Smart Grocery Store
- Weight count of the grocery stock.
- Humidity and Temperature monitoring and adjustment.
- Smart Pricing for goods.
- Business Side App for store managers to monitor stock.
- Client Side App for customers to get price updates.
- Sensor calibration for optimized climate control.
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- To help store managers keep a stock check and provide fresh and good quality products to customers, to maximize store profits.
- To enable customers to buy cheap and fresh products.
- To let people shop smarter by getting good deals as well as know that their products are fresh.
- To help in reducing waste and improving stock management.
- To make grocery stores a more interesting place to shop when there is dynamic change in prices.
- 1. Weight Sensor
- 2. Temperature & Humidity Sensor
- 3. Raspberry Pi 2
- 4. GrovePi+
- 5. Fan & Mist System
The various components deployed here are:
- Sensors: Temperature and Humidity sensor & Weight Sensor
- Fan & Mist System.
- Raspberry Pi and GrovePi+
- Manager and Client side Android Apps
- Web apps for the Store Manager
- The weight sensors measure the change in weight of the grocery items and notify the manager about the low stock.
- The Temperature & Humidity sensors continuously monitor the immediate surrounding conditions of the grocery items and accordingly actuates the fan and the mist system to maintain the optimum temperature and humidity settings for the groceries.
- Based on the freshness of the groceries in a particular stack, the prices are varied to allow quicker sale of those items.
- All the processing is done on the Raspberry Pi complemented by the GrovePi+ .
- The Client side android app and web app allows customers to keep a check of availability of their favourite groceries and their prices, without being physically present at the store.
- Whilst the Manager side app and the web app allows him to keep a stock check of the groceries and accordingly order their replinishment.
1. Patra, Jagdish Chandra, Ganapati Panda, and Rameswar Baliarsingh. "Artificial neural network-based nonlinearity estimation of pressure sensors." IEEE Transactions on Instrumentation and Measurement 43.6 (1994): 874-881.
2. Stoessel, Franziska, et al. "Life cycle inventory and carbon and water foodprint of fruits and vegetables: application to a Swiss retailer." Environmental science & technology 46.6 (2012): 3253-3262.
3. Aburto, Luis, and Richard Weber. "Improved supply chain management based on hybrid demand forecasts." Applied Soft Computing 7.1 (2007): 136-144
4. Hastings, John, David Laverty, and D. John Morrow. "A smart grid information system for demand side participation: Remote control of domestic appliances to balance demand." Power Engineering Conference (UPEC), 2013 48th International Universities'. IEEE, 2013.
5. Rouillard, Jose. "The Pervasive Fridge. A smart computer system against uneaten food loss." Seventh International Conference on Systems (ICONS2012). 2012.
6. Lofshult, Diane. "All that rot: how to select and store produce to prolong its freshness." IDEA Health & Fitness Source 22.4 (2004): 24-28.
7. Banon, Sebastian, et al. "Hardening of oleander seedlings by deficit irrigation and low air humidity." Environmental and Experimental Botany 56.1 (2006): 36-43.
Contact Name 1: Florian Shabanaj, email: email@example.com
Contact Name 2: Ming Zhou, email: firstname.lastname@example.org
Contact Name 3: Rahul Rana, email: email@example.com
Columbia University Department of Electrical Engineering
Class Website: Columbia University EECS E4764 Fall '16 IoT Instructor: Professsor Xiaofan (Fred) Jiang