The adoption of Internet of Things (IoT) practices holds great potential for the industry. Nonetheless, ensuring reliability and maintaining high-quality service is paramount

METHODS AND THEIR APPLICATIONS:
Before proposing a solution, it is essential to acquire a comprehensive grasp of the IoT domain, encompassing present architectural models, methodologies, applications, and obstacles, through literature review. Consequently, this section offers a compilation of peer-reviewed articles pertaining to the architectural framework of the IoT domain. Furthermore, it outlines peer-reviewed research findings related to existing mission-critical systems and pinpoints key areas that underscore the contributions of this paper. The IoT architecture for industrial systems is a broad field with multiple definitions and architecture models available [21], [22]. These architectural models enable the development of diverse IoT applications, spanning from smart systems in private sectors to remote monitoring, maintenance, and data acquisition systems in industrial settings [23], [24]. For our study, we selected a generalized architecture model and tailored it to heavy industry, as depicted in Figure 1. A similar architecture model is presented in a study by Luong et al. [25], but it primarily focuses on the broader scope of IoT. Mission-critical industrial IoT applications involve four key tasks: data acquisition, data transmission, data storage and processing, and data access by end-users.
We adopt a top-down approach, where the physical layer constitutes the highest tier and the application layer resides at the lowest tier. Each layer is described in detail below:
- Physical layer: This layer encompasses all physical devices responsible for data measurement, sensing, and transmission. These devices possess limited resources and are primarily used for raw data acquisition. Besides their measurement and sensing capabilities, physical layer devices can transmit measurements over the Internet to data storage systems [26]. For instance, pumps and compressors controlled by control systems utilize temperature and flow measurements for effective control [27].
- Network layer: The network layer facilitates communication within an industrial IoT application. To enable remote data storage and processing, a communication medium is essential. The information and communications technology field has witnessed rapid advancements in communication protocols and methods, offering a variety of communication options within IoT applications [13]. Mobile communication infrastructure is commonly employed for long-distance communication. However, the remote nature of industrial sites can sometimes impede the effectiveness of mobile communications.
- Data layer: Raw data collected from the physical layer requires processing and storage. The data layer serves as a platform for real-time data processing and storage, often utilizing cloud-based solutions. "Big data" storage mechanisms are employed to accommodate large datasets, as real-time monitoring of industrial equipment typically generates substantial data on a daily basis. Retrieving and processing data from this layer can be accomplished with minimal effort [28].
- Application layer: The application layer grants end-users access to data stored in the data layer. It may consist of application software located either with end-users or in the cloud. The primary objective of the application layer is to provide users with detailed information presented in a user-friendly manner [25]. The visual depiction in Figure 1 below illustrates the core IoT concepts discussed earlier. Within each layer, we have identified the critical challenges and concerns that may adversely affect the Quality of Service (QoS) in industrial systems.