Inspiration
The use of new technologies can greatly help in automating and improving the complex functions of waste management during hajj. Waste is a crucial and challenging problem that deserves to be addressed. Enhancement in this area can greatly enhance the hajj experience, and save a lot of operations cost as well.
What it does
The solution will help automate and improve the functions of managing waste through various technologies and methodologies including IoT, Machine Learning, Crowd Sourcing and later on Computer Vision.
The system will automatically guide the drivers to attend the right trash cans at the right time; all based on:
- Trash can levels
- History of specific cans levels change throughout history (Prediction)
- Drivers locations
The system will also collect data from the crowd which will help in providing a better picture to the wast mgmt. teams to understand the area. This might also be used later as one additional factor with the machine learning algorithms later.
The system can also tell if the sensor is not working (malfunctioning) by detecting unrealistic readings (if they are constant, or if they don't change according to the drivers visits)
How I built it
A mobile application for end users (drivers and the hajj crowd) + A web application for the management teams + simulation data for generating the trash cans locations and sensors.
Challenges I ran into
Real world simulation in the development environment in such a short time.
Accomplishments that I'm proud of
The sensors solution design is unique and has nothing like it in the market especially the hardware communication part through mesh networks. We also worked with many technologies to come up with a complete and unique solution that can truly provide a value for the hajj event.
What I learned
Experience with the different technologies and their integration.
What's next for M -004 Waste Mgmt. System (إماطة)
Taking this project to the next level. The innovative ideas in this project make it perfect for implementations beyond hajj. We will enhance the machine learning part of the solution, and we will add extra value features such as computer vision which can add to the solution algorithms for better decision making.


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