Fog Computing for Big Data Analytics in IoT Aided Smart Grid Networks

The rapid deployment of Phasor Measurement Units (PMUs) in power systems globally is leading to Big Data challenges. New high performance computing techniques are now required to process an ever increasing volume of data from PMUs. To that extent the Hadoop framework, an open source implementation of the MapReduce computing model, is gaining momentum for Big Data analytics in smart grid applications. However, Hadoop has over 190 configuration parameters, which can have a significant impact on the performance of the Hadoop framework. This paper presents an Enhanced Parallel Detrended Fluctuation Analysis (EPDFA) algorithm for scalable analytics on massive volumes of PMU data. The novel EPDFA algorithm builds on an enhanced Hadoop platform whose configuration parameters are optimized by Gene Expression Programming. Experimental results show that the EPDFA is 29 times faster than the sequential DFA in processing PMU data and 1.87 times faster than a parallel DFA, which utilizes the default Hadoop configuration settings.

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A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid

Sustainability ◽

10.3390/su132313322 ◽

2021 ◽

Vol 13 (23) ◽

pp. 13322

Author(s):

Vinoth Kumar Ponnusamy ◽

Padmanathan Kasinathan ◽

Rajvikram Madurai Elavarasan ◽

Vinoth Ramanathan ◽

Ranjith Kumar Anandan ◽

...

Keyword(s):

Big Data ◽

Smart Grid ◽

Data Management ◽

Smart Grids ◽

Data Analytics ◽

Management Practices ◽

Research Work ◽

Big Data Analytics ◽

Energy Data ◽

The Sustainable Development

The role of energy is cardinal for achieving the Sustainable Development Goals (SDGs) through the enhancement and modernization of energy generation and management practices. The smart grid enables efficient communication between utilities and the end- users, and enhances the user experience by monitoring and controlling the energy transmission. The smart grid deals with an enormous amount of energy data, and the absence of proper techniques for data collection, processing, monitoring and decision-making ultimately makes the system ineffective. Big data analytics, in association with the smart grid, enable better grid visualization and contribute toward the attainment of sustainability. The current research work deals with the achievement of sustainability in the smart grid and efficient data management using big data analytics, that has social, economic, technical and political impacts. This study provides clear insights into energy data generated in the grid and the possibilities of energy theft affecting the sustainable future. The paper provides insights about the importance of big data analytics, with their effects on the smart grids’ performance towards the achievement of SDGs. The work highlights efficient real-time energy data management involving artificial intelligence and machine learning for a better future, to short out the effects of the conventional smart grid without big data analytics. Finally, the work discusses the challenges and future directions to improve smart grid technologies with big data analytics in action.

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8 Smart grid–based big data analytics using machine learning and artificial intelligence: a survey

10.1515/9783110714043-008 ◽

2021 ◽

pp. 241-278

Author(s):

Subin Koshy ◽

S. Rahul ◽

R. Sunitha ◽

Elizabeth P. Cheriyan

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Big Data ◽

Smart Grid ◽

Data Analytics ◽

Big Data Analytics ◽

Grid Based

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IoT Big Data Architectures, Approaches, and Challenges

Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing ◽

10.4018/978-1-7998-5339-8.ch011 ◽

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.

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