HIERARCHICAL TRUST-ORIENTED BROKER FEDERATION WITH FINE-GRAINED SECURITY ENFORCEMENT FOR SECURE AND ELASTIC MQTT ARCHITECTURES

Authors:

Snowlin Preethi Janani,J. Immanuel JohnRaja,P. Getzi Jeba Leelipushpam,

DOI NO:

https://doi.org/10.26782/jmcms.2026.05.00003

Keywords:

Hierarchical broker federation,Message Queuing Telemetry Transport,Trust management,Attribute-Based Access Control,IoT,Broker trust evaluation,,Access control policies,Message routing,Distributed systems security.,

Abstract

The rapid proliferation of large-scale Internet of Things (IoT) systems has imposed stringent requirements on Message Queuing Telemetry Transport (MQTT) infrastructures for scalability, security, and trust management. This research proposes a Hierarchical Trust-Oriented Broker Federation (HTBF) framework with fine-grained security enforcement to enable secure and elastic MQTT architectures across distributed edge–cloud environments. The proposed architecture organizes MQTT brokers into a three-tier hierarchy (edge, regional, and core layers), where inter-broker communication is governed by a dynamic trust evaluation model based on behavioral reliability, authentication success rate, and traffic anomaly scores. A lightweight trust computation function based on a discounted Bayesian state-space model enables real-time trust adaptation with negligible computational overhead (<2.1 ms per update). Fine-grained security policies are enforced using Attribute-Based Access Control (ABAC) combined with topic-level authorization, enabling per-client, per-topic, and per-payload security decisions. Experimental evaluation was conducted on a federated testbed comprising 30 brokers and 10,000 concurrent MQTT clients, deployed across edge and cloud nodes. Results demonstrate that the proposed HTBF model achieves a 43.7% reduction in unauthorized message propagation, a 31.2% improvement in broker resilience under coordinated attack scenarios, and a 27.5% decrease in average message latency compared to flat broker federation. Under high-load conditions (100,000 messages/s), the system maintained a throughput of 92,400 messages/s, with an average end-to-end latency of 18.6 ms and packet loss below 0.8%. Additionally, trust-based routing reduced malicious broker participation by 48.3%, significantly improving overall system reliability.

Refference:

I. Agarwal, Sheetal, and Rupal Gupta. “Edge Computing for Energy Efficient IoT.” Energy Efficient Internet of Things?Based Wireless Sensor Network (2026): 187-215. 10.1002/9781394314751. ch7
II. Akshatha, P. S., and SM Dilip Kumar. “MQTT and blockchain sharding: An approach to user-controlled data access with improved security and efficiency.” Blockchain: Research and Applications 4.4 (2023): 100158. 10.1016/j.bcra.2023.100158
III. Al Hanif, Abdulelah, and Mohammad Ilyas. “Effective feature engineering framework for securing MQTT protocol in IoT environments.” Sensors 24.6 (2024): 1782. 10.3390/s24061782
IV. Allaga, Hamza, Mohamed Biniz, and Abderrazak Farchane. “MQTTEEB-D: A high-fidelity benchmark for real-time MQTT anomaly detection using machine learning techniques.” Ad Hoc Networks (2025): 104062. 10.1016/j.adhoc.2025.104062
V. Alqazzaz, Ali. “SecuFL-IoT: an adaptive privacy-preserving federated learning framework for anomaly detection in smart industrial networks.” Scientific Reports (2026). 10.1109/ICISS67859.2026.11453976
VI. Azzedin, Farag, and Turki Alhazmi. “Secure data distribution architecture in IoT using MQTT.” Applied Sciences 13.4 (2023): 2515. 10.3390/app13042515
VII. Chen, Ran, et al. “Blockchain-based MQTT communication access control scheme for the Internet of Things.” Second International Conference on Electronic Information Technology (EIT 2023). Vol. 12719. SPIE, 2023. 10.1117/12.2685781
VIII. Dhokane, Nilima Tatyasaheb, et al. “S-MQTT: A Secure MQTT Protocol with Merkle Tree Authentication and AES Encryption for IoT Communication Systems.” Ingenierie des Systemes d'Information 30.8 (2025): 1963. 10.18280/isi.300803
IX. Kamoun-Abid, Ferdaous, and Amel Meddeb-Makhlouf. “Enhanced MQTT Protocol for Securing Big Data/Hadoop Data Management.” Journal of Sensor and Actuator Networks 15.1 (2026): 22. 10.3390/jsan15010022
X. Ko, Kyeong Il, and Meong Hun Lee. “MQTT-Based Architecture for Real-Time Data Collection and Anomaly Detection in Smart Livestock Housing.” Sensors 25.23 (2025): 7186.
10.1109/HealthCom60686.2025.11343673
XI. Kurdi, Hassan, and Vijey Thayananthan. “A multi-tier MQTT architecture with multiple brokers based on fog computing for securing industrial IoT.” Applied Sciences 12.14 (2022): 7173. 10.3390/app12147173
XII. Maawi, Kholoud Nasser Al, and Munir Abdullah Abduh Qa'id. “A Review on Intrusion Detection Systems for MQTT in IoT Environments.” International Journal of Safety & Security Engineering 15.8 (2025). 10.18280/ijsse.150818
XIII. Radwan, Nael M., and Frederick T. Sheldon. “Experimental Evaluation of MQTT Authentication Mechanisms: Reliability, Enforcement Accuracy, and Security Implications.” (2026). 10.3390/app16073583
XIV. Thanh Binh, Bui Ngoc, et al. “A Protocol-Aware P4 Pipeline for MQTT Security and Anomaly Mitigation in Edge IoT Systems.” arXiv e-prints (2026): arXiv-2601. 10.48550/arXiv.2601.07536
XV. Wang, Ziang, et al. “Research on the Development of a Building Model Management System Integrating MQTT Sensing.” Sensors 25.19 (2025): 6069. 10.3390/s25196069

View Download