论文标题

用于优化智能农业化学传感的纳米链的块链和互联网

Block Chain and Internet of Nano-Things for Optimizing Chemical Sensing in Smart Farming

论文作者

Vimalajeewa, Dixon, Thakur, Subhasis, Breslin, John, Berry, Donagh P., Balasubramaniam, Sasitharan

论文摘要

使用纳米互联网(IOT)将物联网(IoT)使用(IOT)可以进一步扩展决策系统(DMS),以提高可靠性,因为它提供了更多更详细的数据以做出决策的新范围。但是,诸如数据安全性,透明度和处理能力等越来越多的问题挑战了它们在现实应用程序中的使用。与区块链(BC)技术集成的DM可以为克服此类挑战做出巨大贡献。尚未研究ION和IOT与BC一起制造DMS的使用。这项研究提出了一个由BC驱动的IONT(BC-IONT)系统,用于在农场管理的背景下传感化学水平。这是智能农业的关键应用,旨在通过控制化学物质来改善可持续的农场实践。 BC-IONT系统包括使用Langmuir分子结合模型和贝叶斯理论形成的新型机器学习模型,并用作感测化学物质水平的智能合约。信用模型用于量化农场的可追溯性和信誉,以确定它们是否符合化学标准。检测分布式BC-IONT方法的化学物质的准确性> 90%,集中式方法<80%。同样,感知化学物质水平的效率取决于农场之间化学水平的采样频率和变异性。

The use of Internet of Things (IoT) with the Internet of Nano Things (IoNT) can further expand decision making systems (DMS) to improve reliability as it provides a new spectrum of more granular level data to make decisions. However, growing concerns such as data security, transparency and processing capability challenge their use in real-world applications. DMS integrated with Block Chain (BC) technology can contribute immensely to overcome such challenges. The use of IoNT and IoT along with BC for making DMS has not yet been investigated. This study proposes a BC-powered IoNT (BC-IoNT) system for sensing chemicals level in the context of farm management. This is a critical application for smart farming, which aims to improve sustainable farm practices through controlled delivery of chemicals. BC-IoNT system includes a novel machine learning model formed by using the Langmuir molecular binding model and the Bayesian theory, and is used as a smart contract for sensing the level of the chemicals. A credit model is used to quantify the traceability and credibility of farms to determine if they are compliant with the chemical standards. The accuracy of detecting the chemicals of the distributed BC-IoNT approach was >90% and the centralized approach was <80%. Also, the efficiency of sensing the level of chemicals depends on the sampling frequency and variability in chemical level among farms.

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