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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64867
Volume:
10 |
6 |
Jun 2026
Pages:
1-11
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Science & Engineering
KG Reddy College of Engineering & Technology
Computer Science & Engineering
KG Reddy College of Engineering & Technology
Computer Science & Engineering
KG Reddy College of Engineering & Technology
Computer Science & Engineering
KG Reddy College of Engineering & Technology
Computer Science & Engineering
KG Reddy College of Engineering & Technology
Computer Science & Engineering
KG Reddy College of Engineering & Technology
Nowadays, the fast-growing urbanization with increasing population have elevated levels of environmental concern which also brings smart waste management as an essential part of smart cities these days. We are going to design and implement a Smart Waste Management System using IoT & Computer Vision technology in this study with the goal of reducing waste collection time, accurately monitoring waste, and optimizing resources. The system is designed using IoT based waste level sensors installed in the waste bins for monitoring and informing real time waste level in them. The sensors relay data to a central control system, which can send out alerts in real-time when bins are full or overflowing. Meanwhile, waste classification based on computer vision and deep learning algorithms is being used to identify types of waste such as biodegradable waste, metal or non-biodegradable plastic recyclable waste recycling [17]. This dual infusion gives it an enhanced detection precision as well as makes the processes more efficient. It contains wireless communication technologies that connect the data from sensors to the cloud, and between the cloud to municipality through a seamless process. A prediction module for waste generation pattern is based on machine learning estimation which optimally schedules the routing of waste collection vehicles. This contributes to the reduction of fuel consumption, operating costs and pollution of the environment. The idealized model has been developed, tested, and implemented to divulge higher efficiencies over the traditional waste management system particularly in terms of minimizing the overflow and improving the accuracy of waste segregation. Furthermore, the proposed system can also be up-scaled to smart city applications and integrated with the existing urban development. It has many advantages, but there are also some shortcomings such as high energy consumption requirements, need for reliable network operation and need for real-time processing.
KEYWORDS
Smart Waste Management System IoT & CV Based example for garbage disposal in smart cities: Solution to solve waste management issues The sensors of the Internet of Things (IoT), wireless communication, cloud computing and artificial intelligence which make waste monitoring, sorting and collection easier & faster. Sensors that measure the fullness of trash cans, dry measuring sensors, and other gases produced by it to keep us updated in a timely manner. This makes it that much easier to determine how much trash is on the way so cities can avoid a looming landfill. Computer Vision is also used in the Smart Waste Management System for sorting wastes and enhancing recycling. These are typically algorithms like CNN (Convolutional Neural Networks) and YOLO (you only look once) which can assess images and comprehend image contents. The Smart Waste Management System is capable of automatic sorting, eliminating the necessity for manual sorting. It minimizes errors in waste sorting performed top down by the public. Very similar to just picking up rubbish, the Smart Waste Management System looks at photos of trash and decides what can be recycled and what can biodegrade. This is what supports us on looking after the environment sustainably. The Smart Waste Management System serves as a tool for the cities to handle waste.
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Publication Date
6/12/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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