Research and Design of Distributed Fire Alarm System of Indoor Internet of Things Based on LoRa

In order to comprehensively improve the sensitivity of fire warning and effectively shorten the warning time, this paper proposes and implements an indoor distributed fire alarm system based on low power wide area network. The system is mainly composed of three parts: a multisensor acquisition node based on LoRa technology, a distributed edge gateway, and a remote user monitoring system. The multisensor collection node obtains environmental parameters such as indoor temperature, smoke concentration, and air quality and then transmits the sensing data to edge gateway by LoRa after preprocessing. The edge gateway is based on an embedded Linux platform and is deployed in distributed state to collect and store data from multiple collection nodes. Besides, edge gateway forwards valid data to the remote user monitoring system by standard MQTT protocol. The user monitoring system displays current deployment area parameters to users in real time and provides early warning prompts based on relevant preset indicators to help the administrator make more accurate decisions on corresponding measures. The system has been deployed and tested in Nanjing Institute of Technology. By sensor calibration experiments, LoRa communication experiments, and system tests in different environments, the experimental results show that the average received signal strength in a small interference space is -104.12 dBm, and the average received signal strength in a noisy signal environment is -57.5 dBm. By setting the optimal transmitting power for each distance, the packet receiving rate can reach more than 95%, and the alarm accuracy can reach 100% under premise of ensuring the lowest power consumption. Finally, this paper conducts a comprehensive performance analysis on the wireless communication performance of environmental collection nodes, multisensor data fusion algorithm, distributed LoRa edge gateway deployment performance, and remote system early warning accuracy.