Optical fog‐assisted cyber‐physical system for intelligent surveillance in the education system

Abstract

AbstractNowadays, surveillance systems are becoming popular for safety and security purposes in many sectors like government organizations, private institutes, hospitals, schools, residential societies, etc. The role of intelligent surveillance in the education system fulfills diverse requirements such as general surveillance of the campus, recording of a lecture, monitoring behavior of student/teacher, tracking, students, administrative surveillance and security surveillance, etc. In the present educational system, the traditional cloud/fog‐based cyber‐physical system (CPS) and surveillance system enable remote monitoring, machine learning, analytics, and early decision making for real‐time tasks. The existing solutions are inadequate to fulfill the requirements of ultra‐low delay and minimum energy consumption of the surveillance devices. In this paper, an optical fog‐assisted CPS is presented for intelligent surveillance in the education system that uses optical resources at the optical fog layer as fog devices for facilitating early decisions or actions with no delay, minimum energy consumption, and optimum usage of the bandwidth. The proposed system provides (a) a scalable smart OpticalFog node in the middleware of cloud and surveillance devices and (b) service assurance to various CPS‐based applications through the Fog manager. An optimum placement algorithm is proposed for the Fog manager to place the CPS‐based tasks on the nearby optical fog device. The iFogSim toolkit is used for evaluating the proposed algorithm in realizing CPS‐based surveillance in the education scenario. It can be observed from the results that delay in the traditional scenarios for different configurations of surveilled blocks are varying from 210 to 7,867 ms. However, the proposed system has provided ultra‐low delay in the range of 7.6–8.93 ms. Similarly, the energy consumption of surveillance cameras is significantly reduced. Moreover, network usage by the proposed framework is also less than the traditional system. Hence, results show the effectiveness of the proposed framework.