Sus-Bin: Sustainable Battery-Free Smart Garbage Bin for Solid Waste Management in Smart City

Solid waste management has emerged as a significant challenge in smart cities, resulting in severe health and environmental concerns. Municipal tash bins are strategically placed throughout a city; nevertheless, in the absence of real-time monitoring, these bins are often observed to be overflow. It results in environmental pollution, and adverse effects on public sanitation. Thus, there is substantial necessity of real-time solid wate management using which helps the concerned authority in real-time monitoring and accordingly schedule garbage cleaning. This work proposes a sustainable, LoRaWAN-enabled battery-free smart garbage bin framework. The proposed system can harness energy from sunlight using a thin-film Photovoltaic (PV) harvester integrated with an ultra-low power management system. Subsequently, it powers the sensor node to measure the garbage level and trasmit it to a cloud-hosted application sever. Long Range Wide Area Network (LoRaWAN) technology has been incorprated for wireless communication which operates at 867 MHz and covers a communication range up to 361 meter within dense populated area. It has been observed experimentally, that the system harvests energy at an average rate of 10.44 mW under clear sky condition. Subsequently, the power consumption of the system has been optimized using duty cycle technique resulting in average consumption of 131 μW. On the other hand, a hybrid multi-objective framework has been proposed integrating Tiny Machine Learning (TinyML) and Tiny Genetic Algorithm (TinyGA) techniques. The framework predicts the garbage fullness in a garbage bin exhibiting an accuracy of 96.22 % and subsequently provides an optimized route for the garbage cleaning vehicle for maintenance. It ensures minimum fuel consumption, travel distance, travel time, emissions, and cost in garbage cleaning vehicles. The system has been extensively deployed on a real-world test bed to validate its sustainable operation. Thus, the proposed system can be considered as an ultra-low power battery-free alternative to state-of-the-art smart solid waste management systems that align with Sustainable Development Goal (SDG) 7 of the United Nations.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3628872
Primary Topic
Municipal Solid Waste Management
Type
article
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0.00
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article

Sus-Bin: Sustainable Battery-Free Smart Garbage Bin for Solid Waste Management in Smart City

Prajnyajit Mohanty, Mohammed A. Bou-Rabee, Doaa Khalil Ibrahim, Al Anoud et al.
IEEE Access
Municipal Solid Waste Management
article

Sus-Bin: Sustainable Battery-Free Smart Garbage Bin for Solid Waste Management in Smart City

Prajnyajit Mohanty, Mohammed A. Bou-Rabee, Doaa Khalil Ibrahim, Al Anoud, Imad Al Balaa
article en

Abstract

Solid waste management has emerged as a significant challenge in smart cities, resulting in severe health and environmental concerns. Municipal tash bins are strategically placed throughout a city; nevertheless, in the absence of real-time monitoring, these bins are often observed to be overflow. It results in environmental pollution, and adverse effects on public sanitation. Thus, there is substantial necessity of real-time solid wate management using which helps the concerned authority in real-time monitoring and accordingly schedule garbage cleaning. This work proposes a sustainable, LoRaWAN-enabled battery-free smart garbage bin framework. The proposed system can harness energy from sunlight using a thin-film Photovoltaic (PV) harvester integrated with an ultra-low power management system. Subsequently, it powers the sensor node to measure the garbage level and trasmit it to a cloud-hosted application sever. Long Range Wide Area Network (LoRaWAN) technology has been incorprated for wireless communication which operates at 867 MHz and covers a communication range up to 361 meter within dense populated area. It has been observed experimentally, that the system harvests energy at an average rate of 10.44 mW under clear sky condition. Subsequently, the power consumption of the system has been optimized using duty cycle technique resulting in average consumption of 131 μW. On the other hand, a hybrid multi-objective framework has been proposed integrating Tiny Machine Learning (TinyML) and Tiny Genetic Algorithm (TinyGA) techniques. The framework predicts the garbage fullness in a garbage bin exhibiting an accuracy of 96.22 % and subsequently provides an optimized route for the garbage cleaning vehicle for maintenance. It ensures minimum fuel consumption, travel distance, travel time, emissions, and cost in garbage cleaning vehicles. The system has been extensively deployed on a real-world test bed to validate its sustainable operation. Thus, the proposed system can be considered as an ultra-low power battery-free alternative to state-of-the-art smart solid waste management systems that align with Sustainable Development Goal (SDG) 7 of the United Nations.

IEEE AccessVol. 13
Public Authority for Applied Education and Training (KW), National Institute of Technology Rourkela (IN), International Institute of Information Technology (IN), International Institute of Information Technology (IN), Ministry of Energy (IL)
Openalex Percentile: Top 60%
Municipal Solid Waste Management
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