scholarly journals Dependable and Secure Voting Mechanism in Edge Computing

2019 ◽  
Vol 11 (12) ◽  
pp. 262
Author(s):  
Pedro A.R.S. Costa ◽  
Marko Beko
Keyword(s):  
Middle Layer ◽  
Edge Computing ◽  
Cloud Environment ◽  
Computing Paradigm ◽  
Edge Network ◽  
And Storage ◽  
The Internet Of Things ◽  
Multi Cloud ◽  
Voting Mechanism ◽  
Open Issues

Edge computing is a distributed computing paradigm that encompasses data computing and storage and is performed close to the user, efficiently guaranteeing faster response time. This paradigm plays a pivotal role in the world of the Internet of Things (IoT). Moreover, the concept of the distributed edge cloud raises several interesting open issues, e.g., failure recovery and security. In this paper, we propose a system composed of edge nodes and multiple cloud instances, as well as a voting mechanism. The multi-cloud environment aims to perform centralized computations, and edge nodes behave as a middle layer between edge devices and the cloud. Moreover, we present a voting mechanism that leverages the edge network to validate the performed computation that occurred in the centralized environment.

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.

Programmable Implementation and Blockchain Security Scheme Based on Edge Computing Firework Model

2021 ◽  
Vol 16 (1) ◽  
pp. 1-22
Author(s):  
Bao Yi Qin ◽  
Zheng Hao ◽  
Zhao Qiang
Keyword(s):  
Data Transmission ◽  
Edge Computing ◽  
Security Requirements ◽  
Maintenance Cost ◽  
Security Scheme ◽  
Transmission Cost ◽  
Heterogeneous Nature ◽  
Security And Reliability ◽  
And Storage ◽  
The Internet Of Things

In cloud computing, since the program runs in cloud, it can be written in programming language and maintained only in the cloud after compilation. Due to the heterogeneous nature of the edge node platform, many tasks are migrated from the cloud to the edge terminal. It is not easy to realize the programming under the edge computing, and the maintenance cost is also high. At the same time, because the programmable is a high-risk activity, it has high security requirements. In order to solve this problem, this paper designs a programmable and blockchain security scheme based on the edge computing firework model, realizes the programming of the internet of things (IoT) gateway firework node under the edge computing, and appreciates the safe transmission and storage of programmable data through the blockchain system. The experimental results show that this scheme not only facilitates the user's programming, enhances the real-time performance, and saves the data transmission cost, but also ensures the security and reliability of the system.

Edge Architecture Integration of Technologies

2021 ◽  
pp. 1-30
Author(s):  
Sandhya Devi R. S. ◽  
Vijaykumar V. R. ◽  
Sivakumar P. ◽  
Neeraja Lakshmi A. ◽  
Vinoth Kumar B.
Keyword(s):  
Data Privacy ◽  
Response Times ◽  
Management Strategies ◽  
Cost Savings ◽  
Edge Computing ◽  
Battery Life ◽  
Computing Platform ◽  
Computing Paradigm ◽  
Time Latency ◽  
The Internet Of Things

The enormous growth of the internet of things (IoT) and cloud-based services have paved the way for edge computing, the new computing paradigm which processes the data at the edge of the network. Edge computing resolves issues related to response time, latency, battery life limitation, cost savings for bandwidth, as well as data privacy and protection. The architecture brings devices and data back to the consumer. This model of computing as a distributed IT system aims at satisfying end-user demands with faster response times by storing data closer to it. The enormous increase in individuals and locations, connected devices such as appliances, laptops, smartphones, and transport networks that communicate with each other has raised exponentially. Considering these factors in this chapter, edge computing architecture along with the various components that constitute the computing platform are discussed. The chapter also discusses resource management strategies deliberate for edge computing devices and integration of various computing technologies to support efficient IoT architecture.

scholarly journals Modified reinforcement learning based- caching system for mobile edge computing

2020 ◽  
pp. 1-16
Author(s):  
Sarra Mehamel ◽  
Samia Bouzefrane ◽  
Soumya Banarjee ◽  
Mehammed Daoui ◽  
Valentina E. Balas
Keyword(s):  
Reinforcement Learning ◽  
Mobile Networks ◽  
Mobile Network ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Network Nodes ◽  
Caching Strategy ◽  
Edge Network ◽  
The Right ◽  
And Storage

Caching contents at the edge of mobile networks is an efficient mechanism that can alleviate the backhaul links load and reduce the transmission delay. For this purpose, choosing an adequate caching strategy becomes an important issue. Recently, the tremendous growth of Mobile Edge Computing (MEC) empowers the edge network nodes with more computation capabilities and storage capabilities, allowing the execution of resource-intensive tasks within the mobile network edges such as running artificial intelligence (AI) algorithms. Exploiting users context information intelligently makes it possible to design an intelligent context-aware mobile edge caching. To maximize the caching performance, the suitable methodology is to consider both context awareness and intelligence so that the caching strategy is aware of the environment while caching the appropriate content by making the right decision. Inspired by the success of reinforcement learning (RL) that uses agents to deal with decision making problems, we present a modified reinforcement learning (mRL) to cache contents in the network edges. Our proposed solution aims to maximize the cache hit rate and requires a multi awareness of the influencing factors on cache performance. The modified RL differs from other RL algorithms in the learning rate that uses the method of stochastic gradient decent (SGD) beside taking advantage of learning using the optimal caching decision obtained from fuzzy rules.

scholarly journals Design and Implementation of the E-Switch for a Smart Home

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3811
Author(s):  
Fabian García-Vázquez ◽  
Héctor A. Guerrero-Osuna ◽  
Gerardo Ornelas-Vargas ◽  
Rocío Carrasco-Navarro ◽  
Luis F. Luque-Vega ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Mobile Application ◽  
Smart Home ◽  
The Internet ◽  
Cloud Environment ◽  
Design And Implementation ◽  
Methods And Techniques ◽  
And Storage ◽  
The Internet Of Things ◽  
Reducing Energy

As the development of systems in smart homes is increasing, it is of ever-increasing importance to have data, which artificial intelligence methods and techniques can apply to recognize activities and patterns or to detect anomalies, with the aim of reducing energy consumption in the main home domestic services, and to offer users an alternative in the management of these resources. This paper describes the design and implementation of a platform based on the internet of things and a cloud environment that allows the user to remotely control and monitor Wi-Fi wireless e-switch in a home through a mobile application. This platform is intended to represent the first step in transforming a home into a smart home, and it allows the collection and storage of the e-switch information, which can be used for further processing and analysis.

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 Data Security and Privacy-Preserving in Edge Computing Paradigm: Survey and Open Issues

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 18209-18237 ◽  
Author(s):  
Jiale Zhang ◽  
Bing Chen ◽  
Yanchao Zhao ◽  
Xiang Cheng ◽  
Feng Hu
Keyword(s):  
Data Security ◽  
Privacy Preserving ◽  
Edge Computing ◽  
Security And Privacy ◽  
Computing Paradigm ◽  
Open Issues

Fog Computing

Fog Computing ◽  
2018 ◽  
pp. 198-207 ◽  
Author(s):  
Chintan M. Bhatt ◽  
C. K. Bhensdadia
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Real Time ◽  
Fog Computing ◽  
The Internet ◽  
Research Issues ◽  
Computing Paradigm ◽  
Time Interaction ◽  
Edge Network ◽  
The Internet Of Things

The Internet of Things could be a recent computing paradigm, defined by networks of extremely connected things – sensors, actuators and good objects – communication across networks of homes, buildings, vehicles, and even individuals whereas cloud computing could be ready to keep up with current processing and machine demands. Fog computing provides architectural resolution to deal with some of these issues by providing a layer of intermediate nodes what's referred to as an edge network [26]. These edge nodes provide interoperability, real-time interaction, and if necessary, computational to the Cloud. This paper tries to analyse different fog computing functionalities, tools and technologies and research issues.

A Universal Storage Architecture for Heterogeneous Data of Internet of Things

2015 ◽  
Vol 713-715 ◽  
pp. 2423-2426
Author(s):  
Yong Lin Leng ◽  
Qing Chen Zhang ◽  
Yue Wang
Keyword(s):  
Internet Of Things ◽  
Data Storage ◽  
Rapid Development ◽  
Data Preprocessing ◽  
Heterogeneous Data ◽  
Cloud Environment ◽  
Subsequent Work ◽  
Internet Of Things Technology ◽  
And Storage ◽  
The Internet Of Things

With the rapid development of the Internet of Things technology, IOT terminal equipments collect a large amount of data. So the preprocessing and storage become a big challenge. In this paper we presents a universal preprocessing and storage architecture for IOT data in cloud environment. In the data preprocessing module, with the sensor equipments are not stable, a large number of mistake, missing data will be produced, so we propose a imputation algorithm based on clustering to perform data preprocessing. For the data storage module, because of the existence of a large number of unstructured and semi-structured data, we present a storage architecture for heterogeneous data in cloud environment. Experiments show that our architecture can effectively complete the data preprocessing and storage, for the subsequent work, such as query data provides good support.

scholarly journals Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 779
Author(s):  
Shichao Chen ◽  
Qijie Li ◽  
Mengchu Zhou ◽  
Abdullah Abusorrah
Keyword(s):  
Problem Formulation ◽  
Computing System ◽  
High Energy ◽  
Edge Computing ◽  
Computation Offloading ◽  
Future Research ◽  
Research Issues ◽  
Computing Paradigm ◽  
Collaborative Scheduling ◽  
And Storage

In edge computing, edge devices can offload their overloaded computing tasks to an edge server. This can give full play to an edge server’s advantages in computing and storage, and efficiently execute computing tasks. However, if they together offload all the overloaded computing tasks to an edge server, it can be overloaded, thereby resulting in the high processing delay of many computing tasks and unexpectedly high energy consumption. On the other hand, the resources in idle edge devices may be wasted and resource-rich cloud centers may be underutilized. Therefore, it is essential to explore a computing task collaborative scheduling mechanism with an edge server, a cloud center and edge devices according to task characteristics, optimization objectives and system status. It can help one realize efficient collaborative scheduling and precise execution of all computing tasks. This work analyzes and summarizes the edge computing scenarios in an edge computing paradigm. It then classifies the computing tasks in edge computing scenarios. Next, it formulates the optimization problem of computation offloading for an edge computing system. According to the problem formulation, the collaborative scheduling methods of computing tasks are then reviewed. Finally, future research issues for advanced collaborative scheduling in the context of edge computing are indicated.

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