Abstract: This study presents an improved MQTT protocol designed to address broker congestion and connection overflow in large-scale IoT networks. The proposed method integrates Huffman Deep Compression (HDC) at the publisher side to mitigate network traffic and latency. Unlike standard MQTT, which suffers from broker overload, our approach applies efficient data compression on resource-constrained sensor devices prior to publishing. The proposed approach was validated on a real-world air pollution dataset collected from the Tanjung Malim monitoring station in Malaysia, using ESP8266-based IoT nodes. Experimental results demonstrated that broker congestion was reduced by 84.26% for QoS 0 and 79.6% for QoS 1, significantly outperforming both standard MQTT and the state-of-the-art MRT-MQTT (58% and 45%, respectively). The method attained a high compression ratio of 2.62, which directly led to a dramatic reduction in power consumption from 2,664,864 to 63,216 mA (QoS 0) and from 3,155,760 to 49,168 mA (QoS 1). This substantial saving in current consumption contributes to extended device lifetime and enhanced energy efficiency. The findings highlight the potential of this enhanced protocol to support massive IoT deployments by minimizing network overhead at the broker.

Ammar Nasif, Zulaiha Ali Othman, Nor Samsiah Sani and Yousra Abudaqqa. “MQTT Broker Congestion Mitigation Using Huffman Deep Compression”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.1 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170170

@article{Nasif2026,
title = {MQTT Broker Congestion Mitigation Using Huffman Deep Compression},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170170},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170170},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {1},
author = {Ammar Nasif and Zulaiha Ali Othman and Nor Samsiah Sani and Yousra Abudaqqa}
}