Simulation and Analysis of Mathematical Methods in Real-Time Engineering Applications

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1 *Corresponding author: akshathay@presidencyuniversity.in

2 †Corresponding author: pravinthraja@gmail.com

P. Vijayakumar*, Prithiviraj Rajalingam and S. V. K. R. Rajeswari

ECE Department, SRMIST, Kattankulathur, Chennai, India

Abstract

The rapid growth of the Internet of Things (IoT) with advanced applications requires high speed and real-time computing power. Edge computing brings the computation of data closer to the machine where it is being collected. It leads to a decrease in latency, bandwidth usage, and resources for the server and its cost. The significant challenges in edge computing are 1) optimal offloading decision making, 2) resource allocation, 3) Meeting Quality-of-Service (QoS) and Experience (QoE). This chapter addresses the above challenges using mathematical models, Deep Learning and the Evolutionary algorithm. The deep learning algorithm solves the highly complex problem by developing a model from the training data or observation (reinforcement learning). The deep learning approach converts the optimization problem of edge computing into classification or regression or intelligent decision-making problems and solves them. The Evolution algorithm finds an optimum solution for the given problem through the natural process of evaluation, which is used to solve the edge computing multi-optimization problem. An evolution algorithm like a genetic algorithm and ant colony can solve a few research problems of edge computing like task scheduling.

Keywords:Edge computing, deep learning, machine learning, evolutionary algorithm

Edge computing is a new distributed computing paradigm. The pattern of edge computing is closer to the location as a platform for computation and data storage before working with the cloud. In simpler terms, edge computing works with smaller and real-time data, whereas cloud works with big data. Edge computing helps in quick response times and also saves bandwidth [1, 2]. In the use case of cloud-based augmented reality applications, latency and processing limitations are key challenges to implementing the cloud system due to geographical distance from the infrastructure. Edge computing comes into the picture as an advancement of cloud gaming as it allows short-distance travel of data. Edge computing has the advantages of reducing lag times and latency [3]. Mostly edge computing has a role in helping cloud-based IoT systems to provide computational service. A small recap of the Cloud-Based IoT system is provided below.

The Internet of Things (IoT) plays a vital role in human daily life by making all the devices connected through the internet, and it works ingeniously. Day by day, the IoT plays a crucial role in all the domains [4]. For example, IoT provides excellent service to medical applications like tracking patient status, heart rate, blood pressure, and sugar level can be monitored, and if a patient goes into a critical or unstable condition, the doctor can provide solutions through the report generated by the IoT application [6]. The IoT data can also be used to study different patients’ lifestyles and activities to prevent them from going into a critical situation. Therefore, the IoT has developed opportunities to provide brilliant solutions with many predictions and intelligence.

IoT devices are correctly functioning because of several technologies like cloud computing that give many advantages to IoT devices, including storage infrastructure, processing the real-time data in IoT devices, and high-performance computing. It leads to cloud computing as a revolutionary part of IoT devices, which provides smart and self-predicted data [6]. Due to IoT devices’ evolution, cloud providers take an immense advantage to provide the communication or transfer of data between the IoT devices. This results in the Cloud of Things, which connects both cloud computing and IoT devices.