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一种新型的多层射频识别标签货物完整性保证方案。

A Novel Multilayered RFID Tagged Cargo Integrity Assurance Scheme.

作者信息

Yang Ming Hour, Luo Jia Ning, Lu Shao Yong

机构信息

Chung Yuan Christian University, Chung Pei Road, Chung Li District, Taoyuan 32023, Taiwan.

Ming Chuan University, Deming Road, Guishan District, Taoyuan 33348, Taiwan,.

出版信息

Sensors (Basel). 2015 Oct 23;15(10):27087-115. doi: 10.3390/s151027087.

Abstract

To minimize cargo theft during transport, mobile radio frequency identification (RFID) grouping proof methods are generally employed to ensure the integrity of entire cargo loads. However, conventional grouping proofs cannot simultaneously generate grouping proofs for a specific group of RFID tags. The most serious problem of these methods is that nonexistent tags are included in the grouping proofs because of the considerable amount of time it takes to scan a high number of tags. Thus, applying grouping proof methods in the current logistics industry is difficult. To solve this problem, this paper proposes a method for generating multilayered offline grouping proofs. The proposed method provides tag anonymity; moreover, resolving disputes between recipients and transporters over the integrity of cargo deliveries can be expedited by generating grouping proofs and automatically authenticating the consistency between the receipt proof and pick proof. The proposed method can also protect against replay attacks, multi-session attacks, and concurrency attacks. Finally, experimental results verify that, compared with other methods for generating grouping proofs, the proposed method can efficiently generate offline grouping proofs involving several parties in a supply chain using mobile RFID.

摘要

为了尽量减少运输过程中的货物盗窃,通常采用移动射频识别(RFID)分组证明方法来确保整个货物装载的完整性。然而,传统的分组证明不能同时为特定的一组RFID标签生成分组证明。这些方法最严重的问题是,由于扫描大量标签需要相当长的时间,分组证明中包含了不存在的标签。因此,在当前的物流行业中应用分组证明方法很困难。为了解决这个问题,本文提出了一种生成多层离线分组证明的方法。所提出的方法提供了标签匿名性;此外,通过生成分组证明并自动验证收货证明和提货证明之间的一致性,可以加快解决收货人和运输商之间关于货物交付完整性的争议。所提出的方法还可以防范重放攻击、多会话攻击和并发攻击。最后,实验结果验证了,与其他生成分组证明的方法相比,所提出的方法可以使用移动RFID高效地生成涉及供应链中多个参与方的离线分组证明。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/50c4/4634462/9d1baf13dbd7/sensors-15-27087-g001.jpg

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