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Sensor-less Smart Waste Management System
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In order to improve the municipal solid waste management efficiency, smart management approaches have been proposed such as wireless sensor network architecture solution which includes the use of sensors to detect the garbage bin fill levels and vehicle route optimization techniques. Experimental results show that we can save up to 35% of the operational cost by improving the efficiency of solid waste management. In this thesis, a new low-cost architecture solution is proposed for improving the efficiency of municipal solid waste management without the use of sensors. Instead, a messaging application is used to ask the customers for pick up of garbage. Based on their reply, the prototype architecture uses a cluster-first route-second method that implements a clustering algorithm with truck capacity as the constraint and solves a travelling salesman algorithm in each cluster. The prototype architecture consists of a back-end server that implements sweep clustering algorithm for clustering the customers by their location and solves travelling salesman problem with dynamic programming method in each cluster, firebase realtime database and front-end using android application for the mobile. The experimental results show that the prototype system can adapt to the change in dataset size and truck capacity constraints. We have observed that with an increase in truck capacity constraint, the number of clusters formed for the data set decreases. Forward and backward sweep clustering methods have been compared where there is no significant difference in the results produced. The dataset has been generated manually due to unavailability of real data from various sources. As a future work, we need to test the prototype with the real data in order to produce more accurate results.

Place, publisher, year, edition, pages
2019. , p. 50
Series
IT ; 19001
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-393301OAI: oai:DiVA.org:uu-393301DiVA, id: diva2:1352567
Educational program
Master Programme in Computer Science
Supervisors
Examiners
Available from: 2019-09-19 Created: 2019-09-19 Last updated: 2019-09-19Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Output format
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