Containerized Application for IoT Devices: Comparison between balenaCloud and Amazon Web Services Approaches

2020 ◽  
Author(s):  
Robert Botez ◽  
Vlad Strautiu ◽  
Iustin-Alexandru Ivanciu ◽  
Virgil Dobrota
Keyword(s):  
Web Services ◽  
Amazon Web Services ◽  
Iot Devices

Extending IoTs Into the Cloud-Based Platform for Examining Amazon Web Services

2018 ◽  
pp. 216-227 ◽  
Author(s):  
Jagdeep Kaur ◽  
Meghna Sharma
Keyword(s):  
Web Services ◽  
Future Research ◽  
Rule Engine ◽  
Management Tools ◽  
The Public ◽  
Main Motive ◽  
Amazon Web Services ◽  
Future Research Directions ◽  
Iot Devices ◽  
Development And Management

The public cloud Amazon Web Service (AWS) provides a wide range of services like computation, networking, analytics, development and management tools, application services, mobile services, and management of Internet-of-Things (IoT) devices. The Amazon Web Services (AWS) IoT is an excellent IoT cloud platform and is exclusively responsible for connecting devices into various fields like healthcare, biology, municipal setup, smart homes, marketing, industrial, agriculture, education, automotive, etc. This chapter highlights many other initiatives promoted by AWS IoT. The main motive of this chapter is to present how AWS IoT works. The chapter starts with the design principles of AWS IoT services. Further, the authors present a detailed description of the AWS IoT components (e.g., Device SDK, Message Broker, Rule Engine, Security and Identity Service, Thing Registry, Thing Shadow, and Thing Shadow Service). The chapter concludes with a description of various challenges faced by AWS IoT and future research directions.

Extending IoTs Into the Cloud-Based Platform for Examining Amazon Web Services

Fog Computing ◽  
2018 ◽  
pp. 132-141
Author(s):  
Jagdeep Kaur ◽  
Meghna Sharma
Keyword(s):  
Web Services ◽  
Future Research ◽  
Rule Engine ◽  
Management Tools ◽  
The Public ◽  
Main Motive ◽  
Amazon Web Services ◽  
Future Research Directions ◽  
Iot Devices ◽  
Development And Management

The public cloud Amazon Web Service (AWS) provides a wide range of services like computation, networking, analytics, development and management tools, application services, mobile services, and management of Internet-of-Things (IoT) devices. The Amazon Web Services (AWS) IoT is an excellent IoT cloud platform and is exclusively responsible for connecting devices into various fields like healthcare, biology, municipal setup, smart homes, marketing, industrial, agriculture, education, automotive, etc. This chapter highlights many other initiatives promoted by AWS IoT. The main motive of this chapter is to present how AWS IoT works. The chapter starts with the design principles of AWS IoT services. Further, the authors present a detailed description of the AWS IoT components (e.g., Device SDK, Message Broker, Rule Engine, Security and Identity Service, Thing Registry, Thing Shadow, and Thing Shadow Service). The chapter concludes with a description of various challenges faced by AWS IoT and future research directions.

Bidding application in Amazon web services for the sales of agricultural products

2016 ◽  
Author(s):  
R Madhumathi ◽  
R RadhaKrishnan ◽  
S Suresh Kumar ◽  
K Abineshkumar ◽  
M Karthi ◽  
...  
Keyword(s):  
Web Services ◽  
Agricultural Products ◽  
Amazon Web Services

scholarly journals Metamodelo de gestión de conocimiento a partir de análisis so¿cial de lecciones aprendidas [Metamodel knowledge management from social analysis of lessons learned]

2015 ◽  
Author(s):  
José Fernando López Quintero ◽  
Juan Manuel Cueva Lovelle ◽  
Begoña Cristina Pelayo García-Bustelo ◽  
Carlos Enrique Montenegro Marín
Keyword(s):  
Knowledge Management ◽  
Social Network ◽  
Web Services ◽  
Web 2.0 ◽  
Semantic Analysis ◽  
Lessons Learned ◽  
Personal Knowledge ◽  
Redes Sociales ◽  
Functional Architecture ◽  
Amazon Web Services

Este artículo describe el desarrollo de una arquitectura funcional orientada a la Gestión de Conocimiento Personal (GCP), definido desde el concepto de las lecciones aprendidas que se registran en una red social de uso masivo. Esta arquitectura funcional aplica de forma práctica la implementación de un sistema de registro de las lecciones aprendidas personales, en la nube a través de una red social Facebook. El proceso inicia con la adquisición de datos a partir de la conexión a una base de datos no relacional (NoSql) en SimpleDB de Amazon Web Services y a la cual se le ha configurado un algoritmo de análisis complementario para realizar el análisis semántico de la información registrada de las lecciones aprendidas y de esta forma estudiar la generación de Gestión de Conocimiento Organizacional (GCO) desde GCP. El resultado final es el diseño de una arquitectura funcional que permite integrar la aplicación web 2.0 y un algoritmo de análisis semántico a partir de información no estructurada aplicando técnicas de aprendizaje de máquina.Palabras Claves: Gestión de conocimiento, gestión de conocimiento personal, lecciones aprendidas, análisis semántico, computación en la nube, redes sociales, aprendizaje de máquina.This paper shows the development of a functional architecture oriented Personal Knowledge Management (PKM), defined from the concept of lessons learned that are registered in a social network for mass use. This functional architecture applied in a practical implementation of a registration system for personal lessons learned in the cloud through a social network Facebook. The process begins with the acquisition of data from the connection to a non-relational database (NoSQL) in SimpleDB of Amazon Web Services and which you have set up a complementary analysis algorithm for semantic analysis of information recorded lessons learned and thus study the generation of Organizational Knowledge Management (OKM) from PKM. The final result is the design of a functional architecture that enables web 2.0 application integration and semantic analysis of an algorithm from unstructured information using machine learning techniques.Keywords: Management of knowledge, management of personal knowledge, lessons learned, semantic analysis, computing in the cloud, social networks.

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