scholarly journals Analysis, Deployment and Integration of Platforms for Fog Computing

2020 ◽  
Vol 20 (2) ◽  
pp. e12
Author(s):  
Joaquín De Antueno ◽  
Santiago Medina ◽  
Laura De Giusti ◽  
Armando De Giusti
Keyword(s):  
Data Management ◽  
Sensor Network ◽  
Experimental Work ◽  
Response Times ◽  
Fog Computing ◽  
Data Capture ◽  
Local Data ◽  
Iot Applications ◽  
Flow Of Information ◽  
Large Flow

In IoT applications, data capture in a sensor network can generate a large flow of information between the nodes and the cloud, affecting response times and device complexity but, above all, increasing costs. Fog computing refers to the use of pre-processing tools to improve local data management and communication with the cloud. This work presents an analysis of the features that platforms implementing fog computing solutions should have. Additionally, an experimental work integrating two specific platforms used for controlling devices in a sensor network, processing the generated data, and communicating with the cloud is presented.

scholarly journals Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

2021 ◽  
Author(s):  
Karan Bajaj ◽  
Bhisham Sharma ◽  
Raman Singh
Keyword(s):  
High Speed ◽  
Smart Cities ◽  
Fog Computing ◽  
Edge Computing ◽  
Computation Offloading ◽  
Performance Parameters ◽  
Iot Applications ◽  
Time Sensitivity ◽  
Implementation Analysis ◽  
Increasing Demand

AbstractThe Internet of Things (IoT) applications and services are increasingly becoming a part of daily life; from smart homes to smart cities, industry, agriculture, it is penetrating practically in every domain. Data collected over the IoT applications, mostly through the sensors connected over the devices, and with the increasing demand, it is not possible to process all the data on the devices itself. The data collected by the device sensors are in vast amount and require high-speed computation and processing, which demand advanced resources. Various applications and services that are crucial require meeting multiple performance parameters like time-sensitivity and energy efficiency, computation offloading framework comes into play to meet these performance parameters and extreme computation requirements. Computation or data offloading tasks to nearby devices or the fog or cloud structure can aid in achieving the resource requirements of IoT applications. In this paper, the role of context or situation to perform the offloading is studied and drawn to a conclusion, that to meet the performance requirements of IoT enabled services, context-based offloading can play a crucial role. Some of the existing frameworks EMCO, MobiCOP-IoT, Autonomic Management Framework, CSOS, Fog Computing Framework, based on their novelty and optimum performance are taken for implementation analysis and compared with the MAUI, AnyRun Computing (ARC), AutoScaler, Edge computing and Context-Sensitive Model for Offloading System (CoSMOS) frameworks. Based on the study of drawn results and limitations of the existing frameworks, future directions under offloading scenarios are discussed.

A Cognitive Research Tendency in Data Management of Sensor Network

2021 ◽  
pp. 26-36
Author(s):  
Subhra Prosun Paul ◽  
◽  
Dr. Shruti Aggarwal ◽  
Keyword(s):  
Sensor Networks ◽  
Data Management ◽  
Sensor Network ◽  
High Speed ◽  
Web Of Science ◽  
Data Access ◽  
Global Perspective ◽  
Data Movement ◽  
Network Database ◽  
Cognitive Research

In today’s World sensor networks offer various opportunities for data management applications because of their low cost, reliability, scalability, high-speed data processing, and other versatile advantageous purposes. It is a great challenge to organize data effectively and to retrieve the appropriate data from the large volume of various data sets in ad-hoc network databases, mobile databases, etc. The sensor network is necessary for routing of data, performance analysis of data management activities, and data incorporation for the right application. Data management involves intranet and extranet query handling, data access mechanism, modeling of data, different data movement algorithm, data warehousing, and data mining of network database. Additionally, connectivity, design, and lifetime are important issues for sensor networks to perform all data management activities smoothly. In this paper, we are trying to give a cognitive research tendency of Sensor network data management in the last two decades considering all the challenges and issues of both sensor network database and data management functions using Scopus and Web of Science database. To analyze data, different assessments are done considering various parameters like the author, time, publication and citation number, place, source, document separately for Web of Science and Scopus database in global perspective. It is noticed that there is a significant growth of research in data management for sensor networks because of the popularity of this topic.

Mobile Sensor Network Data Management

2017 ◽  
pp. 1-6
Author(s):  
Demetrios Zeinalipour-Yazti ◽  
Panos K. Chrysanthis
Keyword(s):  
Data Management ◽  
Sensor Network ◽  
Network Data ◽  
Mobile Sensor ◽  
Mobile Sensor Network

scholarly journals OPTIMIZING TELEMEDICINE FRAMEWORK USING FOG COMPUTING FOR SMART HEALTHCARE SYSTEMS

2021 ◽  
Author(s):  
Michael Enbibel
Keyword(s):  
Data Management ◽  
Healthcare System ◽  
Data Flow ◽  
System Organization ◽  
Fog Computing ◽  
Healthcare Systems ◽  
Distributed Data ◽  
Smart Healthcare ◽  
Network Latency

This research is done for optimizing telemedicine framework by using fogging or fog computing for smart healthcare systems. Fog computing is used to solve the issues that arise on telemedicine framework of smart healthcare system like Infrastructural, Implementation, Acceptance, Data Management, Security, Bottleneck system organization, and Network latency Issues. we mainly used Distributed Data Flow (DDF) method using fog computing in order to fully solve the listed issues.

Sign in / Sign up

Export Citation Format

Share Document