ArticleScientific reports2025
An online tool based on the Internet of Things and intelligent blockchain technology for data privacy and security in rural and agricultural development.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- RiceBlock-0.2: secured precision rice farming framework using blockchain technology.Scientific reports · 2026Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The standard implementation of the Internet of Things (IoT) has renovated numerous sectors, supporting agriculture with modern technological development. Termed Agriculture-Internet of Things (Agri-IoT), this combination has helped in Smart Farming (SF) using wireless sensors that record real-time data improvement sustainable agriculture practices like irrigation, pest control, and overall field operations. So far, Agri-IoT research faces challenges, mainly focusing on data security and management, which are vulnerabilities in existing centralized solutions. Enter Blockchain Technology (BCT): a decentralized, transparent, and perfect mechanism that improves data security and access control and paves the technique for efficient transactions. This research work introduces a novel multi-tiered BCT personalized for Agri-IoT. The model comprises Edge, Fog, and Cloud levels, employing discrete 'Data Handlers' for each tier, confirming an efficient data lifecycle. Central to this model is the proposed Quantum Neural Network + Bayesian Optimization (QNN + BO), a practiced algorithm that, when combined with methods like the Elliptic Curve Cryptography (ECC) and Coyote Optimization Algorithm (COA), guarantees secure data flow, processing, and storage. The proposed QNN + BO model, evaluated using the ToN_IoT dataset, validates significant performance enhancements - reducing encryption and decryption times by up to 46.7% and 54.6%, and improving prediction accuracy with a 19.3% Mean Absolute Percentage Error (MAPE), outperforming baseline models. Additionally, it consumes up to 33% less memory, supporting its suitability for resource-constrained agricultural environments. This integrative model proposes a complete solution to connect Agri-IoT's potential while addressing its challenges.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.