scholarly journals Fog-Cloud Computing Traffic Model Affecting the Occurrence of Latency on the Ubiquitous Sensor Network

2021 ◽  
Author(s):  
Chonnikan Sangmek ◽  
Nathaphon Boonnam
Keyword(s):  
Cloud Computing ◽  
Data Transmission ◽  
Fog Computing ◽  
Transmission Efficiency ◽  
Traffic Model ◽  
Packet Size ◽  
Internet Connection ◽  
Ubiquitous Sensor Networks ◽  
Different Characteristics ◽  
Main Factor

Abstract The fog-cloud computing traffic model overviews working elements in forming fog-cloud computing with three main layers: Ubiquitous Sensor Networks, Fog Computing, and Cloud Computing. We present a possible method of data transmission that focuses on either measuring or manipulating or both in the system divided into 7 USNs and using latency measurement to demonstrate transmission efficiency. This paper considers the latency test into four prominent cases: internet connection, traffic model, number of devices, and packet by equipment used for testing consisting of microcontroller board, sensor, actuator, and uses fog node two types: pocket Wi-Fi and router. In the latency test, we found that the factor causing the higher latency in the system was the packet size. The main factor consists of the different characteristics of working, fog nodes, and the number of connected devices. Therefore, the packet has correlated directly with the latency depending on the size of the packet increases. The resulting latency is the main factor affecting the work of the system.

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 Research on Internet of Things security architecture based on fog computing

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771988816 ◽  
Author(s):  
Trung Dong Mai
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Data Transmission ◽  
Fog Computing ◽  
Security Architecture ◽  
Time Data ◽  
Local Data ◽  
Computing Device ◽  
Network Bandwidth ◽  
Processing Power

The traditional data processing of the Internet of Things is concentrated in cloud computing, and its huge number of devices and massive real-time data transmission are extremely stressful on network bandwidth and cloud computing data centers. Fog computing is the infrastructure that can use processing power anywhere in the cloud. Virtual computing extends the power of cloud computing to the edge of the network, enabling any computing device to host and process software services, analyzing and storing data closer to where data are generated. The architecture of the fog computing brings enormous processing power. Since its processing power is often located near the required equipment, the distance of data transmission is reduced and the delay is reduced. This article explores how to use the fog computing layer between the cloud data center and the end node layer to store and process large amounts of local data in a timely manner, speeding decision making and enabling Internet of Things manufacturers and software developers to limit their ability to send data. They reduced cloud computing costs and built a reasonable security architecture.

scholarly journals Fog Networks: A Prospective Technology for IoT

2021 ◽  
Vol 10 (3) ◽  
pp. 2024-2028
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Fog Computing ◽  
Cloud Services ◽  
Heterogeneous Devices ◽  
Internet Connection ◽  
Almost All ◽  
Distinguishing Features

Cloud computing is currently the most sought-after solution for almost all of enterprise problems. Distinguishing features of the Cloud are ease of service and lesser hassle on the client end. These services come with a hefty price. Cloud services face issues of delay, slower connectivity and security. Fog Computing answers these downsides by providingnearer-to-ground and ever available Internet connection to nodes.Fog Computing relies on multiple, smaller clouds, nearer to ground.Internet of Things is the next logical leap for Internet. It envisions creating an environment wherein various heterogeneous devices can communicate with each other via internet. Enabling Internet of Things requires uninterrupted Internet connection and an interpreter.Both these features are intrinsically present in Fog Computing. Hence, this paper proposes Fog asthe expected ground for enabling Internet of Things

scholarly journals A New Edge Computing based Cloud System for IoT Applications

2019 ◽  
Vol 8 (2) ◽  
pp. 4289-4293
Keyword(s):  
Cloud Computing ◽  
Spectral Efficiency ◽  
Telecommunication Network ◽  
Fog Computing ◽  
Mobile Internet ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Novel Technology ◽  
Different Characteristics ◽  
The Internet Of Things

The mobile internet and the internet of things (IoT) has emerged out with various applications were centralized cloud computing has faced several challenges over the past years. Challenges include high latency and low Spectral Efficiency. Nevertheless, these challenges can be faced using a novel technology which is now emerging out as a major trending technology that supersedes centralized cloud computing with edge devices of networks. Well, this technology will reduce the latency and will enhance spectral efficiency and will also support massive machine types of communication. A detailed description of this trending technology deals with mobile edge computing, cloudlets and fog computing. In addition, the functioning process of each computing technology is also included. The different characteristics of mobile edge computing and fog computing have been focused. However the most significant part of how these technologies work under the discussion of telecommunication network is also briefly explained.

FSO-Based Analysis of LTE-A MAC Protocols to Achieve Improved QoS

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Prabhjot Kaur ◽  
Hardeep Singh Saini
Keyword(s):  
Data Transmission ◽  
Orthogonal Frequency Division Multiplexing ◽  
Optical Signal ◽  
Transmission Efficiency ◽  
Mac Protocols ◽  
Wireless Access Networks ◽  
Free Space Optical ◽  
Medium Access ◽  
Carrier Sense ◽  
Efficient Data

AbstractFiber wireless (Fi-Wi) communication network is the amalgamation of optical and wireless access networks, which provides better bandwidth for achieving efficient data transmission. Medium access control (MAC) protocols are used in the wireless network for controlling the data flow from the transmitter to the receiver end. The delay produced by these protocols tells about the system efficiency. This paper shows a Fi-Wi system in the long-term evaluation-advanced (LTE-A) environment, which incorporates the dependency of delay generated by the specific MAC protocols during the transmission process. This paper aimed to scrutinize the effects of Carrier Sense Multiplexing Access with Collision Detection (CSMA/CD), Carrier Sense Multiplexing Access with Collision Avoidance (CSMA/CA) and Slotted ALOHA on the performance of the Fi-Wi system. Free space optical (FSO) channel is incorporated to forward the data to user end. In such system, the optical signal is multiplexed using the Orthogonal Frequency Division Multiplexing (OFDM) technique and finally the data are fetched at the receiver end and different criterions such eye-height, Q-factor and bit error rate are evaluated. Simulation results are performed using MATLAB software. The comparative analysis is also performed in terms of data transmission efficiency, delay and throughput of MAC protocols. This shows the effective results of the proposed system according to the delay produced by MAC protocols.

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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