During 9 weeks of investigation multiple solutions for implementing a communication module in a supermarket have been explored. These results led to three options which have been further researched. Bluetooth Low Energy (BLE), 433 MHz and ZigBee have been selected as the best communication protocols
for sensor to gateway communication. Chips for these protocols have been bought and for BLE and 433 MHz libraries have been developed. For the gateway and the communication to the server a Raspberry PI and Mobile Internet have been selected and these have been integrated. During the measurements we came to the conclusion that for the RFM69HCW (433 MHz) and the BLE Nano (BLE) the sleep mode did not work as specified. Resulting in high power consumption and thus worse battery life (sleep mode consumption for RFM69HCW 3.37 mA, BLE Nano 4.38 mA and for XBee 43.2 휇A). The final conclusion of this thesis is that, with the current research, XBee (ZigBee) is
the best communication protocol for communicating within a supermarket. But in further research BLE Nano and RFM69HCW can perform better if the sleep mode is improved to work according to what is specified.

This thesis tries to find a solution for the problem of managing and monitoring the banana shelf in a supermarket using IoT.
The research focuses on using a wireless sensor that detects some features of the banana shelf while being non-intrusive.
The three main features that are examined of the shelf are the quality and quantity of the bananas and the quality of the shelf.
First a research was conducted to find the best sensor to use for these measurements.
The chosen sensor is a color image sensor, the platform for the IoT device is a Raspberry Pi.
Using the python programming language in combination with the openCV library image processing was used to detect the features.

The image is first smoothed using a Gaussian filter, afterwards the foreground is segmented.
The different segmentation methods are researched and adaptive thresholding is used.
To determine the quantity of the bananas and quality of the shelf the stickers on the bananas are detected.
This detection is implemented using different filtering methods ranging from spectral filtering to color thresholding.
With the segmented foreground the quality of the bananas is assessed using a color histogram.
This information is then sent to a communication module that is connected to a IoT dashboard for user interpretation.

With the proposed design the status of the shelf including the percentage of the shelf filled, the quality of the bananas on the shelf and the neatness of the shelf are available for a supermarket manager to better organize his supermarket.
This sensor makes it possible to better organize the banana shelf and act preemptive instead of reactive.