Smart Trolley with Advance Billing System
Keywords:
billing trolley, barcode, nodeMCU, shopping.Abstract
The shopping centre is a spot where individuals get their regular necessities. In shopping malls, there has been an emerging market for fast and simple payment of bills. Very sometimes, shoppers are dissatisfied with finding the items on the shopping list while shopping in a store and no help is required. We have developed a smart trolley with a smartphone app to solve these issues. This paper offers an interface to help consumers locate the product's location. It also offers a consolidated and automatic billing system using NodeMCU's barcode scanner. Super markets will be issued with a barcode for each shopping mall commodity, to distinguish its type. A Product Identification System (PID) containing NodeMCU, the barcode reader, is used for each shopping cart. Purchasing product details on the shopping cart can be read by a barcode reader and presented in the mobile app that is linked to the device. The complete bill is passed to the PC by the processor at the billing counter.
References
- A. Farahzadi, P. Shams, J. Rezazadeh, and R. Farahbakhsh, “Middleware technologies for cloud of things-a survey,” Digital Communications and Networks, Elsevier, 2017.
- D. Singh, G. Tripathi, and A. J. Jara, “A survey of internet-ofthings: Future vision, architecture, challenges and services,” in 2014 IEEE World Forum on Internet of Things (WF-IoT), March 2014, pp. 287–292.
- A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, “Internet of things: A survey on enabling technologies, protocols, and applications,” IEEE Communications Surveys Tutorials, vol. 17, no. 4, pp. 2347–2376, Fourthquarter 2015.
- A. Zanella, N. Bui, A. Castellani, L. Vangelista, and M. Zorzi, “Internet of things for smart cities,” IEEE Internet of Things Journal, vol. 1, no. 1, pp. 22–32, Feb 2014.
- J. Rezazadeh, M. Moradi, A. S. Ismail, and E. Dutkiewicz, “Superior path planning mechanism for mobile beaconassisted localization in wireless sensor networks,” Sensors Journal, IEEE, vol. 14, pp. 3052–3064, 2014.
- M. Hubert, M. Blut, C. Brock, C. Backhaus, and T. Eberhardt, “Acceptance of smart phone-based mobile shopping: Mobile benefits, customer characteristics, perceived risks, and the Impact of application context,” Psychology and Marketing, vol. 34, no. 2, pp. 175–194, 2017.
- J. Rezazadeh, M. Moradi, and A. S. Ismail, “Efficient localization via middle-node cooperation in wireless sensor networks,” in International Conference on Electrical, Control And Computer Engineering, June 2011, pp. 410–415.
- M. Moradi, J. Rezazadeh, and A. S. Ismail, “A reverse localization scheme for underwater acoustic sensor networks,” Sensors, vol. 12, pp. 4352–4380, 2012.
- R. Nallanthighal and V. Chinta, “Improved grid-scan localization algorithm for wireless sensor networks,” Journal of Engineering, Hindawi, vol. 5, no. 10, pp. 21–27, 2014.
- P. Martin, B.-J. Ho, N. Grupen, S. Mu˜noz, and M. Srivastava, “An ibeacon primer for indoor localization: Demo abstract,” in Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings, 2014, pp. 190–191.
- J. Rezazadeh, M. Moradi, A. S. Ismail, and E. Dutkiewicz, “Impact of static trajectories on localization in wireless sensor networks,” Wirel.Netw., vol. 21, no. 3, pp. 809–827, 2015.
- J. Wang, P. Urriza, Y. Han, and D. Cabric, “Weighted centroid localization algorithm: Theoretical analysis and distributed implementation,” IEEE Transactions on Wireless Communications, vol. 10, no. 10, pp. 3403–3413, 2014.
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