IoT-Enabled Smart Waste Management Systems for Smart Cities: A Systematic Review

Sosunova, Inna; Porras, Jari

doi:10.1109/access.2022.3188308

Cited by 47 publications

(19 citation statements)

References 183 publications

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“…We can automate, improve, and interpret the entire waste management process in new ways while reducing cost. Smart containers save time and effort [3], as operators know the number of containers that must be collected per day and the number of trucks that go to collect containers, in addition to saving time in classifying waste by type, making it easier for workers to send them to the competent authorities for recycling. The main benefit of IoT is the ability to collect huge amounts of data and update them in real time utilizing costeffective sensors.…”

Section: A the Impact Of Smart Containers And Iot On Waste Managementmentioning

confidence: 99%

“…The literature includes a variety of waste managementrelated research initiatives. Through a design that uses solar energy to feed the system and sensors to keep track of how much waste has been gathered inside the enclosure, authors in [3] introduce the idea of intelligent disposal. If necessary, the container can compress the waste, reducing its volume up to 10 times, even before it is collected.…”

Section: B Related Workmentioning

confidence: 99%

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Towards an IOT Approach for Smart Waste Management based on Context Ontology: A Case Study

Aloui¹,

Almukadi

Belghith

2023

Eng. Technol. Appl. Sci. Res.

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Nowadays, waste management faces the challenge of providing effective and efficient solutions for waste collection, disposal, and recycling while respecting health and environmental standards. This challenge also includes the lack of understanding of the diverse factors that influence the various stages of waste management, inefficient route planning, insufficient resources, etc. Faster collection, management, and processing of waste are possible with smart containers and IoT technologies allowing waste real-time data provision. Thus, this research proposes a waste management system based on generic and comprehensive generic context ontology and smart containers. The context ontology is conceived to solve the limits and the insufficiencies of waste management by covering all the waste facets for all the stakeholders, optimizing, analyzing, and reusing the waste data and conditions. For given smart container and waste management context, we need to have a global view of the relevant contextual data according to a unified model such as the waste environment data, the waste activity context, the waste computing context, the user context, the collaboration context, etc. One significant advantage of our system is that it provides a unified model for waste management contextual data that could be reused for other waste management systems covering all the properties of this domain. The proposed solution implements an intelligent and adaptive IoT system for waste management according to different waste contexts, waste objectives, and waste activities. The proposed system has been successfully tested under different scenarios in Jeddah City Municipality.

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Section: A the Impact Of Smart Containers And Iot On Waste Managementmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

Towards an IOT Approach for Smart Waste Management based on Context Ontology: A Case Study

Aloui¹,

Almukadi

Belghith

2023

Eng. Technol. Appl. Sci. Res.

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“…The authors [5] Innu Sosunova and Jari Porras Eventually, more cooperation between public and private parties will be necessary to effectively improve trash management. Using GPS technology to map the current garbage collection truck routes and identify the places where the collection is not being done.…”

Section: Literature Surveymentioning

confidence: 99%

Iot Based Garbage Monitoring System and Notification Application

Manoj,

Gadiyar

et al. 2023

IJEAST

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: In today's world there is an immense growth in the population of the cities due to this rapid urbanization there is a waste collection that leads to various infectious diseases in the environment. There is a concept with the prototype which we have built that is the Recycle, Reuse, Reduce policies throughout this waste management system. To prevent these bins from becoming overloaded, access to real-time information about each bin is available. The suggested structure lowers personnel costs while also saving the system time and energy. access to each bin's real-time information, preventing overcrowding of these bins. access to each bin's real-time information, preventing overcrowding of these bins. The suggested structure lowers personnel costs while also saving the system time and energy. We mostly utilized Arduino, NODEMCU, Servo Motors, and Ultrasonic Sensors to build this prototype. The software element is the Blynk app, which is utilized to receive notifications. This concept might serve as the foundation for a smart waste disposal system in urban areas.

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“…Waste management is one of the greatest concerns anywhere in the world [ 1 ]. The random process of the rapid growth of population in urban areas makes the situation a more dynamic and challenging task for municipalities [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning

Ahmed

Mubarak

et al. 2022

IJERPH

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The immense growth of the population generates a polluted environment that must be managed to ensure environmental sustainability, versatility and efficiency in our everyday lives. Particularly, the municipality is unable to cope with the increase in garbage, and many urban areas are becoming increasingly difficult to manage. The advancement of technology allows researchers to transmit data from municipal bins using smart IoT (Internet of Things) devices. These bin data can contribute to a compelling analysis of waste management instead of depending on the historical dataset. Thus, this study proposes forecasting models comprising of 1D CNN (Convolutional Neural Networks) long short-term memory (LSTM), gated recurrent units (GRU) and bidirectional long short-term memory (Bi-LSTM) for time series prediction of public bins. The execution of the models is evaluated by Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Coefficient determination (R2) and Root Mean Squared Error (RMSE). For different numbers of epochs, hidden layers, dense layers, and different units in hidden layers, the RSME values measured for 1D CNN, LSTM, GRU and Bi-LSTM models are 1.12, 1.57, 1.69 and 1.54, respectively. The best MAPE value is 1.855, which is found for the LSTM model. Therefore, our findings indicate that LSTM can be used for bin emptiness or fullness prediction for improved planning and management due to its proven resilience and increased forecast accuracy.

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IoT-Enabled Smart Waste Management Systems for Smart Cities: A Systematic Review

Abstract: This research has been carried out in the scope of the CroBoDDIT project (KS1592).

Cited by 47 publications

References 183 publications

Towards an IOT Approach for Smart Waste Management based on Context Ontology: A Case Study

Towards an IOT Approach for Smart Waste Management based on Context Ontology: A Case Study

Iot Based Garbage Monitoring System and Notification Application

Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning

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