Design a smart waste bin for smart waste management

Wijaya, Aksan Surya; Zainuddin, Zainal Abidin; Niswar, Muhammad

doi:10.1109/ica.2017.8068414

Cited by 80 publications

(26 citation statements)

References 7 publications

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“…This module creates ways to save energy, time and ultimately cost from data from dynamically positioned nodes. Wijaya et al [35] offer a smart waste-bin that can manage waste. The system consists of sensors that measure the waste weight and the fill level of the bin and all incoming data is transmitted to the network environment to manage.…”

Section: Literature Review On Digitalizationmentioning

confidence: 99%

Prioritization of public services for digitalization using fuzzy Z-AHP and fuzzy Z-WASPAS

Sergi

Sarı

2021

Complex Intell. Syst.

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In this paper, public services are analyzed for implementations of Industry 4.0 tools to satisfy citizen expectations. To be able to prioritize public services for digitalization, fuzzy Z-AHP and fuzzy Z-WASPAS are used in the analysis. The decision criteria are determined as reduced cost, fast response, ease of accessibility, reduced service times, increase in the available information and increased quality. After obtaining criteria weights using fuzzy Z-AHP, health care services, waste disposal department, public transportation, information services, social care services, and citizen complaints resolution centers are compared using fuzzy Z-WASPAS that is proposed for the first time in this paper. Results show that health care services have dominant importance for the digitalization among public services.

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Section: Literature Review On Digitalizationmentioning

confidence: 99%

Prioritization of public services for digitalization using fuzzy Z-AHP and fuzzy Z-WASPAS

Sergi

Sarı

2021

Complex Intell. Syst.

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“…The survey presented in [14] contained a bin that was equipped with a microcontroller with a wireless system that was used to show the current status of garbage in a dustbin on a mobile phone with an internet connection. In [22], the author used a microcontroller with CCTV cameras to identify and monitor the external environment. Furthermore, RFID tags were used to identify each bin by assigning each bin a unique ID; to identify the extent of waste in each bin, a wireless sensor network was employed by establishing an alert message to the authorized person using an embedded board, a Zigbee module was used to establish communication between different nodes located in a specific range, and an alert message was displayed on the smart bin when it was about to be full.…”

Section: Related Workmentioning

confidence: 99%

“…In the field of the IoT, researchers [22,25] deals with the management of waste by using a smart module integrated with built-in sensors. The data acquired from the hardware module can also be shown on mobile and web applications at the following two levels: (i) level detection on bin cover and (ii) weight detection on the bottom of the bin.…”

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confidence: 99%

Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

et al. 2020

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Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.

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“…The garbage classification system allocates a sensor for each kind of garbage to identify the type of waste through the sensor. In [10], a garbage can composed of sensors is introduced to measure the weight of garbage and the level of garbage in the garbage can. In [11], the author proposes intelligent garbage can with sensor Internet of things model, which is mainly used to measure the garbage level in the garbage can to help workers optimize the collection path.…”

Section: Existing Workmentioning

confidence: 99%

WasNet: A Neural Network-Based Garbage Collection Management System

Yang

2020

IEEE Access

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In response to the increasing pollution caused by unseparated garbage, classification systems for garbage separation have become very popular. First, we constructed a complex data augmentation combination for model training. Second, we designed a novel lightweight neural network garbage classification system called WasNet. This proposed network's 1.5 million parameters on the ImageNet dataset are onehalf of mainstream neural networks, while at 3 million floating point operations per second (FLOPs) it is one third of mainstream neural networks that have obtained the best performance among known lightweight neural networks. The accuracy on the ImageNet data set is 64.5%, on the Garbage Classification dataset it is 82.5%, and on the TrashNet dataset it is 96.10%. Furthermore, we transplanted the model to the hardware platform and assembled an intelligent trash can; we developed a garbage recognition application to facilitate users to directly identify and receive platform information; we built a visualization and decision support platform to help managers monitor traffic in real time. We combined the intelligent trash can, application, visualization and decision-making platform into a system, which is the most complete and effective system among the known research works. The results of the test we conducted on our platform using our extended dataset showed that our scheme is very reliable. At the same time, we also open source our extended datasets for use by other researchers.

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Design a smart waste bin for smart waste management

Cited by 80 publications

References 7 publications

Prioritization of public services for digitalization using fuzzy Z-AHP and fuzzy Z-WASPAS

Prioritization of public services for digitalization using fuzzy Z-AHP and fuzzy Z-WASPAS

Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

WasNet: A Neural Network-Based Garbage Collection Management System

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