scholarly journals Fog Computıng – A Rasperry Pı Decentralızed Network

2020 ◽  
Vol 02 (01) ◽  
pp. 27-42
Author(s):  
Dr. Bhalaji N. ◽  
Shanmuga Skandh Vinayak E
Keyword(s):  
Cloud Computing ◽  
Parallel Processing ◽  
Fog Computing ◽  
Optimal Solution ◽  
Raspberry Pi ◽  
Blockchain Technology ◽  
Remote Computation ◽  
Distributed Files

Ever since the concept of parallel processing and remote computation became feasible, Cloud computing is at its highest peak in its popularity. Although cloud computing is effective and feasible in its usage, using the cloud for frequent operations may not be the be the most optimal solution. Hence the concept of FOG proves to be more optimal and efficient. In this paper, we propose a solution by improving the FOG computing concept of decentralization by implementing a secure distributed files system utilizing the IPFS and the Ethereum Blockchain technology. Our proposed system has proved to be efficient by successfully distributing the data in a Raspberry Pi network. The outcome of this work will assist FOG architects in implementing this system in their infrastructure and also prove to be effective for IoT developers in implementing a Raspberry Pi decentralized network while providing more security to the data.

scholarly journals Fog Computing in the context of Smart Home, voice assistant and the future of IoT

2020 ◽  
Vol 21 (1) ◽  
pp. 6-12
Author(s):  
Javier Pinzón Castellanos ◽  
Miguel Antonio Cadena Carter
Keyword(s):  
Cloud Computing ◽  
Distributed Computing ◽  
Data Transmission ◽  
Smart Home ◽  
Fog Computing ◽  
Relevant Information ◽  
Raspberry Pi ◽  
Thermal Sensors ◽  
Computing Architecture ◽  
The Future

Fog Computing is the distributed computing layer that lies between the user and the cloud. A successful fog architecture reduces delay or latency and increases efficiency. This paper describes the development and implementation of a distributed computing architecture applied to an automation environment that uses Fog Computing as an intermediary with the cloud computing layer. This study used a Raspberry Pi V3 board connected to end control elements such as servomotors and relays, indicators and thermal sensors. All is controlled by an automation framework that receives orders from Siri and executes them through predetermined instructions. The cloud connection benefits from a reduced amount of data transmission, because it only receives relevant information for analysis.

scholarly journals A Novel Fog-IoT based Framework for Improving Performance of Cloud Computing

2019 ◽  
Vol 8 (12S) ◽  
pp. 889-895
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Secure Communication ◽  
Fog Computing ◽  
Cloud Service ◽  
Raspberry Pi ◽  
New Paradigm ◽  
Privacy And Security ◽  
Security Issues ◽  
Iot Devices

Internet-of-Things (IoT) has been considered as a fundamental part of our day by day existence with billions of IoT devices gathering information remotely and can interoperate within the current Internet framework. Fog computing is nothing but cloud computing to the extreme of network security. It provides computation and storage services via CSP (Cloud Service Provider) to end devices in the Internet of Things (IoT). Fog computing allows the data storing and processing any nearby network devices or nearby cloud endpoint continuum. Using fog computing, the designer can reduce the computation architecture of the IoT devices. Unfortunitily, this new paradigm IoT-Fog faces numerous new privacy and security issues, like authentication and authorization, secure communication, information confidentiality. Despite the fact that the customary cloud-based platform can even utilize heavyweight cryptosystem to upgrade security, it can't be performed on fog devices drectly due to reseource constraints. Additionally, a huge number of smart fog devices are fiercely disseminated and situated in various zones, which expands the danger of being undermined by some pernicious gatherings. Trait Based Encryption (ABE) is an open key encryption conspire that enables clients to scramble and unscramble messages dependent on client qualities, which ensures information classification and hearty information get to control. Be that as it may, its computational expense for encryption and unscrambling stage is straightforwardly corresponding to the multifaceted nature of the arrangements utilized. The points is to assess the planning, CPU burden, and memory burden, and system estimations all through each phase of the cloud-to-things continuum amid an analysis for deciding highlights from a finger tapping exercise for Parkinson's Disease patients. It will be appeared there are confinements to the proposed testbeds when endeavoring to deal with upwards of 35 customers at the same time. These discoveries lead us to a proper conveyance of handling the leaves the Intel NUC as the most suitable fog gadget. While the Intel Edison and Raspberry Pi locate a superior balance at in the edge layer, crossing over correspondence conventions and keeping up a self-mending network topology for "thing" devices in the individual territory organize.

scholarly journals Implementing and Evaluating Health as a Service in Fog and Cloud Computing using Raspberry Pi

2020 ◽  
Vol 13 (6) ◽  
pp. 142-155
Author(s):  
Pratik Kanani ◽  
◽  
Mamta Padole ◽  
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Cloud Computing ◽  
Health Care System ◽  
Fog Computing ◽  
Raspberry Pi ◽  
Transmission Delays ◽  
Network Delays ◽  
Qos Parameters ◽  
Care System

Internet of Things (IoT) generates a myriad amount of data, which is sent over the Cloud computing infrastructure for analytics and Business Intelligence. This application scenario suffers network delays, transmission delays and delays in decision making. Due to these drawbacks, the Cloud-based IoT infrastructure is not suitable for time-critical health care applications. To overcome this problem, a smart way is introduced called “Fog Computing” - a LAN based processing approach which has multiple advantages. When IoT, Fog and Cloud Computing are combined, the resultant system’s performance is far better. Hence, the combination results in a very efficient Health Care system. Fog and Cloud Computing have their dimensions that not only support each other but also explore many new application domains. In this paper, the real-time ElectroCardioGram (ECG) based Health Care system is implemented in Cloud and Fog Computing. Different Quality of Service (QoS) parameters like memory consumption, transmission delays, computation delays, network delays, Carbon dioxide emission, data transferred and response time are measured, analyzed and improved to make the system more efficient. Based on the Fog computing characteristics and capabilities, the Raspberry Pi 3 B+ model is configured as a Health Care serving gateway by using different installation and configuration steps. Initially, the proposed system is tested for one patients ECG data analysis over cloud and Fog. In every set up all QoS parameters are measured and later the system is subjected to multiple ECG streams for varying numbers of patients to find the limitations of the Raspberry Pi node as a Fog Computing node. The obtained results show that for more number of ECG streams the Fog node is not able maintain QoS in decision making time. Every QoS parameter is explored in detail for decision-making time. In the end, the Fog computing based proposed system is concluded for its pros and cons and future aspects of the Fog node are discussed to make better systems.

Fog Computing Architecture for Scalable Processing of Geospatial Big Data

2020 ◽  
Vol 11 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Rabindra K. Barik ◽  
Rojalina Priyadarshini ◽  
Rakesh K. Lenka ◽  
Harishchandra Dubey ◽  
Kunal Mankodiya
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analysis ◽  
Case Studies ◽  
Information Management ◽  
Fog Computing ◽  
Geospatial Data ◽  
Raspberry Pi ◽  
Ganga Basin ◽  
Tourism Information

Geospatial data analysis using cloud computing platform is one of the promising areas for analysing, retrieving, and processing volumetric data. Fog computing paradigm assists cloud platform where fog devices try to increase the throughput and reduce latency at the edge of the client. In this research paper, the authors discuss two case studies on geospatial data analysis using Fog-assisted cloud computing namely, (1)Ganga River Basin Management System; and (2)Tourism Information Management of India. Both case studies evaluate proposed GeoFog architecture for efficient analysis and management of geospatial big data employing fog computing. The authors developed a prototype of GeoFog architecture using Intel Edison and Raspberry Pi devices. The authors implemented some of the open source compression methods for reducing the data transmission overload in the cloud. Proposed architecture performs data compression and overlay analysis of data. The authors further discussed the improvement in scalability and time analysis using proposed GeoFog architecture and Geospark tool. Discussed results show the merit of fog computing that holds an enormous promise for enhanced analysis of geospatial big data in river Ganga basin and tourism information management scenario.

scholarly journals The Purview of Blockchain Appositeness in Computing Paradigms: A Survey

2021 ◽  
Vol 26 (1) ◽  
pp. 33-46
Author(s):  
Battula V. Satish Babu ◽  
Kare Suresh Babu
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Quantum Computing ◽  
Fog Computing ◽  
Research Articles ◽  
Privacy And Security ◽  
Research Opportunities ◽  
Blockchain Technology ◽  
Integration Problems ◽  
Big Data Computing

Blockchain technology is getting more and more pertinent to solve most of the digital problems that we face today. Blockchain is notable for its prominent features like immutability, decentralization, consensus, privacy, and security. However, blockchain is still suffering from different barriers like quantum attacks, scalability problems, integration problems, incompetence to face bigdata, storage problems, and so on. The main aim of this study was to find out the scope, various problems raised, and the applicability of blockchain technology when integrated with different computing paradigms like cloud computing, edge computing, fog computing, osmotic computing, big data computing, and quantum computing. To conduct this study, we have surveyed different research articles in the combination of blockchain technology and computing paradigms. Based on this survey, we have mentioned the contemporary research works, challenges, and a list of possible research opportunities and solutions.

Proposed Back Propagation Deep Neural Network for Intrusion Detection in Internet of Things Fog Computing

2021 ◽  
Vol 9 (4) ◽  
pp. 464-469
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Fog Computing ◽  
Denial Of Service ◽  
Back Propagation ◽  
Security And Privacy ◽  
Brute Force ◽  
Iot Platform ◽  
Proposed Model

Internet of things (IoT) is an emerging concept which aims to connect billions of devices with each other anytime regardless of their location. Sadly, these IoT devices do not have enough computing resources to process huge amount of data. Therefore, Cloud computing is relied on to provide these resources. However, cloud computing based architecture fails in applications that demand very low and predictable latency, therefore the need for fog computing which is a new paradigm that is regarded as an extension of cloud computing to provide services between end users and the cloud user. Unfortunately, Fog-IoT is confronted with various security and privacy risks and prone to several cyberattacks which is a serious challenge. The purpose of this work is to present security and privacy threats towards Fog-IoT platform and discuss the security and privacy requirements in fog computing. We then proceed to propose an Intrusion Detection System (IDS) model using Standard Deep Neural Network's Back Propagation algorithm (BPDNN) to mitigate intrusions that attack Fog-IoT platform. The experimental Dataset for the proposed model is obtained from the Canadian Institute for Cybersecurity 2017 Dataset. Each instance of the attack in the dataset is separated into separate files, which are DoS (Denial of Service), DDoS (Distributed Denial of Service), Web Attack, Brute Force FTP, Brute Force SSH, Heartbleed, Infiltration and Botnet (Bot Network) Attack. The proposed model is trained using a 3-layer BP-DNN

Survey on DDoS Attacks and Defense Mechanisms in Cloud and Fog Computing

2018 ◽  
Vol 10 (3) ◽  
pp. 61-83 ◽  
Author(s):  
Deepali Chaudhary ◽  
Kriti Bhushan ◽  
B.B. Gupta
Keyword(s):  
Cloud Computing ◽  
Defense Mechanisms ◽  
Low Cost ◽  
Fog Computing ◽  
Smart Devices ◽  
Cloud Environment ◽  
Security Issue ◽  
Process Data ◽  
Ddos Attack ◽  
Iot Devices

This article describes how cloud computing has emerged as a strong competitor against traditional IT platforms by offering low-cost and “pay-as-you-go” computing potential and on-demand provisioning of services. Governments, as well as organizations, have migrated their entire or most of the IT infrastructure to the cloud. With the emergence of IoT devices and big data, the amount of data forwarded to the cloud has increased to a huge extent. Therefore, the paradigm of cloud computing is no longer sufficient. Furthermore, with the growth of demand for IoT solutions in organizations, it has become essential to process data quickly, substantially and on-site. Hence, Fog computing is introduced to overcome these drawbacks of cloud computing by bringing intelligence to the edge of the network using smart devices. One major security issue related to the cloud is the DDoS attack. This article discusses in detail about the DDoS attack, cloud computing, fog computing, how DDoS affect cloud environment and how fog computing can be used in a cloud environment to solve a variety of problems.

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