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Messaging Protocols for IoT Systems-A Pragmatic Comparison.

Sensors (Basel, Switzerland)·2021

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Energy Footprint and Reliability of IoT Communication Protocols for Remote Sensor Networks.

Jerzy Krawiec¹, Martyna Wybraniak-Kujawa¹, Ilona Jacyna-Gołda¹

¹Faculty of Mechanical and Technological, Warsaw University of Technology, 00-661 Warsaw, Poland.

Sensors (Basel, Switzerland)

|October 16, 2025

Summary

This study introduces a unified framework to evaluate IoT/IIoT communication protocols for energy efficiency and reliability. MQTT-SN and CoAP are most efficient, extending node lifetime up to 35%.

Keywords:

IoT RS-IoT UAV communication protocols energy consumption maintainability reliability sensor networks

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Area of Science:

Computer Science
Electrical Engineering
Network Engineering

Background:

Communication protocols in IoT/IIoT systems consume significant energy, limiting remote sensor node operational longevity.
Previous research on protocol energy efficiency is fragmented, lacking comparable metrics across diverse technologies and communication layers.

Purpose of the Study:

To design and validate a unified evaluation framework for consistently assessing wired and wireless IoT/IIoT protocols.
To enable comparative analysis of energy efficiency, reliability, and maintenance costs across different communication protocols.

Main Methods:

Utilized laboratory measurements on physical hardware, Single-Board Computer (SBC) device profiling, and simulations in the COOJA/Powertrace environment.
Developed a Unified Comparative Method incorporating bilinear interpolation and weighted normalization, validated with a Spearman rank correlation coefficient > 0.9.

Main Results:

MQTT-SN and CoAP (non-confirmable mode) demonstrated the highest energy efficiency.
HTTP/3 and AMQP exhibited the greatest energy overhead.
Results are presented in the ICoPEP matrix, linking protocol traits to specific IoT scenarios (UAVs, ocean buoys, etc.).

Conclusions:

The developed framework provides engineering guidelines to extend node lifetime by up to 35% through lightweight protocols and optimized sampling.
This reproducible, technology-agnostic tool addresses a research gap, enabling better design of energy-efficient and reliable IoT/IIoT infrastructures.
The framework supports scalable, long-term deployments in diverse application environments.