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An NB-IoT-Based Edge-of-Things Framework for Energy-Efficient Image Transfer
Sikandar Zulqarnain Khan1, Yannick Le Moullec1, Muhammad Mahtab Alam1
1Thomas Johann Seebeck Department of Electronics, Tallinn University of Technology, 19086 Tallinn, Estonia.
Sensors (Basel, Switzerland)
|September 10, 2021
Summary
Machine learning optimizes visual data transmission in NB-IoT networks. This smart gateway reduces data size and quality, significantly cutting transmissions, energy use, and time.
Area of Science:
- Computer Science
- Electrical Engineering
- Telecommunications
Background:
- Internet of Things (IoT) networks face challenges with energy efficiency and data transmission.
- Narrowband Internet of Things (NB-IoT) networks require optimization for visual data handling.
- Machine learning (ML) offers potential solutions for reducing data redundancy and improving transmission efficiency.
Purpose of the Study:
- To develop a smart gateway integrating ML algorithms for energy-efficient visual data transmission over NB-IoT.
- To optimize visual data size and quality using supervised and unsupervised ML before transmission.
- To reduce channel occupancy, energy consumption, and data transmission time in NB-IoT networks.
Main Methods:
- Integration of a low-power processing unit with an NB-IoT radio to create a smart gateway.
- Implementation of supervised and unsupervised ML algorithms for visual data optimization.
- On-field testing and performance evaluation of the smart gateway.
Main Results:
- Achieved up to 93% reduction in NB-IoT radio transmissions.
- Demonstrated up to 90.5% reduction in NB-IoT radio energy consumption.
- Reduced data transmission time by up to 90%.
Conclusions:
- The proposed smart gateway effectively utilizes ML for energy-efficient visual data transmission over NB-IoT.
- ML-driven data optimization significantly enhances the performance of NB-IoT networks.
- The solution offers substantial improvements in transmission reduction, energy savings, and time efficiency.
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