Blockchain-Enhanced Security Framework for Industrial IoT and Vehicular Networks with ChaCha20-Poly1305 Encryption and Zero Knowledge Proof
Downloads
In this paper, a novel security framework for industrial internet of things (IIoT) and vehicular networks is proposed, integrating blockchain technology with advanced encryption and data classification mechanisms to enhance data integrity, confidentiality and trustworthiness. The work employed ChaCha20-Poly1305 encryption to safeguard the data transaction to local cluster nodes. A private blockchain gateway then processes the encrypted data, classifying it based on confidentiality levels, and directing storage either to cloud servers or the interplanetary file system (IPFS). To ensure data integrity, a proof of authority consensus mechanism within the blockchain is incorporated, while zero knowledge proof (ZKP) methods are used for authentication and secure data access. Empirical evaluations demonstrate that our framework achieves a data transmission security rate of 97.5%, with an average encryption and decryption latency of 150 milliseconds, significantly improving over traditional methods. The proof of authority consensus mechanism exhibits a transaction validation speed of 300 transactions per second, showcasing enhanced efficiency compared to standard blockchain models. Furthermore, the integration of ZKP challenges results in a 30% reduction in unauthorised access attempts, indicating a substantial improvement in overall security. This work emphasises the need for continuous innovation in addressing the various security issues in IoT, ultimately advancing the operational efficiency and security of these systems.
Downloads
Rane SB, Narvel YAM. Data-driven decision making with Blockchain-IoT integrated architecture: A project resource management agility perspective of industry 4.0. Int J Syst Assur Eng Manag. 2022;13:1005–1023. DOI: 10.1007/s13198-021-01377-4.
Ma N, et al. Blockchain + IoT sensor network to measure, evaluate and incentivize personal environmental accounting and efficient energy use in indoor spaces. Appl Energy. 2023;332:120443. DOI: 10.1016/j.apenergy.2022.120443.
Alrubei SM, Ball E, Rigelsford JM. A secure blockchain platform for supporting AI-enabled IoT applications at the edge layer. IEEE Access 2022;10:18583–18595. DOI: 10.1109/ACCESS.2022.3151370.
Bataineh MR, Mardini W, Khamayseh YM, Yassein MMB. Novel and secure blockchain framework for health applications in IoT. IEEE Access. 2022;10:14914–14926. DOI: 10.1109/ACCESS.2022.3147795.
Mathur S, et al. A survey on role of blockchain for IoT: Applications and technical aspects. Comput Netw. 2023;227:109726. DOI: 10.1016/j.comnet.2023.109726.
Guru A, et al. A survey on consensus protocols and attacks on blockchain technology. Appl Sci. 2023;13:2604. DOI: 10.3390/app13042604.
Serrano R, et al. ChaCha20–Poly1305 authenticated encryption with additional data for transport layer security 1.3. Cryptography. 2022;6:30. DOI: 10.3390/cryptography6020030.
Chi PW, Lu YH, Guan A. A privacy-preserving zero-knowledge proof for blockchain. IEEE Access. 2023;11:85108–85117. DOI: 10.1109/ACCESS.2023.3302691.
Chatamoni A, Bhukya R. Lightweight compressive sensing for joint compression and encryption of sensor data. Int J Eng Technol Innov. 2022;12:167–181. DOI: 10.46604/ijeti.2022.8599.
Yang W, Wang S, Hu J, Karie NM. Multimedia security and privacy protection in the internet of things: Research developments and challenges. Int J Multim Intell Secur. 2022;4(1):20–46. DOI: 10.1504/IJMIS.2022.10044461
Ma Z, Zhu L, Yu FR, James J. Protection of surveillance recordings via blockchain-assisted multimedia security. Int J Sens Netw. 2021;37(2):69–80. DOI: 10.1504/IJSNET.2021.118486.
Singh R, Dwivedi AD, Mukkamala RR, Alnumay WS. Privacy-preserving ledger for blockchain and internet of things-enabled cyber-physical systems. Comput Electr Eng. 2022;103:108290. DOI: 10.1016/j.compeleceng.2022.108290.
Singh R, et al. A privacy preserving internet of things smart healthcare financial system. IEEE Internet Things J. 2022;1. DOI: 10.1109/JIOT.2022.3233783.
Sharma G, Kalra S. A novel scheme for data security in cloud computing using quantum cryptography. Proc Int Conf Adv Inf Commun Technol Comput AICTC’16. Association for Computing Machinery, New York, NY, USA; 2016. DOI: 10.1145/2979779.2979816.
Ahad MA, et al. IoT data management—security aspects of information linkage in IoT systems. In: Peng S, Pal S, Huang L, editors. Principles of Internet of Things (IoT) ecosystem: Insight paradigm. Springer, Cham; 2020. p.439–464. DOI: 10.1007/978-3-030-33596-0_18.
Bansal S, Kumar D. IoT ecosystem: A survey on devices, gateways, operating systems, middleware and communication. Int J Wireless Inf Networks. 2020;27(3):340–364. DOI: 10.1007/s10776-020-00483-7.
Belli L, et al. IoT-enabled smart sustainable cities: Challenges and approaches. Smart Cities. 2020;3(3):1039–1071. DOI: 10.3390/smartcities3030052.
Haris I, et al. CPS/IoT ecosystem: Indoor vertical farming system. Proc IEEE 23rd Int Symp Consumer Technologies (ISCT). IEEE; 2019. p.47–52. DOI: 10.1109/ISCE.2019.8900974.
Hassan WH. Current research on internet of things (IoT) security: A survey. Comput Netw. 2019;148:283–294. DOI: 10.1016/j.comnet.2018.11.025.
Isakovic H, et al. CPS/IoT ecosystem: A platform for research and education. In: Chamberlain R, Taha W, Törngren M, editors. Cyber physical systems. Springer, Cham; 2019. p.206–213. DOI: 10.1007/978-3-030-23703-5_12.
Peng S, Pal S, Huang L. Principles of Internet of Things (IoT) ecosystem: Insight paradigm. Springer, Cham; 2020. DOI: 10.1007/978-3-030-33596-0.
Zhao W, Yi L. Research on the evolution of the innovation ecosystem of the internet of things: A case study of Xiaomi (China). Procedia Comput Sci. 2022;199:56–62. DOI: 10.1016/j.procs.2022.01.008.
Dobraunig C, Eichlseder M, Mendel F, Schläffer M. Ascon v1.2: Lightweight authenticated encryption and hashing. J Cryptol. 2021;34(3):33. DOI: 10.1007/s00145-021-09398-9.
Alajlan NN, Ibrahim DM. TinyML: Enabling of inference deep learning models on ultra-low-power IoT edge devices for AI applications. Micromachines. 2022;13(6):851. DOI: 10.3390/mi13060851.
Liu H, et al. Tiny machine learning (TinyML) for efficient channel estimation and signal detection. IEEE Trans Veh Technol. 2022;71(6):6795–6800. DOI: 10.1109/TVT.2022.3163786.
Cao B, et al. A many-objective optimization model of industrial internet of things based on private blockchain. IEEE Netw. 2020;34(5):78–83. DOI: 10.1109/MNET.011.1900536.
Almalki FA, et al. Green IoT for eco-friendly and sustainable smart cities: Future directions and opportunities. Mobile Netw Appl. 2023;28:178–202. DOI: 10.1007/s11036-021-01790-w.
Copyright (c) 2026 Santhosh NANDEESWARAN, Gopalakrishnan VARADARAJAN
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
