ONS resolution prediction based on Rasch model

Huqing Wang; Feng Xiang; Wenbing Zhao; Zhixin Sun; Huqing Wang; Feng Xiang; Wenbing Zhao; Zhixin Sun

doi:10.3934/mbe.2019333

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 6: 6683-6695. doi: 10.3934/mbe.2019333

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Research article Special Issues

ONS resolution prediction based on Rasch model

1.
Technology Research and Development Center of Postal Industry of State Post Bureau, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2.
Yuan Tong Express co. LTD, Shanghai, 201705, China
3.
Cleveland State University, Cleveland; OH, United States

Received: 06 January 2019 Accepted: 04 May 2019 Published: 24 July 2019

IoT (Internet of Things) involves a wide range of fields, and its application scenarios are complex and diverse. Failure of security defense in any link of IoT may lead to huge information leakage and immeasurable losses. IoT security problem is affecting and restricting its application prospect, and has become one of the hotspots in the field of IoT. ONS (Object Naming Service) is responsible for mapping function from EPC code information to URI (Uniform Resource Identifier). The security mechanism of ONS has been extensively studied by more and more scholars in recent years. The purpose of this paper is to apply Rasch, a famous psychological model, to ONS resolution security technology. Through observing the past resolution result, the ability of ONS resolution and the difficulty of EPC code can be calculated. With the difference between the ability of ONS resolution and the difficulty of EPC code, this model can predict the probability of the ONS future resolution to achieve the purpose of privacy protection in IoT addressing. Through simulation and Ministep software, the feasibility of the model is verified.

Keywords:

Citation: Huqing Wang, Feng Xiang, Wenbing Zhao, Zhixin Sun. ONS resolution prediction based on Rasch model[J]. Mathematical Biosciences and Engineering, 2019, 16(6): 6683-6695. doi: 10.3934/mbe.2019333

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Abstract

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