Smart Waste Segregation Systems Using Sensor Networks

  • Authors

    • Ajay Krishnan Technical Lead, Tech Mahindra, India. Author

    DOI:

    https://doi.org/10.67228/30715636/IJETMR-2022PII7V3H

    Published 09-03-2022

  • Smart Waste Management, Sensor Networks, IoT, Machine Learning, Waste Segregation, Smart Cities, Embedded Systems, Environmental Monitoring

    Issue

    Vol. 5 No. 2 (2022)

    Section

    Articles

    How to Cite

    [1]
    A. Krishnan, “Smart Waste Segregation Systems Using Sensor Networks”, IJETMR, vol. 5, no. 2, pp. 01–15, Sep. 2022, doi: 10.67228/30715636/IJETMR-2022PII7V3H.

  • Abstract

    Rapid population growth and industrialization have significantly increased municipal solid waste (MSW) generation worldwide. Inefficient waste segregation leads to environmental degradation, increased landfill usage, greenhouse gas emissions, and poor recycling efficiency. Traditional waste management systems rely on manual segregation and fixed collection schedules, resulting in operational inefficiencies. This paper proposes a Smart Waste Segregation System (SWSS) that integrates sensor networks, embedded systems, Internet of Things (IoT) architecture, and machine learning for automated waste classification and monitoring. The system uses multiple sensors such as moisture, inductive, capacitive, ultrasonic, and gas sensors, along with image-based classification to identify biodegradable, recyclable, metallic, and hazardous waste in real time. Sensor nodes communicate through low-power wireless protocols to a central gateway for cloud-based data analysis. A supervised machine learning algorithm improves classification accuracy and bin-level monitoring. Experimental results show over 92% segregation accuracy with optimized energy consumption and low communication overhead. The system enhances recycling efficiency, reduces landfill waste, and supports scalable, sustainable waste management solutions suitable for smart city environments.

  • References

    [1] M. Arebey, M. A. Hannan, H. Basri, and H. Abdullah, “Solid waste monitoring using ultrasonic sensor and GSM communication system,” Waste Management, vol. 30, no. 8–9, pp. 1402–1407, 2010.

    [2] S. Longhi, D. Marzioni, E. Alidori, G. Di Buò, M. Prist, M. Grisostomi, and M. Pirro, “Solid waste management architecture using wireless sensor network technology,” International Journal of Communications, Network and System Sciences, vol. 5, no. 11, pp. 775–783, 2012.

    [3] N. Anagnostopoulos, A. Zaslavsky, and A. Medvedev, “Robust waste collection exploiting cost-efficient IoT technologies,” IEEE Sensors Journal, vol. 17, no. 21, pp. 7285–7296, 2017.

    [4] M. Folianto, Y. S. Low, and W. L. Yeoh, “Smartbin: Smart waste management system,” in Proc. IEEE TENSYMP, 2015, pp. 1–5.

    [5] K. Shyam, S. Manvi, and P. Bharti, “Smart waste management using Internet of Things (IoT),” in Proc. International Conference on Computing, Communication and Automation, 2017, pp. 199–203.

    [6] J. Yang and G. Thung, “Classification of trash for recyclability status,” Stanford CS229 Project Report, 2016.

    [7] [7] H. Zhang, J. Huang, and X. Wang, “Waste classification using deep convolutional neural networks,” IEEE Access, vol. 8, pp. 123–134, 2020.

    [8] A. Rad, M. von Kaenel, and A. D. S. Rodrigues, “A computer vision system for waste classification,” in Proc. IEEE International Conference on Image Processing (ICIP), 2017, pp. 2234–2238.

    [9] M. Mittal, M. Iwendi, S. Khan, and A. R. Javed, “A deep learning approach for classification of waste materials using convolutional neural networks,” Computers & Electrical Engineering, vol. 86, 2020.

    [10] S. A. Al Mamun, M. A. Hannan, A. Hussain, and H. Basri, “Integrated sensing systems and algorithms for solid waste bin state management automation,” IEEE Sensors Journal, vol. 15, no. 1, pp. 561–567, 2015.

    [11] A. Glouche and P. Couderc, “A smart waste management with self-describing objects,” in Proc. International Conference on Next Generation Mobile Applications, Services and Technologies, 2013, pp. 61–66.

    [12] C. Hong, D. Park, and B. Kim, “IoT-based smart garbage system for efficient food waste management,” Sensors, vol. 14, no. 10, pp. 18984–19002, 2014.

    [13] S. Kumar, R. Singh, and A. Kumar, “Real-time garbage monitoring system using IoT,” International Journal of Engineering Research & Technology, vol. 7, no. 3, pp. 1–5, 2018.

    [14] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436–444, 2015.

    [15] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems (NIPS), 2012, pp. 1097–1105.

  • Downloads