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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63788
Volume:
10 |
5 |
May 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.
MASTER OF COMPUTER APPLICATIONS
BAPUJI INSTITUTE OF ENGINEERONG AND TECHNOLOGY DAVANAGERE
MASTER OF COMPUTER APPLICATIONS
BAPUJI INSTITUTE OF ENGINEERONG AND TECHNOLOGY DAVANAGERE
MASTER OF COMPUTER APPLICATIONS
BAPUJI INSTITUTE OF ENGINEERONG AND TECHNOLOGY DAVANAGERE
Dense due to the development and growth of population makes it difficult for conventional systems of waste removal. Inefficient removal unsegregated and non-optimized system of waste removal results in conservational pollution increase in operational costs and health effects. The proposed project "Big Data-Based Ecosystem for Smart Waste Administration Optimization" thus aims to develop an intelligent and optimized system of waste removal using big data analytics and IoT sensor technologies. Projected Classification The proposed system involves the collection of data from IoT sensors fitted over the waste bins GPS and waste processing units to track waste resources type of wastes and priority areas of removal. Big data analytics will develop this data and help in optimized waste removal routes prediction about potential growth increases of wastes and optimized resource allocation. The porch may also afford material on recycling resource management and reduction of conservation impacts that enable an entity to operate sustainably and competently. Improved efficient effectiveness through real-time monitoring and optimized techniques of big data analytics the proposed ecosystem seeks to reduce conservation impacts and make sure that a healthier environment is preserved.
KEYWORDS
The project “Big Data-Based Ecosystem for Smart Waste Management Optimization” successfully demonstrates how Big Data analytics and predictive modelling can transform traditional waste management systems into intelligent and sustainable solutions. The system efficiently integrates real-time monitoring data analytics and dynamic route optimization to improve operational efficiency and promote environmental sustainability. Through -enabled smart bins the system was able to monitor waste levels accurately and ensure timely waste collection. The integration of GPS tracking with route optimization algorithms minimized fuel consumption reduced travel time and enhanced the overall efficiency of waste collection operations. predictive insights helping municipal authorities forecast waste generation trends allocate resources effectively and plan better collection strategies. Additionally, the system’s interactive dashboard enabled the administrators to have a clear and comprehensive insight into the status of waste management in the city. This approach not only assisted the administrators in making enhanced decisions but it also ensured that the services are delivered in a better way in the urban areas.Therefore it can be concluded that the system developed is an effective means of overcoming the shortcomings of conventional waste management practices with an intelligent automated and sustainable ecosystem. This project elaborates the vast possibilities of merging IoT.
1. Ghosh S. & Bandyopadhyay S. (2021). Big Data Analytics. International Journal of Computer Applications. 2. Kumar R. & Sharma P. (2020). Big Data Analytics for Efficient Urban Waste Management. Journal of Emerging Technologies in Computing. 3. Gupta S. & Verma A. (2019). IoT-Based Smart Bin. 4. Singh P. & Kaur H. (2020). Predictive Analytics in Smart Waste Management Systems. International Journal of Engineering and Technology. 5. United Nations. (2021). Solid Waste Management in Urban Areas: Challenges and Solutions. UN-Habitat Report. 6. Hadoop Official Documentation. (2023). Retrieved from: https://hadoop.apache.org/docs/ 7. Spark Official Documentation. (2023). Retrieved from: https://spark.apache.org/docs/ 8. IBM Big Data Analytics Resources. (2023). Retrieved from: https://www.ibm.com/analytics/big-data 9.Coursera. (2023). IoT and Big Data for Smart Cities. Retrieved from: https://www.coursera.org 10. Zhao X. & Li Y. (2019). IoT-Based Urban Waste Management: A Data-Driven Approach. IEEE Access.
Publication Date
5/22/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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