Automated data process in participatory sensing using QR-code and EAN-13 barcode
Advancement of digital technology nowadays has led to the creation of various type of mobile devices such as smartphone, tablet, phablet, computer and many more. Internet also is one of an important element to either connecting people or spreading of an information. This contributes to the creation...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/68913/1/FSKTM%202018%2030%20-%20IR.pdf |
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| Summary: | Advancement of digital technology nowadays has led to the creation of various type of mobile devices such as smartphone, tablet, phablet, computer and many more. Internet also is one of an important element to either connecting people or spreading of an information. This contributes to the creation large amount of data or information such as big data. Big data is a phrase for huge data sets having large, more variety and complicated element with the challenges of storing, analyzing and visualizing for further actions and obtaining the results. However, maintaining data integrity for specific item or information is always being a challenge. In this paper, Quick Response Code (QR code) and EAN-13 barcode was used to enhancing the previous work. The QR code was used as a mechanism to activating the function for mobile application and determining the location, while EAN-13 barcode was used as a product identification. Both mechanism was used to maintain data integrity between the prices corresponding to the product. Thus, correct and updated crowdsourced data are stored in the database are based on real-time data and location that was submitted by the user or known as crowdsourcer or crowdworker for this work. The enhanced algorithm was evaluated using a developed prototype which is an Android mobile application of a crowdsourcing data submission based on product price and information, WE+Price, in which, the algorithm was embedded. The results showed that the algorithm was able to preserving data integrity with 99.13% and up to 100% accuracy. |
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