A LOCATION-BASED SMART SHOPPING SYSTEM WITH IOT TECHNOLOGY

Abstract

Localization is one important part of Internet of Things (IoT) where the Location of Everything (LoE) system plays an important role to improve most services in IoT area. On the other hand, data mining techniques are essential analyses when we have big data from IoT platforms. Indeed, integration of location-based methods and data mining analysis process can make a smart system service for IoT scenarios and applications. For this purpose, we design a smart shopping platform including four components, location of everything component, data collection component, data filtering/analysing component and data mining component. Then a novel accurate localization scheme named “location orbital” is developed that estimates the current location of mobile objects (users or everything) based on both current and the previous locations. Finally, an implementation of the experiment in a shopping mall is conducted to practically examine performance evaluation of the location-based scheme. The experimental results show that the proposed scheme could achieve significant higher precision than other localization techniques.

Existing System

RFID, ZigBee, and MCU(Microcontroller) Unit

Proposed System

The proposed system is based on IoT devices such as iBeacons, Raspberry pi, Tile-BLE, smart devices and etc. In this framework, each customer has a special profile that can manage it any time by a user-friendly application. The system offers goods categories that are available at the shopping mall. So, the users can select favorite categories in their profile. The system provides a shopping list which users can write a list of what they need to buy before shopping. On the other hand, every day all offers update via a central management application. Therefore, when users come to the shopping mall, the users’ app help them to find best offer according to their profile and help them to find appropriate path to collect their items in the shopping mall. The users’ information such as searched goods and the traveled paths will be recorded at the central server so the system can analyse and apply data mining method to make a better recommendation system based on users activities. 

Architecture

BLOCK DIAGRAM