scholarly journals Edge-Oriented Computing: A Survey on Research and Use Cases

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 452
Author(s):  
Nour Alhuda Sulieman ◽  
Lorenzo Ricciardi Celsi ◽  
Wei Li ◽  
Albert Zomaya ◽  
Massimo Villari
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Autonomous Vehicles ◽  
Research Field ◽  
Edge Computing ◽  
Use Cases ◽  
Comprehensive Overview ◽  
System Architectures ◽  
Computing Paradigm ◽  
Edge Networks

Edge computing is a distributed computing paradigm such that client data are processed at the periphery of the network, as close as possible to the originating source. Since the 21st century has come to be known as the century of data due to the rapid increase in the quantity of exchanged data worldwide (especially in smart city applications such as autonomous vehicles), collecting and processing such data from sensors and Internet of Things devices operating in real time from remote locations and inhospitable operating environments almost anywhere in the world is a relevant emerging need. Indeed, edge computing is reshaping information technology and business computing. In this respect, the paper is aimed at providing a comprehensive overview of what edge computing is as well as the most relevant edge use cases, tradeoffs, and implementation considerations. In particular, this review article is focused on highlighting (i) the most recent trends relative to edge computing emerging in the research field and (ii) the main businesses that are taking operations at the edge as well as the most used edge computing platforms (both proprietary and open source). First, the paper summarizes the concept of edge computing and compares it with cloud computing. After that, we discuss the challenges of optimal server placement, data security in edge networks, hybrid edge-cloud computing, simulation platforms for edge computing, and state-of-the-art improved edge networks. Finally, we explain the edge computing applications to 5G/6G networks and industrial internet of things. Several studies review a set of attractive edge features, system architectures, and edge application platforms that impact different industry sectors. The experimental results achieved in the cited works are reported in order to prove how edge computing improves the efficiency of Internet of Things networks. On the other hand, the work highlights possible vulnerabilities and open issues emerging in the context of edge computing architectures, thus proposing future directions to be investigated.

scholarly journals DM-MQTT: An Efficient MQTT Based on SDN Multicast for Massive IoT Communications

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3071 ◽  
Author(s):  
Jun-Hong Park ◽  
Hyeong-Su Kim ◽  
Won-Tae Kim
Keyword(s):  
Cloud Computing ◽  
Traffic Congestion ◽  
Data Exchange ◽  
Data Transfer ◽  
Edge Computing ◽  
Computing Paradigm ◽  
Transfer Delay ◽  
Network Usage ◽  
Iot Devices ◽  
Edge Networks

Edge computing is proposed to solve the problem of centralized cloud computing caused by a large number of IoT (Internet of Things) devices. The IoT protocols need to be modified according to the edge computing paradigm, where the edge computing devices for analyzing IoT data are distributed to the edge networks. The MQTT (Message Queuing Telemetry Transport) protocol, as a data distribution protocol widely adopted in many international IoT standards, is suitable for cloud computing because it uses a centralized broker to effectively collect and transmit data. However, the standard MQTT may suffer from serious traffic congestion problem on the broker, causing long transfer delays if there are massive IoT devices connected to the broker. In addition, the big data exchange between the IoT devices and the broker decreases network capability of the edge networks. The authors in this paper propose a novel MQTT with a multicast mechanism to minimize data transfer delay and network usage for the massive IoT communications. The proposed MQTT reduces data transfer delays by establishing bidirectional SDN (Software Defined Networking) multicast trees between the publishers and the subscribers by means of bypassing the centralized broker. As a result, it can reduce transmission delay by 65% and network usage by 58% compared with the standard MQTT.

An Edge-Computing Paradigm for Internet of Things over Power Line Communication Networks

IEEE Network ◽  
2020 ◽  
Vol 34 (2) ◽  
pp. 262-269
Author(s):  
Yuwen Qian ◽  
Long Shi ◽  
Jun Li ◽  
Xiangwei Zhou ◽  
Feng Shu ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Communication Networks ◽  
Power Line ◽  
Edge Computing ◽  
Power Line Communication ◽  
Computing Paradigm

Examining IoT's Applications Using Cloud Services

2018 ◽  
pp. 147-163 ◽  
Author(s):  
Saravanan K ◽  
P. Srinivasan
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Distributed Computing ◽  
Knowledge Sharing ◽  
Effective Communication ◽  
Cloud Services ◽  
On Demand ◽  
Computing Paradigm ◽  
Iot Devices ◽  
And Storage

Cloud IoT has evolved from the convergence of Cloud computing with Internet of Things (IoT). The networked devices in the IoT world grow exponentially in the distributed computing paradigm and thus require the power of the Cloud to access and share computing and storage for these devices. Cloud offers scalable on-demand services to the IoT devices for effective communication and knowledge sharing. It alleviates the computational load of IoT, which makes the devices smarter. This chapter explores the different IoT services offered by the Cloud as well as application domains that are benefited by the Cloud IoT. The challenges on offloading the IoT computation into the Cloud are also discussed.

Distributed Intelligence Platform to the Edge Computing

2020 ◽  
pp. 108-130 ◽  
Author(s):  
Xalphonse Inbaraj
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Traffic Control ◽  
Edge Computing ◽  
Connected Vehicle ◽  
Public Cloud ◽  
Distributed Intelligence ◽  
Pros And Cons ◽  
And Performance ◽  
Smart Cars

With the explosion of information, devices, and interactions, cloud design on its own cannot handle the flow of data. While the cloud provides us access to compute, storage, and even connectivity that we can access easily and cost-effectively, these centralized resources can create delays and performance issues for devices and information that are far from a centralized public cloud or information center source. Internet of things-connected devices are a transparent use for edge computing architecture. In this chapter, the author discusses the main differences between edge, fog, and cloud computing; pros and cons; and various applications, namely, smart cars and traffic control in transportation scenario, visual and surveillance security, connected vehicle, and smart ID card.

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 Research on Intelligent Scheduling Mechanism in Edge Network for Industrial Internet of Things

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Zhenzhong Zhang ◽  
Wei Sun ◽  
Yanliang Yu
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Task Scheduling ◽  
Edge Computing ◽  
The Internet ◽  
Computing Technology ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Internet Of Things Technology

With the vigorous development of the Internet of Things, the Internet, cloud computing, and mobile terminals, edge computing has emerged as a new type of Internet of Things technology, which is one of the important components of the Industrial Internet of Things. In the face of large-scale data processing and calculations, traditional cloud computing is facing tremendous pressure, and the demand for new low-latency computing technologies is imminent. As a supplementary expansion of cloud computing technology, mobile edge computing will sink the computing power from the previous cloud to a network edge node. Through the mutual cooperation between computing nodes, the number of nodes that can be calculated is more, the types are more comprehensive, and the computing range is even greater. Broadly, it makes up for the shortcomings of cloud computing technology. Although edge computing technology has many advantages and has certain research and application results, how to allocate a large number of computing tasks and computing resources to computing nodes and how to schedule computing tasks at edge nodes are still challenges for edge computing. In view of the problems encountered by edge computing technology in resource allocation and task scheduling, this paper designs a dynamic task scheduling strategy for edge computing with delay-aware characteristics, which realizes the reasonable utilization of computing resources and is required for edge computing systems. This paper proposes a resource allocation scheme combined with the simulated annealing algorithm, which minimizes the overall performance loss of the system while keeping the system low delay. Finally, it is verified through experiments that the task scheduling and resource allocation methods proposed in this paper can significantly reduce the response delay of the application.

scholarly journals An efficient fog computing for comprising approach to avoid data theft attack

2018 ◽  
Vol 7 (2.8) ◽  
pp. 680
Author(s):  
T Pavan Kumar ◽  
B Eswar ◽  
P Ayyappa Reddy ◽  
D Sindhu Bhargavi
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Paradigm Shift ◽  
Fog Computing ◽  
Edge Computing ◽  
New Paradigm ◽  
Computing Power ◽  
Data Theft

Cloud computing has become a new paradigm shift in the IT world because of its revolutionary model of computing. It provides flexibility, scalability, and reliability and decreased operational and support expenses for an organization. The Enterprise edition software’s are very costly and maintaining a separate IT team and maintaining their own servers is very expensive and that’s the reason why most of the companies are opting for Cloud computing over enterprise edition of the software. However, few organization cloud customers are not willing to step to cloud computing up on a big scale because of the safety problems present in cloud computing. One more disadvantage of Cloud is it’s not suitable for another revolutionary technology i.e.IoT(Internet of things)In this paper we are going to present the Advantages of Fog Computing and Decoy technology to address the security in cloud computing by extending it into fog computing.Fog Computing is a new paradigm in which the computing power moves to the edge of the network. So, it’s also called as Edge Computing.

scholarly journals Optimized Mobile Edge Computing Framework for IoT based Medical Sensor Network Nodes

2021 ◽  
Vol 3 (1) ◽  
pp. 33-42
Author(s):  
Jennifer S. Raj
Keyword(s):  
Cloud Computing ◽  
Wearable Sensors ◽  
Resource Limitation ◽  
Transmission Efficiency ◽  
Edge Computing ◽  
Task Completion ◽  
Network Nodes ◽  
Computing Paradigm ◽  
Edge Network ◽  
Initial Stage

Edge computing is a new computing paradigm that is rapidly emerging in various fields. Task completion is performed by various edge devices with distributed cloud computing in several conventional applications. Resource limitation, transmission efficiency, functionality and other edge network based circumstantial factors make this system more complex when compared to cloud computing. During cooperation between the edge devices, an instability occurs that cannot be ignored. The edge cooperative network is optimized with a novel framework proposed in this paper. This helps in improving the efficiency of edge computing tasks. The cooperation evaluation metrics are defined in the initial stage. Further, the performance of specific tasks are improved by optimizing the edge network cooperation. Real datasets obtained from elderly people and their wearable sensors is used for demonstrating the performance of the proposed framework. The extensive experimentation also helps in validating the efficiency of the proposed optimization algorithm.

scholarly journals Privacy in Cloud-Based Computing

2020 ◽  
pp. 239-252
Author(s):  
Monjur Ahmed ◽  
Nurul I. Sarkar
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Data Privacy ◽  
Fog Computing ◽  
Edge Computing ◽  
Third Party ◽  
Research Topics ◽  
Sensitive Data ◽  
Privacy Issues ◽  
Third Party Ownership

Cloud computing, internet of things (IoT), edge computing, and fog computing are gaining attention as emerging research topics and computing approaches in recent years. These computing approaches are rather conceptual and contextual strategies rather than being computing technologies themselves, and in practice, they often overlap. For example, an IoT architecture may incorporate cloud computing and fog computing. Cloud computing is a significant concept in contemporary computing and being adopted in almost every means of computing. All computing architectures incorporating cloud computing are termed as cloud-based computing (CbC) in general. However, cloud computing itself is the basis of CbC because it significantly depends on resources that are remote, and the remote resources are often under third-party ownership where the privacy of sensitive data is a big concern. This chapter investigates various privacy issues associated with CbC. The data privacy issues and possible solutions within the context of cloud computing, IoT, edge computing, and fog computing are also explored.

Sign in / Sign up

Export Citation Format

Share Document