire protection and prevention in industrial organizations to a level where plant equipment faults that are likely to cause fire are detected and addressed before the fault triggers a fire accident. The engineering value is based on designing complete, modular systems that apply across industries to manufacturing and construction as well as public works, which can translate to savings and improved safety.

The actual implementation of these technologies illustrates the prospects of these technologies for preventing fatalities, minimizing infrastructural losses, and enhancing security performance in various commercial sectors, which explains why additional study of these technologies is needed.

6. Challenges in IoT-Based Fire Safety Systems

6.1. Cost Challenges in Upgrading to IoT-Based Fire Protection Systems

Cost is one of the key issues organizations encounter when seeking to migrate from traditional fire protection systems to IoT-based systems. To integrate IoT structure, considerable amounts have to be spent on equipment (sensors, detectors, control units) and software (cloud memory, data analysis systems) [112]. Also, the challenge of making the systems interoperable and training personnel to operate these new technologies is very costly, especially for SMEs.

To address such issues on the financial aspect, governments should consider providing subsidies or incentives for implementing IoT solutions in fire safety solutions. The policy of “staged implementation” beginning with high-risk industries would also make it easier for industries to have cost control measures put in place while at the same time enhancing general safety measures.

6.2. Interoperability Issues in IoT-Based Fire Systems

At the same time, machine integration into fire safety systems is only becoming more common and standard; thus, the integration of different devices, platforms, and protocols is crucial. Interoperability means the capacity and communications of diverse systems and parts in one system, irrespective of the manufacturer or platform [113]. The absence of interoperability results in problems with data sharing, communication, and ultimately delays in responding to an emergency, hence compromising the safety of the people involved.

• Existing Industry Standards for Interoperability

These challenges are tackled by standardization, whereby the industry develops standards that ensure the integrated systems work well together. OSI is one of the most commonly known standards; however, there is also the ISO OSI Model, which can be used to describe communication protocols used by the devices of different networks. When it comes to IoT fire safety systems, the OSI model ensures that the sensor, alarms, and control systems can interconnect no matter the producer of the products or the operating system in existence.

Another is the IoT Reference Architecture, which is based on ISO/IEC 30141:2018 [114] and describes the IoT systems architecture, focusing on interoperability and scalability [115]. This standard provides recommendations to achieve the integration of different IoT devices provided by various vendors in the fire safety management system.

b.

Application of Industry Standards in Fire Safety Management

When it comes to real-world IoT applications in fire safety management, what is seen to be cooperation and integration needs to comply with standards including IEEE 802.15.4 [116], which is well suited for low-power wireless communications within the IoT networks. This standard reflects that all IoT devices including fire detectors, temperature sensors, alarms, etc. should be able to work on the same network to avoid some of the devices not being able to communicate when there is a fire outbreak.

Interoperability between fire safety systems and building management systems is enhanced by another tool known as Building Information Modeling (BIM). BIM encompasses technologies put together and enables the information flow between various parts [117], guaranteeing the real-time info that first responders and safety managers insist on during a fire emergency. For example, BIM integrated with Internet of Things sensors can offer real-time visualization of built assets, whereby data on the building such as heating ventilation, air conditioning, fire detection and alarm, and emergency lighting are all integrated.

c.

Ensuring Compliance with Global Standards

NFPA 72, the national fire alarm and signaling code, is crucial in determining the level of compliance of fire safety systems with industry standards in terms of interoperability. NFPA 72 requires that all the parts in a fire alarm system, including IoT devices, are capable of interfacing with other systems [118]. This standard entails all the aspects from the type of wiring followed to the software communication standards of the devices.

Another STI is the ETSI EN 303 645 standard [119] on IoT cybersecurity with a focus on interoperability as well. Adherence to this standard facilitates secure links of devices and averts potential risks that may see the channel disrupted during a fire emergency.

d.

Methods to Ensure Interoperability Compliance

To ensure compliance with these standards, several methods can be adopted:

• Use of Middleware Platforms: One M2M and IoTivity are middleware platforms that would facilitate communication of the IoT devices within the same ecosystem, irrespective of their design. These platforms incorporate APIs and protocols and provide ways to ensure that devices from different manufacturers can interoperate within a network without much integration.
• Protocol Translation: As far as compatibility is considered, the protocol translation services can be used where the legacy systems are installed. For instance, transforming Modbus protocol to MQTT (Message Queuing Telemetry Transport) makes it possible to enable older fire safety systems to share data with new IoT systems [120].

e.

Future Directions for Enhancing Interoperability

The current standards go a long way towards addressing interoperability issues; however, due to the technology-push nature of IoT development, the standards are not fixed. Other trajectories involve self-assembling IoT networks that implement detection mechanisms to determine each other’s protocols and the implementation of edge computing to facilitate real-time data analysis as opposed to using the cloud.

7. Conclusions

The integration of Industry 4.0 technologies, especially the IoT, has offered efficient ways of monitoring, analyzing, foreseeing, and even responding to emergencies, especially in fire safety management. This paper presents a critical evaluation of how IoT, cloud computing, AI, and many more sophisticated technologies can improve fire safety systems in built structures. These technologies take advantage of the integration of sensors, cloud platforms, and smart data processing systems to enhance quick identification and suppression of fire risks, optimize the period of response to incidents, and increase synergy between the fire safety staff.

Some of the studies reviewed include the following: IoT-based fire safety systems coupled with BIM provide live situational information to the emergency response teams. This integration improves the what, where, how, and when of fire hazard identification, evacuation pathfinding, and resource use in suppression. In addition, the affordable price of cloud computing makes it possible to store large quantities of sensor data and apply them for predictive and long-term risk management. The use of AI and machine learning algorithms also contributes to identifying the future likelihood of fire by assessing past data to provide meaningful information to fire safety managers.

However, these assistants have still some issues, especially with overall compatibility between different systems and devices. It has been established that there are international standards that can be used in defining and implementing this technology such as ISO/IEC 30141 and IEEE 802.15.4, which enable IoT devices from different manufacturers to be interoperable and, therefore, communicate effectively. The guidelines stated above are crucial elements to be followed to attain perfect interoperability in fire safety systems based on IoT. Furthermore, this paper focuses on protocol conversion and middleware solutions, whereby legacy systems should connect with modern IoT solutions to enhance the shift to Industry 4.0 technologies, making the operations relatively frictionless and cheap.

Among the aforementioned limitations, cost is considered as one of the most significant barriers, notably if the implementation process is conducted in small enterprises. Converting currently used fire safety systems into IoT ones is very costly for both hardware and software. Nevertheless, the constraints such as financial barriers can be met through subsidies provided by the government or thermal tune-up through step-by-step gradual practices focusing on the areas that are most prone to such issues. Further, concerns like the applicability of IoT systems across large industrial settings and handling a large number of data generated by IoT sensors have to be sorted out.

It also outlines the possible gaps that constitute directions to focus on in future research and development. Smarter solutions for fire detection, the application of AI for the predictive maintenance of assets, and the autonomic configuration of the IoTs are only some of several edge use cases that could enhance modern fire safety management. Further, continued innovation of cybersecurity will be relevant to the fire safety systems based on IoT since the systems will involve extensive sharing of big data.

Lastly, the deployment of Industry 4.0 technologies, most of which include the IoT, has very bright prospects in improving fire safety and the management of it in both the residential and industrial fields. The known challenges including interoperability issues, scalability, and cost factors need to be resolved, but the advantages such as real-time monitoring, big data analytics, and self-controlled responses are more rewarding. Further advancements in the areas of research and the implementation of international standards will help IoT-based fire safety systems to become effective tools for ensuring the increased security of built environments and fire prevention and the minimization of people’s loss and property damage.

8. Recommendations

There are advantages and disadvantages to integrating Industry 4.0 technologies with fire safety management. These technologies bring greater complexity along with the potential to increase efficacy and efficiency. Some challenges and future work for improvements in fire safety management are discussed:

• Privacy and Data Security Issue

Industry 4.0 tools mostly focus on gathering and exchanging data. This raises questions regarding the privacy and security of vital fire safety system data. It can be enhanced by putting strong cybersecurity safeguards, encryption, and access restrictions to preserve the solidarity and privacy of fire safety information and handle new threats, and by assessing and updating security procedures regularly.

ii.

Implementation Cost

Implementing Industry 4.0 fire safety management technologies might also feature a high initial investment, especially for small business enterprises or projects. Government involvement through the creation of various programs, subsidies, and incentives might motivate small enterprises to use modernized fire management systems.

iii.

Dependability and Maintenance

Reliance on networked smart devices and sensors might result in heightened intricacy and possible malfunctions in the system. Reliability becomes dependent upon routine maintenance. It can be encountered by creating predictive maintenance algorithms to see potential issues before they arise.

iv.

Artificial Intelligence (AI) Integration for Early Detection

Even though artificial intelligence (AI) can enhance the early detection of fire occurrences, false positives and negatives may still happen, affecting the system’s overall dependability. AI algorithms should be continuously improved by using machine learning and feedback from real-world data to make fire detection systems more accurate and dependable.

v.

Interoperability

Interoperability problems between various systems might arise from the integration of various Industry 4.0 technologies, preventing smooth coordination and communication in fire safety management. To guarantee interoperability, establish industry standards for communication protocols. Before deployment, thoroughly test the system, and update it often to include new technology.