Blockchain-based Edge Computing Data Storage Protocol Under Simplified Group Signature

2021 ◽  
pp. 1-1
Author(s):  
Zhiwei Wang
Keyword(s):  
Data Storage ◽  
Edge Computing ◽  
Group Signature

Using AI at the Edge and Incremental Machine Learning to Process Onboard Instrument Data

2021 ◽  
Author(s):  
Nicholas Parkyn
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Real Time ◽  
Data Storage ◽  
High Performance ◽  
Heterogeneous Computing ◽  
Low Cost ◽  
Machine Intelligence ◽  
Edge Computing ◽  
Continual Learning

Emerging heterogeneous computing, computing at the edge, machine learning and AI at the edge technology drives approaches and techniques for processing and analysing onboard instrument data in near real-time. The author has used edge computing and neural networks combined with high performance heterogeneous computing platforms to accelerate AI workloads. Heterogeneous computing hardware used is readily available, low cost, delivers impressive AI performance and can run multiple neural networks in parallel. Collecting, processing and machine learning from onboard instruments data in near real-time is not a trivial problem due to data volumes, complexities of data filtering, data storage and continual learning. Little research has been done on continual machine learning which aims at a higher level of machine intelligence through providing the artificial agents with the ability to learn from a non-stationary and never-ending stream of data. The author has applied the concept of continual learning to building a system that continually learns from actual boat performance and refines predictions previously done using static VPP data. The neural networks used are initially trained using the output from traditional VPP software and continue to learn from actual data collected under real sailing conditions. The author will present the system design, AI, and edge computing techniques used and the approaches he has researched for incremental training to realise continual learning.

PAGS: PUF-based Anonymous Group Signature for Node Authentication in Edge Computing

2021 ◽  
Vol 22 (6) ◽  
pp. 1229-1239
Author(s):  
Junqing Lu Junqing Lu ◽  
Jian Shen Junqing Lu ◽  
Chin-Feng Lai Jian Shen ◽  
Fei Gao Chin-Feng Lai
Keyword(s):  
Edge Computing ◽  
Group Signature

scholarly journals Microservice Security Agent Based On API Gateway in Edge Computing

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4905 ◽  
Author(s):  
Rongxu Xu ◽  
Wenquan Jin ◽  
Dohyeun Kim
Keyword(s):  
Data Storage ◽  
User Authentication ◽  
Edge Computing ◽  
Security And Privacy ◽  
Agent Based ◽  
Computing Platform ◽  
Authentication Mechanism ◽  
Computing Paradigm ◽  
Security Agent ◽  
Secure Authentication

Internet of Things (IoT) devices are embedded with software, electronics, and sensors, and feature connectivity with constrained resources. They require the edge computing paradigm, with modular characteristics relying on microservices, to provide an extensible and lightweight computing framework at the edge of the network. Edge computing can relieve the burden of centralized cloud computing by performing certain operations, such as data storage and task computation, at the edge of the network. Despite the benefits of edge computing, it can lead to many challenges in terms of security and privacy issues. Thus, services that protect privacy and secure data are essential functions in edge computing. For example, the end user’s ownership and privacy information and control are separated, which can easily lead to data leakage, unauthorized data manipulation, and other data security concerns. Thus, the confidentiality and integrity of the data cannot be guaranteed and, so, more secure authentication and access mechanisms are required to ensure that the microservices are exposed only to authorized users. In this paper, we propose a microservice security agent to integrate the edge computing platform with the API gateway technology for presenting a secure authentication mechanism. The aim of this platform is to afford edge computing clients a practical application which provides user authentication and allows JSON Web Token (JWT)-based secure access to the services of edge computing. To integrate the edge computing platform with the API gateway, we implement a microservice security agent based on the open-source Kong in the EdgeX Foundry framework. Also to provide an easy-to-use approach with Kong, we implement REST APIs for generating new consumers, registering services, configuring access controls. Finally, the usability of the proposed approach is demonstrated by evaluating the round trip time (RTT). The results demonstrate the efficiency of the system and its suitability for real-world applications.

scholarly journals A Review of Machine Learning Algorithms for Cloud Computing Security

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1379
Author(s):  
Umer Ahmed Butt ◽  
Muhammad Mehmood ◽  
Syed Bilal Hussain Shah ◽  
Rashid Amin ◽  
M. Waqas Shaukat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Data Storage ◽  
Response Times ◽  
Edge Computing ◽  
Machine Learning Algorithms ◽  
Information Storage ◽  
Future Research ◽  
Computer Algorithms ◽  
Advantages And Disadvantages

Cloud computing (CC) is on-demand accessibility of network resources, especially data storage and processing power, without special and direct management by the users. CC recently has emerged as a set of public and private datacenters that offers the client a single platform across the Internet. Edge computing is an evolving computing paradigm that brings computation and information storage nearer to the end-users to improve response times and spare transmission capacity. Mobile CC (MCC) uses distributed computing to convey applications to cell phones. However, CC and edge computing have security challenges, including vulnerability for clients and association acknowledgment, that delay the rapid adoption of computing models. Machine learning (ML) is the investigation of computer algorithms that improve naturally through experience. In this review paper, we present an analysis of CC security threats, issues, and solutions that utilized one or several ML algorithms. We review different ML algorithms that are used to overcome the cloud security issues including supervised, unsupervised, semi-supervised, and reinforcement learning. Then, we compare the performance of each technique based on their features, advantages, and disadvantages. Moreover, we enlist future research directions to secure CC models.

scholarly journals Analysis of Mobile Edge Computing for Vehicular Networks

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1303 ◽  
Author(s):  
Zachary Lamb ◽  
Dharma Agrawal
Keyword(s):  
Cellular Networks ◽  
Data Storage ◽  
Intelligent Transportation Systems ◽  
Vehicular Networks ◽  
Vehicular Ad Hoc Networks ◽  
Transportation Systems ◽  
Edge Computing ◽  
Test Bed ◽  
And Storage ◽  
Cloud Resources

Vehicular ad-hoc Networks (VANETs) are an integral part of intelligent transportation systems (ITS) that facilitate communications between vehicles and the internet. More recently, VANET communications research has strayed from the antiquated DSRC standard and favored more modern cellular technologies, such as fifth generation (5G). The ability of cellular networks to serve highly mobile devices combined with the drastically increased capacity of 5G, would enable VANETs to accommodate large numbers of vehicles and support range of applications. The addition of thousands of new connected devices not only stresses the cellular networks, but also the computational and storage requirements supporting the applications and software of these devices. Autonomous vehicles, with numerous on-board sensors, are expected to generate large amounts of data that must be transmitted and processed. Realistically, on-board computing and storage resources of the vehicle cannot be expected to handle all data that will be generated over the vehicles lifetime. Cloud computing will be an essential technology in VANETs and will support the majority of computation and long-term data storage. However, the networking overhead and latency associated with remote cloud resources could prove detrimental to overall network performance. Edge computing seeks to reduce the overhead by placing computational resources nearer to the end users of the network. The geographical diversity and varied hardware configurations of resource in a edge-enabled network would require careful management to ensure efficient resource utilization. In this paper, we introduce an architecture which evaluates available resources in real-time and makes allocations to the most logical and feasible resource. We evaluate our approach mathematically with the use of a multi-criteria decision analysis algorithm and validate our results with experiments using a test-bed of cloud resources. Results demonstrate that an algorithmic ranking of physical resources matches very closely with experimental results and provides a means of delegating tasks to the best available resource.

scholarly journals A Novel Blockchain Framework for Industrial IoT Edge Computing

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2061 ◽  
Author(s):  
Xuesong Xu ◽  
Zhi Zeng ◽  
Shengjie Yang ◽  
Hongyan Shao
Keyword(s):  
Data Storage ◽  
Resource Constraints ◽  
Rapid Development ◽  
Edge Computing ◽  
Consensus Algorithm ◽  
Resource Constrained ◽  
Blockchain Technology ◽  
Industrial Internet ◽  
Industrial Iot ◽  
And Performance

With the rapid development of industrial internet of thing (IIoT), the distributed topology of IIoT and resource constraints of edge computing conduct new challenges to traditional data storage, transmission, and security protection. A distributed trust and allocated ledger of blockchain technology are suitable for the distributed IIoT, which also becomes an effective method for edge computing applications. This paper proposes a resource constrained Layered Lightweight Blockchain Framework (LLBF) and implementation mechanism. The framework consists of a resource constrained layer (RCL) and a resource extended layer (REL) blockchain used in IIoT. We redesign the block structure and size to suit to IIoT edge computing devices. A lightweight consensus algorithm and a dynamic trust right algorithm is developed to improve the throughput of blockchain and reduce the number of transactions validated in new blocks respectively. Through a high throughput management to guarantee the transaction load balance of blockchain. Finally, we conducted kinds of blockchain simulation and performance experiments, the outcome indicated that the method have a good performance in IIoT edge application.

scholarly journals Decentralized Trusted Data Sharing Management on Internet of Vehicle Edge Computing (IoVEC) Networks Using Consortium Blockchain

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2410
Author(s):  
Muhammad Firdaus ◽  
Sandi Rahmadika ◽  
Kyung-Hyune Rhee
Keyword(s):  
Data Sharing ◽  
Data Storage ◽  
Traffic Safety ◽  
Network Performance ◽  
Intelligent Transportation System ◽  
Edge Computing ◽  
The Internet ◽  
Smart Vehicles ◽  
The Incentive Mechanism ◽  
Trusted Data

The emergence of the Internet of Vehicles (IoV) aims to facilitate the next generation of intelligent transportation system (ITS) applications by combining smart vehicles and the internet to improve traffic safety and efficiency. On the other hand, mobile edge computing (MEC) technology provides enormous storage resources with powerful computing on the edge networks. Hence, the idea of IoV edge computing (IoVEC) networks has grown to be an assuring paradigm with various opportunities to advance massive data storage, data sharing, and computing processing close to vehicles. However, the participant’s vehicle may be unwilling to share their data since the data-sharing system still relies on a centralized server approach with the potential risk of data leakage and privacy security. In addition, vehicles have difficulty evaluating the credibility of the messages they received because of untrusted environments. To address these challenges, we propose consortium blockchain and smart contracts to accomplish a decentralized trusted data sharing management system in IoVEC. This system allows vehicles to validate the credibility of messages from their neighboring by generating a reputation rating. Moreover, the incentive mechanism is utilized to trigger the vehicles to store and share their data honestly; thus, they will obtain certain rewards from the system. Simulation results substantially display an efficient network performance along with forming an appropriate incentive model to reach a decentralized trusted data sharing management of IoVEC networks.

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