{"title":"A novel authentication scheme for secure data sharing in IoT enabled agriculture","authors":"Arun A. Kumbi, M. Birje","doi":"10.3233/web-230244","DOIUrl":null,"url":null,"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.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":"77 1","pages":""},"PeriodicalIF":0.2000,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Web Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/web-230244","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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.
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[...]