IoT-based system for campus community security

Improving on-campus security measures to ensure the well-being of students and staff results in a significant enhancement in the overall quality of life. This research proposes an Internet of Things (IoT)-based system that leverages sound recognition to detect distress screams and quickly notify a central unit. The system uses an IoT device, Arduino, to collect data from the environment, processes it, and sends the information to a central unit via a cloud IoT service over a Low Power Wide Area Network. The system uses The Things Network and Amazon Web Services platforms to enable communication. The system design considers the resource limitations of Arduino devices and low-power infrastructure. To achieve local detection within the Arduino, several Convolutional Neural Network architectures were compared and evaluated for their effectiveness in scream detection. We evaluated the performance of our models based on accuracy and F1 score, achieving our best results with an accuracy of 95% and an F1 score of 93.4% for the scream class. In addition, the reception coverage in the selected area covers different types of terrain. The results demonstrate the feasibility of implementing an IoT system specifically designed to detect dangerous situations through mobile devices on campus in the surrounding areas. •Design of an Internet of Things system to support campus security.•Deployment of Convolutional Neural Networks in a resource constrained device.•Performance evaluation of Low Power Wide Area Network within an interest area.•Data Pipeline from data collection to data storage in the cloud.