Application of Edge Computing-Based Information-Centric Networking in Smart Cities
Munqith Saleem1,*, Hanan Burhan Saadon2, Marwa S. Mahdi Hussin3, Tamarah Alaa Diame4, Raaid Alubady5, Mohd K. Abd Ghani6, Hatıra Günerhan7
1Medical instruments engineering techniques, Al-farahidi University, Baghdad, Iraq:
2Department of Medical Devices Engineering Technologies, National University of Science and Technology, Dhi Qar, Nasiriyah, Iraq
3Computer Technologies Engineering, Al-Turath University College, Baghdad,Iraq
4Technical Computer Engineering Department, Al-Kunooze University College, Basrah, Iraq
5Technical Engineering College, Al-Ayen University, Thi-Qar, Iraq
6Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal 76100, Malaysia
7Department of Mathematics, Faculty of Education, Kafkas University, Kars, Turkey
Emails: Munqith Saleem@uoalfarahidi.edu.iq; hanan.saadon@nust.edu.iq; marwa.saad@turath.edu.iq; Tamarah. Alaa@Kunoozu. Edu. Iq; alubadyraaid@alayen.edu.iq; khanapi@utem.edu.my; hatira.gunerhan@kafkas.edu.tr
*Corresponding Author: Munqith Saleem@uoalfarahidi.edu.iq
Abstract
Many data resources and network availability are needed for the smart city applications to execute at their highest efficiency level. Demand for these objects is driving up data traffic, which in turn is placing strain on the network. The 5G-enabled Internet of Things applications address these difficulties in smart city applications. This article proposes an Information-centric Networking System using Multiaccess Edge Computing (ICNMEC) to reduce computation offloading and optimize data traffic. This system's 5G network slicing approaches combine edge computing and software characterization. Internet of Things applications have been used to store and analyze the information gathered. In addition, an algorithm known as OMNM (Optimized Memory Network Management) is created to control network traffic better and better use of storage. With minimal delays, network traffic, and storage ratio, the system's modeling tests demonstrate that it is very efficient. This method can progressively enhance the pace at which one can access and use the system. The performance assessment shows that the proposed method can improve the efficiency ratio of 95.141%, storage utilization ratio of 60.1%, and access rate by 0.9, reducing network traffic and delay by 0.6.
Keywords: Edge computing; networking; information access; smart city; memory