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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM.AITCON015
Volume:
10 |
4 |
April 2026
Pages:
1-5
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 Technology
AITRC Vita.
Computer Technology
AITRC Vita.
Computer Technology
AITRC Vita.
Computer Technology
AITRC Vita.
Computer Technology
AITRC Vita.
Electronic waste (E-waste) is one of the fastest-growing waste streams in the world due to rapid technological advancements and frequent replacement of electronic devices. Improper disposal of electronic waste leads to severe environmental and health hazards because it contains toxic materials such as lead, mercury, cadmium, and arsenic. Traditional waste management systems rely on manual monitoring and inefficient collection processes, which often result in illegal dumping and delayed recycling operations. This paper proposes an IoT-based Smart E-Waste Management System that enables efficient monitoring, tracking and management of electronic waste through smart sensors and real- time communication technologies. The proposed system integrates sensor-enabled smart bins, microcontrollers, IoT communication modules, and cloud-based monitoring platforms to detect and track the level of electronic waste deposited in designated bins. Sensors such as ultrasonic sensors and weight sensors measure the bin capacity
KEYWORDS
This research presents an IoT-based Smart E- Waste Management System designed to improve the efficiency of electronic waste monitoring and collection. By integrating smart sensors, IoT communication modules, and cloud-based monitoring platforms, the system enables real-time tracking of waste bins and automated notification to collection authorities. The proposed system significantly improves waste collection efficiency while reducing operational costs and environmental impact. Future research can integrate machine learning algorithms for automated e-waste classification and predictive waste management systems.
[1] Forti, V., Baldé, C. P., Kuehr, R., & Bel, G., “The Global E-waste Monitor 2020,” United Nations University, Bonn, Germany. [2] Robinson, B., “E-waste: An assessment of global production and environmental impacts,” Science of the Total Environment, vol. 408, no. 2,pp. 183–191, 2019. [3] Atzori, L., Iera, A., & Morabito, G., “The Internet of Things: A Survey,” Computer Networks, vol. 54, no. 15, pp. 2787–2805, 2018. [4] Kumar, N., & Singh, R., “IoT-Based Smart Garbage Monitoring System,” International Journal of Engineering Research and Technology, vol. 9, no. 5, pp. 120–125, 2021. [5] Zhang, S., Li, Y., & Wang, X., “Smart Waste Management System Using IoT and Cloud Computing,” IEEE Access, vol. 8, pp. 125–134, 2020. [6] Patel, D., & Shah, P., “RFID-Based Electronic Waste Tracking System,” Proceedings of IEEE International Conference on Green Computing, pp. 22
Publication Date
4/30/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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