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MQTT-L: a lightweight end-to-end secure protocol with renewable hash chains for IoT environments

Chengzhi Yu · Menglong Qi · Han Luo · Jie Tian · Jintian Lu
10.1007/s44443-026-00972-4 399 Views 0 Citations
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Abstract

Abstract
The Message Queuing Telemetry Transport (MQTT) protocol has become a de-facto standard for lightweight communication in the Internet of Things. However, the standard MQTT lacks built-in security mechanisms for authentication and end-to-end data protection. Existing research has enhanced MQTT security via TLS, reliance on trusted authorities, and lightweight cryptographic extensions, but these approaches suffer from significant computational overhead, heavy reliance on trusted Brokers, and poor scalability in resource-constrained environments. Accordingly, this paper proposes MQTT-L, a lightweight end-to-end secure protocol designed for resource-constrained devices. MQTT-L introduces salt-driven renewable one-way hash chains to achieve lightweight Client-to-Broker authentication and fast reconnection. Furthermore, a key agreement mechanism based on extended Chebyshev chaotic maps is designed to establish end-to-end session keys, and the ChaCha20-Poly1305 cipher is integrated into MQTT-L to ensure the confidentiality and integrity of end-to-end messages. To verify the security and authentication properties of MQTT-L, we utilize the well-known ProVerif tool for formal security verification and further prove its security informally. Finally, performance evaluations, including theoretical analysis, and experimental evaluations conducted on a Mosquitto MQTT Broker testbed, demonstrate the superiority of MQTT-L over existing schemes and TLS-MQTT.

Cite this Article (APA)
Chengzhi, Y., Menglong, Q., Han, L., Jie, T., Jintian, L. (2026). MQTT-L: a lightweight end-to-end secure protocol with renewable hash chains for IoT environments. Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1007/s44443-026-00972-4
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Published in
ISSN 1319-1578
Quartile Q1
AMS Score 100
Field Computer Science & AI
Publisher Elsevier / King Saud University
Country 🇸🇦 Saudi Arabia
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Publication Details
Year 2026
Language English
Added 06 Jul 2026