| Autor: |
Maria Barbara Safianowska, Yi-Chieh Peter Chang, ChingYao Huang, Te-Jen Wang, Chih-Wei Huang |
| Rok vydání: |
2017 |
| Předmět: |
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| Zdroj: |
FWC |
| DOI: |
10.1109/fwc.2017.8368522 |
| Popis: |
Adopting AR/VR technology on smart retail services is gaining more momentum with the progress in indoor map scanning technology and the research on AI deep learning algorithms. In this paper we propose the use of a Fog computing node to generate an AR/VR view of the real store on a web page. The customers can then use the service robot to view the merchandise in the real store via the web and make purchases. Since the service robot is a precious resource on the AR/VR business model, we develop an auction method to optimize the customer satisfaction and the owner satisfaction in terms of customer waiting time and the average number of transactions that are assisted by the service robot respectively. We demonstrate that the auction method is a critical part in the AR/VR smart business services when the number of service robots is much less than the number of active customers from the web and that it performs better than the standard preemptive method. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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