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An Optimized IoT-enabled Big Data Analytics Architecture for Edge-Cloud Computing.

Muhammad Babar1, Mian Ahmad Jan2, Xiangjian He3

  • 1Department of Computer Science, Allama Iqbal Open University (AIOU), Islamabad, Pakistan.

IEEE Internet of Things Journal
|December 4, 2023
PubMed
Summary
This summary is machine-generated.

Edge computing enhances Internet of Things (IoT) data analytics by introducing an edge intelligence module for efficient processing and storage. This optimized architecture addresses big data challenges in IoT environments.

Keywords:
Backpropagation (BP) Neural NetworkBig Data AnalyticsEdge ComputingInternet of Things (IoT)Machine LearningYet Another Resource Negotiator (YARN)

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Area of Science:

  • Computer Science
  • Data Science
  • Network Engineering

Background:

  • Edge computing is crucial for Internet of Things (IoT) due to scalability and latency demands.
  • Existing IoT Big Data analytics frameworks struggle with massive, heterogeneous data, storage, processing, and communication overhead.
  • Current solutions lack efficient parallel data loading and robust communication handling mechanisms.

Purpose of the Study:

  • To propose an optimized IoT-enabled big data analytics architecture for edge-cloud computing using machine learning.
  • To introduce an edge intelligence module for efficient edge data processing and storage, integrated with cloud technology.
  • To address the challenges of data volume, heterogeneity, and processing time in IoT Big Data analytics.

Main Methods:

  • An optimized two-layer architecture: IoT-edge and Cloud-processing.
  • Implementation of an edge intelligence module for efficient data handling at the network edge.
  • Utilized an optimized MapReduce parallel algorithm for data injection and storage.
  • Employed Optimized Yet Another Resource Negotiator (YARN) for efficient cluster management.
  • Experimentally simulated the proposed design using Apache Spark with an authentic dataset.

Main Results:

  • The proposed architecture demonstrates efficient processing and storage of big data at the network edge.
  • Optimized MapReduce and YARN contribute to effective data handling and cluster management.
  • Experimental simulations validate the efficiency of the proposed edge-cloud architecture.
  • Comparative analysis shows superior performance against existing proposals and traditional mechanisms.

Conclusions:

  • The proposed optimized IoT-enabled big data analytics architecture effectively addresses the challenges of edge-cloud computing.
  • The integration of edge intelligence and cloud technology enhances Big Data analytics for IoT applications.
  • The architecture provides an efficient, scalable, and robust solution for handling massive and heterogeneous IoT data.