Comparison of Stock Price Prediction Models using Pre-trained Neural Networks
Volume-3 | Issue-2
Efficient Two Stage Identification for Face mask detection using Multiclass Deep Learning Approach
Volume-3 | Issue-2
Design an Adaptive Hybrid Approach for Genetic Algorithm to Detect Effective Malware Detection in Android Division
Volume-3 | Issue-2
Blockchain Framework for Communication between Vehicle through IoT Devices and Sensors
Volume-3 | Issue-2
Gas Leakage Detection in Pipeline by SVM classifier with Automatic Eddy Current based Defect Recognition Method
Volume-3 | Issue-3
A Review on Data Securing Techniques using Internet of Medical Things
Volume-3 | Issue-3
Machine Learning Algorithms Performance Analysis for VLSI IC Design
Volume-3 | Issue-2
Maximizing the Prediction Accuracy in Tweet Sentiment Extraction using Tensor Flow based Deep Neural Networks
Volume-3 | Issue-2
Assimilation of IoT sensors for Data Visualization in a Smart Campus Environment
Volume-3 | Issue-4
DDOS ATTACK DETECTION IN TELECOMMUNICATION NETWORK USING MACHINE LEARNING
Volume-1 | Issue-1
Gas Leakage Detection in Pipeline by SVM classifier with Automatic Eddy Current based Defect Recognition Method
Volume-3 | Issue-3
Design an Adaptive Hybrid Approach for Genetic Algorithm to Detect Effective Malware Detection in Android Division
Volume-3 | Issue-2
Comparison of Stock Price Prediction Models using Pre-trained Neural Networks
Volume-3 | Issue-2
Construction of a Framework for Selecting an Effective Learning Procedure in the School-Level Sector of Online Teaching Informatics
Volume-3 | Issue-4
Machine Learning Algorithms Performance Analysis for VLSI IC Design
Volume-3 | Issue-2
Efficient Two Stage Identification for Face mask detection using Multiclass Deep Learning Approach
Volume-3 | Issue-2
Characterizing WDT subsystem of a Wi-Fi controller in an Automobile based on MIPS32 CPU platform across PVT
Volume-2 | Issue-4
Assimilation of IoT sensors for Data Visualization in a Smart Campus Environment
Volume-3 | Issue-4
Design of Data Mining Techniques for Online Blood Bank Management by CNN Model
Volume-3 | Issue-3
Ethereum and IOTA based Battery Management System with Internet of Vehicles
Volume-3 | Issue-3
Volume - 3 | Issue - 1 | march 2021
Published
23 April, 2021
As the number of context-aware and unique data extracting and computing has grown leaps and bounds, paving way to their application in many platforms, Internet of Things (IoT) has gained a lot of importance in recent years. Using IoT, it is possible to connect any number of objects and in any context, giving rise to a diverse range of services. Hence power management, data storage, service discovery, service management, service creation and resource management need a more sophisticated mechanism and a better infrastructure. However, a single IoT that is power-constrained will not be able to tackle all the data that is generated. Hence cloud computing plays a crucial role by integrating with the IoT in such a way that the future internet and envisioned IoT is achievable. There are a number of challenges involved of which data trimming is common challenge. Due to unnecessary communication, the data this sent to the cloud may be interrupted. Hence to prevent this, data is initially preprocessed following which it is trimmed and then transferred to the cloud. This aspect of data processing can be carried out through Fog Computing or Smart Network that serves as a smart gateway. In this paper, we have introduced a novel concept Fog computing as the smart gateway. The results have been analysed and tested with respect to bulk-data synchronizstion Delay, bulk-data upload delay, jitter, synchronization delay and upload delay.
KeywordsFog computing smart gateway cloud computing IoT smart gateway
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