Scholarlink

<p>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 <i>‘Data Handlers’</i> 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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An online tool based on the Internet of Things and intelligent blockchain technology for data privacy and security in rural and agricultural development

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月27日

查看全文
  • Krishnaprasath Vellimalaipattinam Thiruvenkatasamy,
  • Hayder M. A. Ghanimi,
  • Sudhakar Sengan,
  • Meshal Ghalib Alharbi

摘要

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.