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A Modular Framework for Data Processing at the Edge: Design and Implementation.

Lubomir Urblik¹, Erik Kajati¹, Peter Papcun¹

¹Department of Cybernetics and Artificial Intelligence, Faculty of EE & Informatics, Technical University of Kosice, 042 00 Kosice, Slovakia.

Sensors (Basel, Switzerland)

|September 9, 2023

Summary

This study introduces a containerized edge computing framework for efficient Internet of Things (IoT) data processing. It addresses cloud limitations, offering a unified, modular solution for time-sensitive edge analytics.

Keywords:

Docker Kubernetes containerization data processing framework edge computing

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Area of Science:

Computer Science
Data Science
Network Engineering

Background:

The proliferation of Internet of Things (IoT) devices generates massive datasets, overwhelming traditional cloud architectures due to latency, bandwidth, and privacy concerns.
Existing edge computing solutions lack a unified approach, often requiring device-specific applications or relying on containerization solely for deployment and monitoring.

Purpose of the Study:

To propose and evaluate a universal, modular edge computing framework for efficient data processing and analysis.
To address the heterogeneity of edge environments through a standardized, containerized approach.

Main Methods:

Implementation of an edge computing framework utilizing containerization for scalable deployment.
Development of a streaming Extract, Transform, Load (ETL) pipeline for real-time data processing.
Utilization of ZeroMQ as the communication backbone for efficient data transfer.

Main Results:

The proposed framework effectively processes and analyzes data at the network edge.
Containerization enables scalable and flexible deployment across diverse edge environments.
The streaming ETL pipeline demonstrates suitability for time-sensitive IoT applications.

Conclusions:

The developed edge computing framework offers a unified and modular solution for IoT data processing challenges.
Containerization is a key enabler for efficient and scalable edge analytics.
The framework is well-suited for time-sensitive applications requiring low latency and high throughput.