Application of wireless sensor networks (WSN) and Internet of Things (IoT) used to provide real-time monitoring of fire outbreak in markets. The system integrates three subsystems namely; sensing subsystem which uses multiple sensors for detecting fire outbreaks. Data processing subsystem which collects data from the sensing subsystem through Xbee, analyses, and uploads data to the cloud. If values exceed the sensor threshold, an alarm is triggered and notification is sent to stakeholders via mobile application subsystem. The integration between sensing, data processing, and mobile application subsystems pave a new way for the mitigation of fire outbreaks at its early stage.

The existing systems normally used to monitor the industrial environmental condition in factories in Kenya are mostly wired systems and more expensive. Despite the benefits of tools and emerging technology, most companies do not use automated systems due to the high installation and maintenance costs and a lack of formal early-warning mechanism. This research project intends to provide information using wireless sensor technology, which comprises of sensors, Zigbee, raspberry pi, Arduino Uno, and wireless sensor network (WSN). The system is developed using open-source hardware raspberry pi and Zigbee which proves to be cost-effective and has low power consumption. The sensors will collect the data of various environmental parameters and transmit it to the raspberry pi, which acts as a base station. The raspberry pi will then transmit the data using Zigbee, and the processed data displayed on GUI through the Zigbee on the receiver end. The database was developed using MYSQL DB and the web application written in the PHP programming language. In addidtion, the system is integrated with Zigbee technology to send SMS and e-mail notifications to the respective registered users. The developed system was tested at different plant areas to detect the concentration of Carbon monoxide, temperature, humidity, and dust in the factory. The result shows that the system responds positively when these parameters were detected and sends notification alerts via e-mail and SMS. Finally, the data collected can be used for further data analysis and research in the future.