Smart bins, equipped with sensors and IoT technologies, play a crucial role in optimizing waste collection by providing real-time data on bin fill levels. This paper introduces a Markovian Agent Model to simulate and evaluate different garbage collection strategies in a smart bin system. By analyzing various alarm thresholds and routing policies, the study identifies optimal approaches for minimizing overflows and enhancing collection efficiency. The results demonstrate that a strategy combining responsive alarm handling with route resumption (Resume policy) and a higher alarm threshold improves system stability and operational effectiveness.
Barbierato, E., Gatti, A., Gribaudo, M., Iacono, M., Performance Evaluation of Smart Bin Systems Using Markovian Agents for Efficient Garbage Collection, Paper, in LECTURE NOTES IN COMPUTER SCIENCE, (Venezia, 24-27 June 2024), Springer Science and Business Media Deutschland GmbH, Amsterdam 2024:15454 60-74. 10.1007/978-3-031-80932-3_5 [https://hdl.handle.net/10807/326946]
Performance Evaluation of Smart Bin Systems Using Markovian Agents for Efficient Garbage Collection
Barbierato, Enrico
Primo
Validation
;
2024
Abstract
Smart bins, equipped with sensors and IoT technologies, play a crucial role in optimizing waste collection by providing real-time data on bin fill levels. This paper introduces a Markovian Agent Model to simulate and evaluate different garbage collection strategies in a smart bin system. By analyzing various alarm thresholds and routing policies, the study identifies optimal approaches for minimizing overflows and enhancing collection efficiency. The results demonstrate that a strategy combining responsive alarm handling with route resumption (Resume policy) and a higher alarm threshold improves system stability and operational effectiveness.
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