IoT Big Data Architectures, Approaches, and Challenges

2021 ◽  
pp. 227-250
Author(s):  
David Sarabia-Jácome ◽  
Regel Gonzalez-Usach ◽  
Carlos E. Palau
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Fog Computing ◽  
Big Data Analytics ◽  
Late Response ◽  
Reference Architecture ◽  
Comprehensive Overview ◽  
High Bandwidth ◽  
Iot Devices ◽  
Application Developers

The internet of things (IoT) generates large amounts of data that are sent to the cloud to be stored, processed, and analyzed to extract useful information. However, the cloud-based big data analytics approach is not completely appropriate for the analysis of IoT data sources, and presents some issues and limitations, such as inherent delay, late response, and high bandwidth occupancy. Fog computing emerges as a possible solution to address these cloud limitations by extending cloud computing capabilities at the network edge (i.e., gateways, switches), close to the IoT devices. This chapter presents a comprehensive overview of IoT big data analytics architectures, approaches, and solutions. Particularly, the fog-cloud reference architecture is proposed as the best approach for performing big data analytics in IoT ecosystems. Moreover, the benefits of the fog-cloud approach are analyzed in two IoT application case studies. Finally, fog-cloud open research challenges are described, providing some guidelines to researchers and application developers to address fog-cloud limitations.

IoT Big Data Architectures, Approaches, and Challenges

2019 ◽  
pp. 125-148 ◽  
Author(s):  
David Sarabia-Jácome ◽  
Regel Gonzalez-Usach ◽  
Carlos E. Palau
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Fog Computing ◽  
Big Data Analytics ◽  
Late Response ◽  
Reference Architecture ◽  
Comprehensive Overview ◽  
High Bandwidth ◽  
Iot Devices ◽  
Application Developers

The internet of things (IoT) generates large amounts of data that are sent to the cloud to be stored, processed, and analyzed to extract useful information. However, the cloud-based big data analytics approach is not completely appropriate for the analysis of IoT data sources, and presents some issues and limitations, such as inherent delay, late response, and high bandwidth occupancy. Fog computing emerges as a possible solution to address these cloud limitations by extending cloud computing capabilities at the network edge (i.e., gateways, switches), close to the IoT devices. This chapter presents a comprehensive overview of IoT big data analytics architectures, approaches, and solutions. Particularly, the fog-cloud reference architecture is proposed as the best approach for performing big data analytics in IoT ecosystems. Moreover, the benefits of the fog-cloud approach are analyzed in two IoT application case studies. Finally, fog-cloud open research challenges are described, providing some guidelines to researchers and application developers to address fog-cloud limitations.

Fog Computing Realization for Big Data Analytics

2019 ◽  
pp. 259-290 ◽  
Author(s):  
Farhad Mehdipour ◽  
Bahman Javadi ◽  
Aniket Mahanti ◽  
Guillermo Ramirez-Prado
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Fog Computing ◽  
Big Data Analytics

Big-Data-Based Architectures and Techniques

2019 ◽  
pp. 19-50
Author(s):  
Gopala Krishna Behara
Keyword(s):  
Big Data ◽  
Real World ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Sources ◽  
Reference Architecture ◽  
The Real ◽  
Key Concepts ◽  
Insight Into

This chapter covers the essentials of big data analytics ecosystems primarily from the business and technology context. It delivers insight into key concepts and terminology that define the essence of big data and the promise it holds to deliver sophisticated business insights. The various characteristics that distinguish big data datasets are articulated. It also describes the conceptual and logical reference architecture to manage a huge volume of data generated by various data sources of an enterprise. It also covers drivers, opportunities, and benefits of big data analytics implementation applicable to the real world.

An Automatic Generation of Domain Ontologies Based on an MDA Approach to Support Big Data Analytics

2021 ◽  
pp. 26-45
Author(s):  
Naziha Laaz ◽  
Karzan Wakil ◽  
Sara Gotti ◽  
Zineb Gotti ◽  
Samir Mbarki
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Automatic Generation ◽  
Model Driven ◽  
Ontology Modeling ◽  
E Learning ◽  
Domain Ontologies ◽  
Application Developers ◽  
Work Supports

This chapter proposes a new methodology for the automatic generation of domain ontologies to support big data analytics. This method ensures the recommendations of the MDA approach by transforming UML class diagrams to domain ontologies in PSM level through ODM, which is an OMG standard for ontology modeling. In this work, the authors have focused on the model-driven architecture approach as the best solution for representing and generating ontology artifacts in an intuitive way using the UML graphical syntax. The creation of domain ontologies will form the basis for application developers to target business professional context; however, the future of big data will depend on the use of technologies to model ontologies. With that said, this work supports the combination of ontologies and big data approaches as the most efficient way to store, extract, and analyze data. It is shown using the theoretical approach and concrete results obtained after applying the proposed process to an e-learning domain ontology.

scholarly journals Big Data and Personalisation for Non-Intrusive Smart Home Automation

2021 ◽  
Vol 5 (1) ◽  
pp. 6
Author(s):  
Suriya Priya R. Asaithambi ◽  
Sitalakshmi Venkatraman ◽  
Ramanathan Venkatraman
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Smart Home ◽  
Big Data Analytics ◽  
Cost Effective ◽  
Smart Devices ◽  
Home Automation ◽  
Open Standard ◽  
Iot Devices ◽  
And Control

With the advent of the Internet of Things (IoT), many different smart home technologies are commercially available. However, the adoption of such technologies is slow as many of them are not cost-effective and focus on specific functions such as energy efficiency. Recently, IoT devices and sensors have been designed to enhance the quality of personal life by having the capability to generate continuous data streams that can be used to monitor and make inferences by the user. While smart home devices connect to the home Wi-Fi network, there are still compatibility issues between devices from different manufacturers. Smart devices get even smarter when they can communicate with and control each other. The information collected by one device can be shared with others for achieving an enhanced automation of their operations. This paper proposes a non-intrusive approach of integrating and collecting data from open standard IoT devices for personalised smart home automation using big data analytics and machine learning. We demonstrate the implementation of our proposed novel technology instantiation approach for achieving non-intrusive IoT based big data analytics with a use case of a smart home environment. We employ open-source frameworks such as Apache Spark, Apache NiFi and FB-Prophet along with popular vendor tech-stacks such as Azure and DataBricks.

The Age of Autonomous Internet of Things Devices

2021 ◽  
pp. 1-16
Author(s):  
Mutwalibi Nambobi ◽  
Kanyana Ruth ◽  
Adam A. Alli ◽  
Rajab Ssemwogerere
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Social Sensing ◽  
Smart Systems ◽  
Multiple Sensors ◽  
Sensing Systems ◽  
Measure Blood Pressure ◽  
Iot Devices ◽  
Smart Things

The age of autonomous sensing has dominated almost every industry today. Our lives have been engaged with multiple sensors embedded in our smartphones to achieve sensing of all sorts starting from proximity sensing to social sensing. Our possessions (cars, fridges, oven) have sensors embedded in them. The art of autonomous IoT has shifted from a mere detection of events or changes in the environment to dominant systems for social sensing, big data analytics, and smart things. Recently, sensing systems have adapted connectivity resulting in input mechanisms for big data analytics and smart systems resulting in pervasive systems. Currently, a range of sensors has come to existence, for example, mobile phone sensors that measure blood pressure at patients' figure tip, or the sensors that be used to detect deforestation. In this chapter, the authors provide a technical view upon which autonomous IoT devices can be implemented and enlist opportunities and challenges of the same.

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