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A Light-Weight Commodity Integrity Detection Algorithm Based on Chinese Remainder Theorem

Authors: Chaoliang Li; Guojun Wang;

A Light-Weight Commodity Integrity Detection Algorithm Based on Chinese Remainder Theorem

Abstract

Internet of things (IOT), which is based on radio frequency identifier (RFID) technology, has many important applications, especially in warehouse management. In a modern company, there are a huge amount of commodities that get in or get out of a warehouse. In order to make sure that every transaction in warehouse management is right, it is necessary to design a fast and light-weight commodity integrity detection algorithm. In this paper, we propose a commodity integrity detection algorithm (CIDA) based on Chinese remainder theorem (CRT), which compares the hash value of each commodity identifier and the product of these values with those values that have been stored in a database in advance. Then, we conduct experiments to compare the proposed algorithm with a typical commodity detection protocol, called trusted reader protocol (TRP). Experimental results show that the proposed algorithm has better properties: (1) it can detect the integrity of commodities very fast; (2) it can provide efficient security and privacy protection.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
13
Top 10%
Top 10%
Average
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