# A Distributed Real-Time Monitoring Scheme for Air Pressure Stream Data Based on Kafka
## 1 Introduction  
With continuous technological advancement and increasing demand, real-time air pressure monitoring has become indispensable and crucial in various industries. Strict air pressure control is essential in sectors such as petroleum, chemicals, transportation, and mining [1, 2, 3, 4]; an accurate, timely, and remotely controllable real-time pressure monitoring scheme is of paramount importance. This would enable management personnel to make rapid decisions in response to emergencies, directly impacting production safety and operational efficiency. The recent widespread implementation of Industry 4.0 has initiated a new wave of digital industrial development. This paradigm can deeply impact efficiency, energy, sustainability, working conditions, human resources, production management, and maintenance planning in various industries [5]. Comparative analyses conducted by [PERSON] et al. [6] on widely used open-source Internet of Things (IoT) platforms, as well as the intelligent factory architecture based on Industry 4.0 technologies implemented through open-source software by [PERSON] et al. [7], demonstrate that the various free and open-source IoT platforms and projects can provide reliable technical support for the development of Industry 4.0. These platforms offer advantages to include low cost, internet-accessible information, and robust configuration capabilities for developers. Simultaneously, the development of IoT technology has allowed various types of low-power sensor devices to incorporate innovative technologies such as electronic miniturization, portability, wireless communication, and energy-efficient microprocessors [8; 9]. [PERSON] et al. [10] proposed a tire pressure detection system for motorized vehicles. [PERSON] et al. [11] developed a landfill gas pressure monitoring and management system. [PERSON] et al. [12] devised an environmental monitoring system. These systems use low-power sensor networks to wirelessly transmit real-time air pressure data to provide remote, real-time, and reliable data transmission. However, traditional air pressure monitoring systems have some limitations. Typically, they store the real-time air pressure data gathered by sensors in databases. Subsequent analyses and decision making depend on reading, querying, and processing the stored data from the database. The system faces some challenges. Firstly, user feedback depends on database operations, which introduces delay. Data volume increases cause performance bottlenecks, which further exacerbate this delay. This implies that users cannot promptly access real-time air pressure data during emergencies, causing difficulties when making corresponding decisions quickly, which in turn increases the possibility of data loss. Therefore, the importance of real-time monitoring systems cannot be overlooked in practical working environments; the systems must be capable of quickly gathering and analyzing large amounts of data streams generated by sensors. This process becomes crucial when dealing with time series data to support critical decisions [13]. However, overcoming the limitations of existing schemes is a significant challenge in real-time air pressure monitoring. Therefore, we must continuously explore new methods and technologies to address these challenges, improve system performance and reliability, and better meet the specific requirements of real-time monitoring systems for data gathering, processing, and supporting decisions. The emergence of complex event processing (CEP) and distributed stream processing (DSP) systems has provided a new approach to processing high-volume stream data using big data technology in real time [13]. In this field, Kafka plays an important role as a stream processing (SP) architecture based on the pub/sub mechanism, especially for handling massive logs in distributed environments with multiple sensor nodes and large clusters [14]. Therefore, Kafka has become widely used as a stream processing tool for real-time data analysis [15].