scholarly journals Analysis of Security Issues of Edge Computing Based Internet of Things Applications

2020 ◽  
Author(s):  
Shamim Muhammad ◽  
Inderveer Chana ◽  
Supriya Thilakanathan
Keyword(s):  
Internet Of Things ◽  
Edge Computing ◽  
The Internet ◽  
Security Vulnerabilities ◽  
Computing Power ◽  
Security Issues ◽  
Computing Paradigm ◽  
Improved Performance ◽  
The Internet Of Things ◽  
Common Security

Edge computing is a technology that allows resources to be processed or executed close to the edge of the internet. The interconnected network of devices in the Internet of Things has led to an increased amount of data, increasing internet traffic usage every year. Also, edge computing is driving applications and computing power away from the integrated points to areas close to users, leading to improved performance of the application. Despite the explosive growth of the edge computing paradigm, there are common security vulnerabilities associated with the Internet of Things applications. This paper will evaluate and analyze some of the most common security issues that pose a serious threat to the edge computing paradigm.

scholarly journals A New Task Offloading Algorithm in Edge Computing

2021 ◽  
Author(s):  
Zhenjiang Zhang ◽  
Chen Li ◽  
ShengLung Peng ◽  
Xintong Pei
Keyword(s):  
Internet Of Things ◽  
User Experience ◽  
Mobile Internet ◽  
Edge Computing ◽  
The Internet ◽  
Disruptive Technology ◽  
Computing Power ◽  
New Applications ◽  
The Internet Of Things ◽  
Task Offloading

Abstract In the last few years, the Internet of Things (IOT), as a new disruptive technology, has gradually changed the world. With the prosperous development of the mobile Internet and the rapid growth of the Internet of Things, various new applications continue to emerge, such as mobile payment, face recognition, wearable devices, driverless, VR/AR, etc. Although the computing power of mobile terminals is getting higher and the traditional cloud computing model has higher computing power, it is often accompanied by higher latency and cannot meet the needs of users. In order to reduce user delay to improve user experience, and at the same time reduce network load to a certain extent, edge computing, as an application of IOT, came into being. In view of the new architecture after dating edge computing, this paper focuses on the task offloading in edge computing, from task migration in multi-user scenarios and edge server resource management expansion, and proposes a multi-agent load balancing distribution based on deep reinforcement learning DTOMALB, a distributed task allocation algorithm, can perform a reasonable offload method for this scenario to improve user experience and balance resource utilization. Simulations show that the algorithm has a certain adaptability compared to the traditional algorithm in the scenario of multi-user single cell, and reduces the complexity of the algorithm compared to the centralized algorithm, and reduces the average response delay of the overall user. And balance the load of each edge computing server, improve the robustness and scalability of the system.

scholarly journals A New Task Offloading Algorithm in Edge Computing

2020 ◽  
Author(s):  
Zhenjiang Zhang ◽  
Chen Li ◽  
ShengLung Peng ◽  
Xintong Pei
Keyword(s):  
Internet Of Things ◽  
User Experience ◽  
Mobile Internet ◽  
Edge Computing ◽  
The Internet ◽  
Disruptive Technology ◽  
Computing Power ◽  
New Applications ◽  
The Internet Of Things ◽  
Task Offloading

Abstract In the last few years, the Internet of Things (IOT), as a new disruptive technology, has gradually changed the world. With the prosperous development of the mobile Internet and the rapid growth of the Internet of Things, various new applications continue to emerge, such as mobile payment, face recognition, wearable devices, driverless, VR/AR, etc. Although the computing power of mobile terminals is getting higher and the traditional cloud computing model has higher computing power, it is often accompanied by higher latency and cannot meet the needs of users. In order to reduce user delay to improve user experience, and at the same time reduce network load to a certain extent, edge computing, as an application of IOT, came into being. In view of the new architecture after dating edge computing, this paper focuses on the task offloading in edge computing, from task migration in multi-user scenarios and edge server resource management expansion, and proposes a multi-agent load balancing distribution based on deep reinforcement learning DTOMALB, a distributed task allocation algorithm, can perform a reasonable offload method for this scenario to improve user experience and balance resource utilization. Simulations show that the algorithm has a certain adaptability compared to the traditional algorithm in the scenario of multi-user single cell, and reduces the complexity of the algorithm compared to the centralized algorithm, and reduces the average response delay of the overall user. And balance the load of each edge computing server, improve the robustness and scalability of the system.

scholarly journals A new task offloading algorithm in edge computing

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Zhenjiang Zhang ◽  
Chen Li ◽  
ShengLung Peng ◽  
Xintong Pei
Keyword(s):  
Internet Of Things ◽  
User Experience ◽  
Mobile Internet ◽  
Edge Computing ◽  
The Internet ◽  
Disruptive Technology ◽  
Computing Power ◽  
New Applications ◽  
The Internet Of Things ◽  
Task Offloading

AbstractIn the last few years, the Internet of Things (IOT), as a new disruptive technology, has gradually changed the world. With the prosperous development of the mobile Internet and the rapid growth of the Internet of Things, various new applications continue to emerge, such as mobile payment, face recognition, wearable devices, driverless, VR/AR, etc. Although the computing power of mobile terminals is getting higher and the traditional cloud computing model has higher computing power, it is often accompanied by higher latency and cannot meet the needs of users. In order to reduce user delay to improve user experience, and at the same time reduce network load to a certain extent, edge computing, as an application of IOT, came into being. In view of the new architecture after dating edge computing, this paper focuses on the task offloading in edge computing, from task migration in multi-user scenarios and edge server resource management expansion, and proposes a multi-agent load balancing distribution based on deep reinforcement learning DTOMALB, a distributed task allocation algorithm, can perform a reasonable offload method for this scenario to improve user experience and balance resource utilization. Simulations show that the algorithm has a certain adaptability compared to the traditional algorithm in the scenario of multi-user single cell, and reduces the complexity of the algorithm compared to the centralized algorithm, and reduces the average response delay of the overall user. And balance the load of each edge computing server, improve the robustness and scalability of the system.

scholarly journals Edge Computing for the Internet of Things: A Case Study

2018 ◽  
Vol 5 (2) ◽  
pp. 1275-1284 ◽  
Author(s):  
Gopika Premsankar ◽  
Mario Di Francesco ◽  
Tarik Taleb
Keyword(s):  
Internet Of Things ◽  
Edge Computing ◽  
The Internet ◽  
The Internet Of Things

Distributing Computing in the Internet of Things: Cloud, Fog and Edge Computing Overview

2017 ◽  
pp. 87-115 ◽  
Author(s):  
P. J. Escamilla-Ambrosio ◽  
A. Rodríguez-Mota ◽  
E. Aguirre-Anaya ◽  
R. Acosta-Bermejo ◽  
M. Salinas-Rosales
Keyword(s):  
Internet Of Things ◽  
Edge Computing ◽  
The Internet ◽  
The Internet Of Things

Bridging the IoT Gap Through Edge Computing

2019 ◽  
pp. 1-9
Author(s):  
R. I. Minu ◽  
G. Nagarajan
Keyword(s):  
Internet Of Things ◽  
Information Needs ◽  
Edge Computing ◽  
The Internet ◽  
Cloud Server ◽  
The Cost ◽  
The Internet Of Things ◽  
Clear Objective ◽  
Origin Point

In the present-day scenario, computing is migrating from the on-premises server to the cloud server and now, progressively from the cloud to Edge server where the data is gathered from the origin point. So, the clear objective is to support the execution and unwavering quality of applications and benefits, and decrease the cost of running them, by shortening the separation information needs to travel, subsequently alleviating transmission capacity and inactivity issues. This chapter provides an insight of how the internet of things (IoT) connects with edge computing.

A Review of Cryptographic Algorithms for the Internet of Things

2019 ◽  
pp. 67-93 ◽  
Author(s):  
Issmat Shah Masoodi ◽  
Bisma Javid
Keyword(s):  
Internet Of Things ◽  
Power Consumption ◽  
The Internet ◽  
Reference Architecture ◽  
Memory Space ◽  
Security Issues ◽  
Research Challenges ◽  
Short Period ◽  
Cryptographic Algorithms ◽  
The Internet Of Things

There are various emerging areas in which profoundly constrained interconnected devices connect to accomplish specific tasks. Nowadays, internet of things (IoT) enables many low-resource and constrained devices to communicate, do computations, and make smarter decisions within a short period. However, there are many challenges and issues in such devices like power consumption, limited battery, memory space, performance, cost, and security. This chapter presents the security issues in such a constrained environment, where the traditional cryptographic algorithms cannot be used and, thus, discusses various lightweight cryptographic algorithms in detail and present a comparison between these algorithms. Further, the chapter also discusses the power awakening scheme and reference architecture in IoT for constrained device environment with a focus on research challenges, issues, and their solutions.

Sign in / Sign up

Export Citation Format

Share Document