Design of a Cloud-Based Fire Alarm Monitoring System for Buildings Based on LoRa Technology

Traditional building fire alarm monitoring systems often use wired connections, making installation time-consuming and labor-intensive, especially in older residential areas. With the development of Internet of Things (IoT) technology, a fire alarm monitoring system based on LoRa technology has been designed, leveraging the advantages of LoRa technology such as long communication distance, low power consumption, and low cost, eliminating the need for wiring and enabling long-distance transmission. The wireless sensor network built using LoRa technology consists of aggregation nodes and terminal nodes. The terminal nodes can monitor multiple points and various types of data, while the aggregation nodes receive data transmitted from all terminal nodes and then package the data for upload to the cloud platform via the EC204G module. This system is cost-effective, easy to deploy, and capable of real-time monitoring of environmental information, cloud storage, and timely alerts, ensuring stable and reliable operation with strong practicality.The system consists of three parts: terminal nodes, aggregation nodes, and the cloud platform. The terminal nodes are composed of an STM32 microcontroller, various sensors, and a LoRa wireless communication module. The aggregation nodes consist of an STM32 microcontroller, a LoRa module, and a 4G module. The STM32 microcontroller in the aggregation node processes and packages the data received from the terminal nodes via LoRa, then uploads it to the cloud platform using the MQTT protocol through the EC204G module. The cloud platform parses, displays, and stores the data, providing timely alerts in case of anomalies. All nodes are powered by dry batteries and use wireless transmission, eliminating the need for layout wiring, making deployment simple and convenient.According to the data transmission process, the system hardware circuit can be divided into two parts: terminal nodes and aggregation nodes. The terminal nodes include an STM32 microprocessor module, a temperature and humidity sensor module, a flame sensor module, a smoke sensor module, a dry battery module, and a LoRa communication module, primarily responsible for data collection and transmission to the aggregation node via the LoRa module. The aggregation nodes include an STM32 microprocessor module, a LoRa communication module, a dry battery module, and an EC204G module, mainly processing and packaging the data sent from the terminal nodes for upload to the cloud platform via the EC204G module.In the system design, the power consumption of the microcontroller has a significant impact on the lifespan of the dry battery. Additionally, the microcontroller needs to have a rich set of peripheral interfaces to accommodate various sensors, the LoRa communication module, and the EC204G communication module, while also reserving certain peripheral interfaces for future system requirements.Based on its low power consumption and rich peripheral capabilities, the STM32L476 meets the design requirements of this system. LoRa is a low-power wireless standard for local area networks, offering longer transmission distances and lower power consumption, combining the advantages of long-range and low power consumption. Furthermore, LoRa signals have strong penetration capabilities, making them suitable for building fire alarm monitoring systems.The LoRa wireless communication module uses the SX1278 RF chip and employs LoRa spread spectrum modulation; it has strong anti-interference capabilities, long transmission distances, and low power consumption. Communication between terminal nodes and aggregation nodes occurs via the LoRa wireless module.The terminal nodes mainly consist of an STM32 microcontroller, a DHT11 temperature and humidity sensor, an MQ-2 smoke sensor, a Flame-D flame sensor, and a LoRa wireless communication module.The aggregation nodes include an STM32 microcontroller, a LoRa wireless communication module, and an EC204G communication module. The LoRa module facilitates information exchange between the aggregation nodes and terminal nodes, while the EC204G module enables the aggregation node to upload all terminal node data to the cloud platform.The entire system software mainly includes terminal node software design, aggregation node software design, and cloud platform. Communication between the aggregation nodes and terminal nodes is conducted in a polling manner, where the aggregation node sends data request commands to the terminal nodes. Upon receiving the data request command, the terminal nodes send the collected data to the aggregation nodes via the LoRa module. After receiving the data, the aggregation nodes perform verification, and upon confirming the data’s accuracy, they upload it to the cloud platform via the EC204G module. The cloud platform handles data storage, analysis, visualization, and timely alerts.The upper computer platform uses the OneNET cloud platform, which supports various device access protocols, allowing users to choose the appropriate access protocol based on their needs, hence the selection of the MQTT communication protocol.Terminals can upload different data formats, allowing corresponding cloud devices to have multiple data streams, with the uploaded data stored in these data streams. The OneNET cloud platform provides visualization and trigger services for data stream resources. Users can create applications in the application management interface and add the required data stream resources to these applications to achieve data visualization. Users can add triggers in the trigger management interface, select one or more data streams, and set corresponding trigger conditions. When the uploaded data meets the trigger conditions, the trigger is activated to issue alerts.The greatest advantage of the OneNET cloud platform is that it allows users to design and implement the upper computer software monitoring interface directly in the cloud. Users can complete data visualization, data stream management, trigger management, and more on the platform, and generate internet address links for the data visualization application interface, allowing real-time viewing of application data. Additionally, a mobile app is provided for users to view data uploaded to the cloud platform anytime and anywhere.Upon receiving the data request command sent by the aggregation node, the terminal nodes transmit the collected environmental data to the aggregation nodes via the LoRa module.The main functions of the aggregation node are to periodically send data request commands to multiple terminal nodes and receive monitoring data returned from the terminal nodes, completing the communication process between the aggregation nodes and terminal nodes; and to upload the received environmental data to the OneNET cloud platform via the EC204G module for storage, analysis, and visualization.After the aggregation node initialization is complete, it first constructs the MQTT connection package, connecting multiple real terminals to the corresponding cloud devices on the OneNET cloud platform. Then, the STM32 microprocessor sends data request commands to the terminal nodes via the USART1 serial port through the LoRa module and waits for responses from the terminal nodes. After receiving the monitoring environmental data from the terminal nodes, the data is verified, and if the data verification is correct, it is parsed.After parsing the data sent from the terminal nodes, the next step is to construct the JSON data format required for access to OneNET. Then, an MQTT-OneNET publish message package is constructed, and the packaged data is uploaded to the corresponding device data stream on the OneNET cloud platform via the EC204G module connected to the UART5 serial port.A cloud-based building fire alarm monitoring system has been designed using LoRa technology, achieving long-distance transmission and monitoring of fire alarm information within buildings while maintaining low system power consumption. This system addresses the issues of complex wiring, short transmission distances, low intelligence, and poor information aggregation capabilities found in traditional fire alarm monitoring systems. The system flexibly integrates with mature cloud platforms available on the market, simplifying the design and development process, reducing development costs, and allowing users to conveniently observe data in real-time. It can also accommodate different sensors based on varying needs or expand existing sensors, as well as build custom cloud platforms, enabling 2D/3D positioning and display of sensors, visualizing alarm information, and shortening response times to meet higher-level requirements.Source: Li Yaoxing, Zheng Gongming. Design of a Cloud-Based Fire Alarm Monitoring System for Buildings Based on LoRa Technology. Fire Science and Technology, 2021, 40(09): 1373-1376.

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