IoT-based Smart Campus Monitoring Based on an Improved Chimp Optimization-Based Deep Belief Neural Network

Abstract

Internet of Things (IoT) is a fast emerging technology that gained momentum steadily and shaped the future of the smart world. It has been created from the curiosity of human beings to provide comfortable and connected lifestyles with the mitigation of labor and therein promptly reduces the errors. This led to the usage of smart devices in everyday activities and thus enhances the efficacy of all smart applications. Smart applications include smart farming, healthcare, smart grid, smart city, and more. The application of IoT in monitoring the smart campus is an inevitable one to monitor the attendance of students and monitoring other activitieson the campus to protect the students and improve the education standards. Most education institutes use smart classrooms to achieve the aforementioned quality. Smart classrooms include audio-visual aids, multimedia, and smart boards along with these it is ineluctable to monitor the activities such as students’ attendance, analyzing the students-faculty performance, and content deliveries. To record the students’ attendance automatically we propose a Bluetooth-enabled IoT smart system for the positing of students with low energy utilization. The attendance can be recorded in the cloud environment by the Received signal strength indicator (RSSI). To achieve this we propose a novel IoT-based Deep Belief Neural Network (DBN) based Improved Chimp Optimization algorithm (ICO) for monitoring the attendance and positioning of the students’. An experimental study is conducted on Raspberry Pi with the deployment of Python and shows that our proposed approach provides better accuracy even with high interference signals.