scholarly journals BECA: A Blockchain-Based Edge Computing Architecture for Internet of Things Systems

IoT ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 610-632
Author(s):  
Oluwashina Joseph Ajayi ◽  
Joseph Rafferty ◽  
Jose Santos ◽  
Matias Garcia-Constantino ◽  
Zhan Cui
Keyword(s):  
Internet Of Things ◽  
Data Storage ◽  
Distributed Storage ◽  
Storage System ◽  
Data Retrieval ◽  
Edge Computing ◽  
Computing Architecture ◽  
Symbiotic Relationships ◽  
Data Ownership ◽  
Latency Response

The scale of Internet of Things (IoT) systems has expanded in recent times and, in tandem with this, IoT solutions have developed symbiotic relationships with technologies, such as edge Computing. IoT has leveraged edge computing capabilities to improve the capabilities of IoT solutions, such as facilitating quick data retrieval, low latency response, and advanced computation, among others. However, in contrast with the benefits offered by edge computing capabilities, there are several detractors, such as centralized data storage, data ownership, privacy, data auditability, and security, which concern the IoT community. This study leveraged blockchain’s inherent capabilities, including distributed storage system, non-repudiation, privacy, security, and immutability, to provide a novel, advanced edge computing architecture for IoT systems. Specifically, this blockchain-based edge computing architecture addressed centralized data storage, data auditability, privacy, data ownership, and security. Following implementation, the performance of this solution was evaluated to quantify performance in terms of response time and resource utilization. The results show the viability of the proposed and implemented architecture, characterized by improved privacy, device data ownership, security, and data auditability while implementing decentralized storage.

Monitoring of a Grid Storage Virtualization Service

2013 ◽  
Vol 5 (1) ◽  
pp. 53-69
Author(s):  
Jacques Jorda ◽  
Aurélien Ortiz ◽  
Abdelaziz M’zoughi ◽  
Salam Traboulsi
Keyword(s):  
Monitoring System ◽  
Data Storage ◽  
Large Scale ◽  
Distributed Storage ◽  
Storage System ◽  
Data Access ◽  
Data Placement ◽  
Workload Prediction ◽  
Storage Virtualization

Grid computing is commonly used for large scale application requiring huge computation capabilities. In such distributed architectures, the data storage on the distributed storage resources must be handled by a dedicated storage system to ensure the required quality of service. In order to simplify the data placement on nodes and to increase the performance of applications, a storage virtualization layer can be used. This layer can be a single parallel filesystem (like GPFS) or a more complex middleware. The latter is preferred as it allows the data placement on the nodes to be tuned to increase both the reliability and the performance of data access. Thus, in such a middleware, a dedicated monitoring system must be used to ensure optimal performance. In this paper, the authors briefly introduce the Visage middleware – a middleware for storage virtualization. They present the most broadly used grid monitoring systems, and explain why they are not adequate for virtualized storage monitoring. The authors then present the architecture of their monitoring system dedicated to storage virtualization. We introduce the workload prediction model used to define the best node for data placement, and show on a simple experiment its accuracy.

Optimal Elliptic Curve Cryptography-Based Effective Approach for Secure Data Storage in Clouds

2020 ◽  
Vol 11 (4) ◽  
pp. 65-81
Author(s):  
Anju Malik ◽  
Mayank Aggarwal ◽  
Bharti Sharma ◽  
Akansha Singh ◽  
Krishna Kant Singh
Keyword(s):  
Elliptic Curve ◽  
Data Storage ◽  
Elliptic Curve Cryptography ◽  
Search Algorithm ◽  
Distributed Storage ◽  
Data Retrieval ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Sensitive Data ◽  
Cloud Server

With the rapid development of cloud advancement, a data security challenge has emerged. In this paper, a technique based on elliptical cryptography and cuckoo search algorithm is proposed. With this technique, data owners securely store their data files in the cloud server. Initially the user sends a file storage request to store a file in a cloud server provider (CSP). The input file is checked whether it is sensitive or non-sensitive by the user. If the file is sensitive, then it would be split and stored in different virtual machines (VMs), and if the file is non-sensitive, then it would be assigned in a single VM. This approach was used for the first time as per the survey. To add further security, the sensitive data retrieval needs an encryption process that is supported by the proposed algorithm. If the data owner stores the sensitive data to cloud server, the data owner's document is encrypted by the double encryption technique. Here RSA and optimal elliptic curve cryptography (OECC) algorithm is used to encrypt the document with high security. The authors have used cuckoo search algorithm to identify the optimal key in ECC. This paper has proposed a novel cryptography approach for delivering mass distributed storage by which user's original data cannot be directly reached by cloud operators. Hence, this research has proved that the proposed work will give better securable data storage solving the security issues.

scholarly journals Distributed Storage Strategy and Visual Analysis for Economic Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xiangli Chang ◽  
Hailang Cui
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Visual Analysis ◽  
Distributed Storage ◽  
Storage System ◽  
Service Level Agreement ◽  
Wiener Filter ◽  
Service Level ◽  
Prototype System ◽  
Data Types

With the increasing popularity of a large number of Internet-based services and a large number of services hosted on cloud platforms, a more powerful back-end storage system is needed to support these services. At present, it is very difficult or impossible to implement a distributed storage to meet all the above assumptions. Therefore, the focus of research is to limit different characteristics to design different distributed storage solutions to meet different usage scenarios. Economic big data should have the basic requirements of high storage efficiency and fast retrieval speed. The large number of small files and the diversity of file types make the storage and retrieval of economic big data face severe challenges. This paper is oriented to the application requirements of cross-modal analysis of economic big data. According to the source and characteristics of economic big data, the data types are analyzed and the database storage architecture and data storage structure of economic big data are designed. Taking into account the spatial, temporal, and semantic characteristics of economic big data, this paper proposes a unified coding method based on the spatiotemporal data multilevel division strategy combined with Geohash and Hilbert and spatiotemporal semantic constraints. A prototype system was constructed based on Mongo DB, and the performance of the multilevel partition algorithm proposed in this paper was verified by the prototype system based on the realization of data storage management functions. The Wiener distributed memory based on the principle of Wiener filter is used to store the workload of each workload distributed storage window in a distributed manner. For distributed storage workloads, this article adopts specific types of workloads. According to its periodicity, the workload is divided into distributed storage windows of specific duration. At the beginning of each distributed storage window, distributed storage is distributed to the next distributed storage window. Experiments and tests have verified the distributed storage strategy proposed in this article, which proves that the Wiener distributed storage solution can save platform resources and configuration costs while ensuring Service Level Agreement (SLA).

scholarly journals Research on Distributed Data Sharing System based on Internet of Things and Blockchain

2021 ◽  
Vol 9 (3) ◽  
pp. 239-254
Author(s):  
Enchang Sun ◽  
Kang Meng ◽  
Ruizhe Yang ◽  
Yanhua Zhang ◽  
Meng Li
Keyword(s):  
Internet Of Things ◽  
Data Sharing ◽  
Data Storage ◽  
Network Architecture ◽  
Data Privacy ◽  
Distributed Storage ◽  
Global Network ◽  
Local Network ◽  
Network Data ◽  
Distributed Data

Abstract Aiming at the problems of the traditional centralized data sharing platform, such as poor data privacy protection ability, insufficient scalability of the system and poor interaction ability, this paper proposes a distributed data sharing system architecture based on the Internet of Things and blockchain technology. In this system, the distributed consensus mechanism of blockchain and the distributed storage technology are employed to manage the access and storage of Internet of Things data in a secure manner. Up to the physical topology of the network, a hierarchical blockchain network architecture is proposed for local network data storage and global network data sharing, which reduces networking complexity and improves the scalability of the system. In addition, smart contract and distributed machine learning are adopted to design automatic processing functions for different types of data (public or private) and supervise the data sharing process, improving both the security and interactive ability of the system.

scholarly journals Blockchain-Based Security Mechanism for the Medical Data at Fog Computing Architecture of Internet of Things

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2110
Author(s):  
Desire Ngabo ◽  
Dong Wang ◽  
Celestine Iwendi ◽  
Joseph Henry Anajemba ◽  
Lukman Adewale Ajao ◽  
...  
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Data Storage ◽  
Fog Computing ◽  
Medical Data ◽  
Cyber Attacks ◽  
Security Mechanism ◽  
Computing Architecture ◽  
Blockchain Technology ◽  
And Storage

The recent developments in fog computing architecture and cloud of things (CoT) technology includes data mining management and artificial intelligence operations. However, one of the major challenges of this model is vulnerability to security threats and cyber-attacks against the fog computing layers. In such a scenario, each of the layers are susceptible to different intimidations, including the sensed data (edge layer), computing and processing of data (fog (layer), and storage and management for public users (cloud). The conventional data storage and security mechanisms that are currently in use appear to not be suitable for such a huge amount of generated data in the fog computing architecture. Thus, the major focus of this research is to provide security countermeasures against medical data mining threats, which are generated from the sensing layer (a human wearable device) and storage of data in the cloud database of internet of things (IoT). Therefore, we propose a public-permissioned blockchain security mechanism using elliptic curve crypto (ECC) digital signature that that supports a distributed ledger database (server) to provide an immutable security solution, transaction transparency and prevent the patient records tampering at the IoTs fog layer. The blockchain technology approach also helps to mitigate these issues of latency, centralization, and scalability in the fog model.

scholarly journals Desain Dan Implementasi Data Warehouse Pada Perpustakaan Daerah Provinsi Sumatera Selatan

2021 ◽  
Vol 2 (1) ◽  
pp. 33-50
Author(s):  
Nia Adila ◽  
Andri Andri
Keyword(s):  
Data Warehouse ◽  
Data Storage ◽  
Storage System ◽  
Data Retrieval ◽  
Design Stage ◽  
Process Selection ◽  
Step Method ◽  
Analysis Process ◽  
Data Book ◽  
Data Member

This research will focus on processing visitor data, member data, book borrowing data and book return data at the Regional Library of South Sumatra Province. The data is stored in the form of an excel file with a large amount of data, causing problems in the storage system such as data accumulation, data loss, no data analysis and delays in the reporting process. The process of data storage and data retrieval will be well integrated by building a data Warehouse at the Regional Library of South Sumatra Province. Data Warehouse is a system that contains several years of history and facilitates decision making. At the data Warehouse design stage using the Nine-Step method (Kimball, 2002), in this method there are nine steps in designing a data Warehouse, namely Process Selection, Grain Selection, Identification of dimensional adjustments, Fact Selection, Storage of initial calculations in the fact table, Reviewing the dimension table, selecting the database duration, tracking dimension changes, and prioritizing, querying the model and selecting the physical design. And the design and data processing process will use the Pentaho kettle and public Tableau applications, with the design and implementation of the data Warehouse, it is expected to help facilitate the reporting and analysis process for the Regional Library of South Sumatra Province.

scholarly journals PRICHAIN: A Partially Decentralized Implementation of UbiPri Middleware Using Blockchain

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4483 ◽  
Author(s):  
Iago Sestrem Ochôa ◽  
Luis Augusto Silva ◽  
Gabriel de Mello ◽  
Bruno Alves da Silva ◽  
Juan Francisco de Paz ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Data Storage ◽  
Personal Information ◽  
Distributed Storage ◽  
The Internet ◽  
Storage Security ◽  
User Data ◽  
And Performance ◽  
The Internet Of Things ◽  
Different Levels

With the popularization of the Internet-of-Things, various applications have emerged to make life easier. These applications generate a large amount of user data. Analyzing the data obtained from these applications, one can infer personal information about each user. Considering this, it is clear that ensuring privacy in this type of application is essential. To guarantee privacy various solutions exist, one of them is UbiPri middleware. This paper presents a decentralized implementation of UbiPri middleware using the Ethereum blockchain. Smart contracts were used in conjunction with a communication gateway and a distributed storage service to ensure users privacy. The results obtained show that the implementation of this work ensures privacy at different levels, data storage security, and performance regarding scalability in the Internet of Things environments.

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