Secure distributor data storage and retrieval of unstructured data in blockchain enabled edge computing

Abstract

In modern IoT applications, managing large volumes of unstructured data securely and efficiently is a growing challenge, especially within blockchain-enabled edge computing environments. Traditional data storage and retrieval methods often fall short in terms of error detection, indexing efficiency, and secure data handling. To address these limitations, this research proposes a secure and intelligent distributor framework for the storage and retrieval of unstructured data using a blockchain-supported edge computing model. The system architecture is composed of three layers, such as the IoT network layer for data collection, the blockchain-based edge computing layer for secure data handling, and the cloud layer for scalable storage. The proposed framework introduces a novel indexing mechanism, the Optimal Cluster Inverted Index (OCII), which is computed using a newly designed Taylor Fire Hawk Optimizer (Taylor FHO), which is the hybridization of the Taylor series and Fire Hawk Optimizer (FHO). The data handling framework involves five key processes, like KeyGeneration, OCII Generation, AuthGen, Check, and Dynamics, ensuring secure indexing, authentication, and data validation. Experimental evaluation demonstrates that the Taylor FHO achieves a better precision of 86.067%, recall of 87.080%, F-measure of 87.748%, and indexing time of 0.401 sec. This research provides a scalable and secure solution for real-time unstructured data processing in IoT systems.