A Lightweight Decentralized Framework for Secure Data Integrity in Industrial IoT

Authors

  • E Pavithra Author
  • B G Selvam Author
  • K Keerthana Author

DOI:

https://doi.org/10.64751/

Keywords:

Internet of Things, blockchain, privacy, security, lightweight clients

Abstract

Internet of Things (IoT) devices are commonly used in industrial settings to keep an eye on things like furnace temperature and the state of agricultural soil. The data they collect is usually sent to central computers to be stored and analyzed. Centralized storage, on the other hand, can be changed by people inside or outside the company. To check its accuracy, third-party tools are often needed, which raises both the cost and the security risk. To solve this problem, blockchain technology is used to create an autonomous end-to-end security system that stores data in a way that can't be changed or tampered with and doesn't depend on third-party verifiers. Each data exchange is saved as a block with a unique hashcode that checks the integrity of the entries that come after it. This makes it easy to spot changes that were not made by the intended person. Lightweight SPECK encryption and SHA-256 hashing are also used to protect data while it's being sent, which improves speed for IoT devices that don't have a lot of resources. To make things even safer and more efficient, CHACHA20 encryption is built in to speed up computations and lower the risk of attack, and compressed SHA hashcodes cut down on storage needs. By using smart contracts for automated control and consensus techniques to make sure transactions are true, this method supports scalability, privacy, and strong authentication. Overall, this method protects the privacy and integrity of data in Industrial IoT applications without slowing down devices or letting outside verification systems see sensitive data.

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Published

2026-04-08

How to Cite

E Pavithra, B G Selvam, & K Keerthana. (2026). A Lightweight Decentralized Framework for Secure Data Integrity in Industrial IoT. International Journal of Data Science and IoT Management System, 5(2(1), 63-70. https://doi.org/10.64751/

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