Blockchain-Assisted Authentication and Energy-Efficient Clustering Framework for Secure IoT Communication
DOI:
https://doi.org/10.4108/eetiot.9520Keywords:
Authentication, Blockchain, Cluster Head, Differential Privacy, Internet of Things, Lightweight encryptionAbstract
Securing sensitive information is a challenging task in an IoT environment due to the resource constraints of edge devices. Simultaneously, ensuring energy-efficient communication is equally important in clustered IoT networks. Resource depletion impacts the overall network lifetime. Existing methodologies treat authentication and routing as separate tasks, which leads to security vulnerabilities. Many existing authentication techniques use centralized control and static key management and are therefore vulnerable to various kinds of attacks including impersonation and brute force attacks. To address these issues, a blockchain based decentralized authentication and privacy-preserving framework for clustered IoT networks is proposed in this paper. A Trusted Domain Authority (TDA) enforces authentication among IoT devices, users, and gateways. It securely stores the authentication credentials of these entities in the blockchain and creates dynamic session keys for the Cluster Heads(CHs). The clusters are formed using a ranking-based K-Nearest Neighbour (KNN) algorithm. Reward-based Deep Q-Learning (OR-DQL) selects optimal CHs by using energy, vicinity, and trust as parameters. Additionally, the proposed framework safeguards packet headers from traffic analysis using a bounded Laplace differential privacy mechanism. The TDA generates dynamic session keys using Chinese Remainder Theorem (CRT) and securely distributes them to the CHs using the lightweight PRESENT cipher. The proposed system is implemented on the NS-3 network simulator. The simulation results demonstrate an improvement in throughput by 30.7%, a reduction in energy consumption by 24%, and a reduction in end-to-end delay by 27.7% compared to protocols such as ESMR and PBA. These results confirm that the suggested system can provide energy efficient secure communication in IoT networks.
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References
[1] Wakili A, Bakkali S. Privacy‐preserving security of IoT networks: A comparative analysis of methods and applications. Cyber Security and Applications. 2025;3.
[2] Rosa P, Souto A, Cecílio J. Light-SAE: A Lightweight Authentication Protocol for Large-Scale IoT Environments Made with Constrained Devices. IEEE Trans Netw Serv Manag. 2023;20(3):2428–2441
[3] Kumar A, Saha R, Conti M, Kumar G, Buchanan WJ, Kim TH. A comprehensive survey of authentication methods in Internet-of-Things and its conjunctions. J Netw Comput Appl. 2022; 204.
[4] Zerrouki F, Ouchani S, Bouarfa H. PUF-based mutual authentication and session key establishment protocol for IoT devices. J Ambient Intell Human Comput. 2023;14:12575–12593.
[5] Saadati M, Mazinani SM, Khazaei AA, Chabok SJSM. Energy efficient clustering for dense wireless sensor network by applying Graph Neural Networks with coverage metrics. Ad Hoc Netw. 2024;156.
[6] Niakanlahiji A, Orlowski S, Vahid A, Jafarian JH. Toward practical defense against traffic analysis attacks on encrypted DNS traffic. Comput Secur. 2023;124.
[7] Mandal S, Bera B, Sutrala AK, Das AK, Choo KKR, Park Y. Certificateless Signcryption-Based Three-Factor User Access Control Scheme for IoT Environment. IEEE Internet Things J. 2020;1(1):1–1.
[8] Sathyadevan S, Achuthan K, Doss R, Pan LP. Protean Authentication Scheme – A Time-bound Dynamic KeyGen Authentication Technique for IoT Edge Nodes in Outdoor Deployments. IEEE Access. 2019;1:1–1.
[9] Haseeb K, Islam N, Almogren A, Din IU, Almajed HN, Guizani N. Secret sharing-based energy-aware and multi-hop routing protocol for IoT-based WSNs. IEEE Access. 2019;7:79980–79988.
[10] Fang L, Zhang H, Li M, Ge C, Liu L, Liu Z. A Secure and Fine-grained Scheme for Data Security in Industrial IoT Platforms for Smart City. IEEE Internet Things J. 2020;1:1–1.
[11] Luo X, Yin L, Li C, Wang C, Fang F, Zhu C, Tian Z. A Lightweight Privacy-Preserving Communication Protocol for Heterogeneous IoT Environment. IEEE Access. 2020;1:1–1.
[12] Cui Z, Xue F, Zhang S, Cai X, Cao Y, Zhang W, Chen J. A Hybrid BlockChain-Based Identity Authentication Scheme for Multi-WSN. IEEE Trans Serv Comput. 2020;1:1–1.
[13] Misra S, Mukherjee A, Roy A, Saurabh N, Rahulamathavan Y, Rajarajan M. Blockchain at the Edge: Performance of Resource-Constrained IoT Networks. IEEE Trans Parallel Distrib Syst. 2021;32(1):174–183.
[14] Chiang TH, Lo HY, Lin SD. A ranking-based KNN approach for multi-label classification. In: Proceedings of the Asian Conference on Machine Learning; 2012. Singapore: PMLR; 2012.
[15] Merah M, Aliouat Z, Mabed H. A Novel Cluster Head Selection Algorithm Based on Q-Learning for Internet of Things Networks. In: Proceedings of the 2024 International Conference on Telecommunications and Intelligent Systems (ICTIS); 2024; Djelfa, Algeria. p. 1–6.
[16] Zhu T, Ye D, Wang W, Zhou W, Yu PS. More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence. IEEE Trans Knowl Data Eng. 2022;34(6):2824–2843.
[17] Kadiyala R, Lakshmi Narayana CV, China Ramu S, Putta N, Pabboju SS, Ramana Reddy B. Trust-Aware Federated Learning with Differential Privacy for Secure AIoT in Critical Infrastructures. EAI Endorsed Trans IoT [Internet]. 2025 Dec. 2
[18] Rawool MPF. Chinese Remainder Theorem and its Applications. Goa: GOA University; 2024.
[19] Madhu GC, Perumal V. Encryption with automatic key generation and compression. In: Proceedings of the Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT); Aug. 2022; Kannur. p. 295–301.
[20] Bogdanov A, Knudsen L, Leander G, Paar C, Poschmann A, Robshaw MJ, Seurin Y, Vikkelsoe C. PRESENT: An ultra-lightweight block cipher. In: Proceedings of the Cryptographic Hardware and Embedded Systems (CHES); Sep. 10–13 2007; Vienna, Austria. Springer; 2007. p. 450–466.
[21] Madhu GC, Kumar PV. A survey and analysis of different lightweight block cipher techniques for resource-constrained devices. Int J Electron Secur Digit Forensics. 2022;14(1):96–110.
[22] Latah M, Toker L. Minimizing false positive rate for DoS attack detection: A hybrid SDN-based approach. ICT Express. 2020;6(2):125–127.
[23] Madhu, G.C., Vijayakumar, P. and Gao, X.Z. Resource constrained IOT environments: A survey. Journal of Advanced Research in Dynamical and Control Systems, 2017, 9(Special Issue 16), pp.445-457.
[24] J. J, K. T. Yadav CHT, M. Bharathi, M. G. C, V. K. Peroumal and R. Mitra. Software based performance evaluation of data encryption algorithms. In: Proceedings of the 2025 International Conference on Electronics, Computing, Communication and Control Technology (ICECCC); 2025; Bengaluru, India. Bengaluru: ICECCC; 2025. p. 1–5.
[25] G. C. Madhu, K. S. Reddy, M. M. Shabir, J. R. Kumar, G. P. Kumar and D. Srihari. Comparative analysis of energy aware routing techniques in WSN. In: Proceedings of the 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE); IEEE; 2024. p. 926–929.
[26] S. Gnana Selvan, Jerith GG, C. Mahesh, Ravikumar S, Shaffi SS, S. Jagadeesh. A Trust Based Energy Efficient Routing Design Based on Hybrid Particle Swarm Optimization (HPSO) for Wireless Sensor Networks . EAI Endorsed Trans IoT [Internet]. 2025 Sep. 16 [cited 2025 Dec. 5];11
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