A novel authentication scheme for secure data sharing in IoT enabled agriculture

IF 0.2 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Arun A. Kumbi, M. Birje
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引用次数: 0

Abstract

Now a days, the Internet of Things (IoT) plays a vital role in every industry including agriculture due to its widespread and easy integrations. The agricultural methods are incorporated with IoT technologies for significant growth in agricultural fields. IoT is utilized to support farmers in using their resources effectively and support decision-making systems with better field monitoring techniques. The data collected from IoT-based agricultural systems are highly vulnerable to attack, hence to address this issue it is necessary to employ an authentication scheme. In this paper, Auth Key_Deep Convolutional Neural Network (Auth Key_DCNN) is designed to promote secure data sharing in IoT-enabled agriculture systems. The different entities, namely sensors, Private Key Generator (PKG), controller, and data user are initially considered and the parameters are randomly initialized. The entities are registered and by using DCNN a secret key is generated in PKG. The encryption of transmitted data is performed in the data protection phase during the protection of data between the controller and the user. Additionally, the performance of the designed model is estimated, where the experimental results revealed that the Auth Key_DCNN model recorded superior performance with a minimal computational cost of 142.56, a memory usage of 49.5 MB, and a computational time of 1.34 sec.
物联网农业数据安全共享的新型认证方案
如今,物联网(IoT)因其广泛和易于集成的特点,在包括农业在内的各行各业都发挥着至关重要的作用。农业方法与物联网技术相结合,促进了农业领域的显著发展。物联网可帮助农民有效利用资源,并通过更好的田间监测技术为决策系统提供支持。从基于物联网的农业系统中收集的数据极易受到攻击,因此有必要采用一种身份验证方案来解决这一问题。本文设计了 Auth Key_Deep 卷积神经网络(Auth Key_DCNN),以促进物联网农业系统中的安全数据共享。最初考虑了不同的实体,即传感器、私钥生成器(PKG)、控制器和数据用户,并随机初始化了参数。各实体注册后,使用 DCNN 在 PKG 中生成密钥。在数据保护阶段,在控制器和用户之间的数据保护过程中,对传输的数据进行加密。此外,还对所设计模型的性能进行了评估,实验结果表明 Auth Key_DCNN 模型性能优越,计算成本最低,为 142.56 美元,内存使用量为 49.5 MB,计算时间为 1.34 秒。
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来源期刊
Web Intelligence
Web Intelligence COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
0.90
自引率
0.00%
发文量
35
期刊介绍: Web Intelligence (WI) is an official journal of the Web Intelligence Consortium (WIC), an international organization dedicated to promoting collaborative scientific research and industrial development in the era of Web intelligence. WI seeks to collaborate with major societies and international conferences in the field. WI is a peer-reviewed journal, which publishes four issues a year, in both online and print form. WI aims to achieve a multi-disciplinary balance between research advances in theories and methods usually associated with Collective Intelligence, Data Science, Human-Centric Computing, Knowledge Management, and Network Science. It is committed to publishing research that both deepen the understanding of computational, logical, cognitive, physical, and social foundations of the future Web, and enable the development and application of technologies based on Web intelligence. The journal features high-quality, original research papers (including state-of-the-art reviews), brief papers, and letters in all theoretical and technology areas that make up the field of WI. The papers should clearly focus on some of the following areas of interest: a. Collective Intelligence[...] b. Data Science[...] c. Human-Centric Computing[...] d. Knowledge Management[...] e. Network Science[...]
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