Smart Bin with Automated Metal Segregation and Optimal Distribution of the Bins

Saranya, K.; Vijayaraj, Sujan; Abivishaq, Balasubramanian; Kanna, K. Nithish

doi:10.1007/978-981-13-7968-0_9

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…e waste is tracked using an RFIDbased system integrating the Web information at the host server, and an online platform has been introduced where users can log in to check their total points, which can be redeemed for products and services. Reference [19] discusses features of a smart bin that can segregate metal and non-metal waste with the help of an RLC circuit, which is activated only in the proximity of the user, and the purpose of the RLC metal detector was to separate metal and nonmetal to reuse and recycle the metal. NodeMCU with ESP8266 Wi-Fi module was used to send the real-time status of the bin to the cloud.…”

Section: Introductionmentioning

confidence: 99%

“…eir CNN model has about 95.3% waste classification accuracy, and the system usability scale was 86%. Reference [19] proposed an E-bin, which would contain a pair of light-emitting diode (LED) displays. e total amount of waste accumulated is indicated by the first display, while the display will exhibit the highest cost for which the waste can be sold for, to various entities and organizations who would employ the waste effectively.…”

Section: Introductionmentioning

confidence: 99%

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GRUBin: Time-Series Forecasting-Based Efficient Garbage Monitoring and Management System for Smart Cities

Mishra

Vajire

Saxena

et al. 2022

Computational Intelligence and Neuroscience

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The waste management of an evolving smart city environment is one of the most important tasks as the living conditions and health of the population depend on proper waste management. Currently deployed systems are failing to monitor the garbage production as they use IoT-based pipelines to monitor the production in a locality, but often the device is used to get destroyed by the frequent use of dustbin. This leads to an increase in expenditure and affects the sustainability of the system. In this work, we propose an efficient and scalable garbage monitoring and collection methodology based on time-series forecasting techniques. The proposed system is also cost-effective because of the iterative deployment of rented IoT sensors, which are used to collect time series format data and then used to train the forecasting module to learn the temporal representation of the data that can produce accurate results for monitoring the fill-up time of the garbage collector. We also propose an efficient collection in-routing technique based on the ranking of bin stations on the basis of temporal and spatial data of the fill-up time and route location to minimize the collection time by making an efficient routing algorithm for garbage collection. This concept of garbage collection will be very useful for smart city planners.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GRUBin: Time-Series Forecasting-Based Efficient Garbage Monitoring and Management System for Smart Cities

Mishra

Vajire

Saxena

et al. 2022

Computational Intelligence and Neuroscience

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“…Connect to the internet 3. Garbage level monitoring Garbage bin network Karnalim et al ( 2020 ); Saranya et al ( 2020 ); Mustafa and Azir ( 2017 ) Ultrasonic sensors Red external sensor 1. Identify garbage 2.…”

Section: Artificial Intelligence In Waste Managementmentioning

confidence: 99%

“…It has an automatic metal and non-metal separator. Using NodeMCU, the bin’s water level can be monitored in real time and sent to the cloud for further analysis and processing (Saranya et al 2020 ).…”

Section: Artificial Intelligence In Waste Managementmentioning

confidence: 99%

Artificial intelligence for waste management in smart cities: a review

Fang

Chen

et al. 2023

Environ Chem Lett

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The rising amount of waste generated worldwide is inducing issues of pollution, waste management, and recycling, calling for new strategies to improve the waste ecosystem, such as the use of artificial intelligence. Here, we review the application of artificial intelligence in waste-to-energy, smart bins, waste-sorting robots, waste generation models, waste monitoring and tracking, plastic pyrolysis, distinguishing fossil and modern materials, logistics, disposal, illegal dumping, resource recovery, smart cities, process efficiency, cost savings, and improving public health. Using artificial intelligence in waste logistics can reduce transportation distance by up to 36.8%, cost savings by up to 13.35%, and time savings by up to 28.22%. Artificial intelligence allows for identifying and sorting waste with an accuracy ranging from 72.8 to 99.95%. Artificial intelligence combined with chemical analysis improves waste pyrolysis, carbon emission estimation, and energy conversion. We also explain how efficiency can be increased and costs can be reduced by artificial intelligence in waste management systems for smart cities.

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“…The bin incorporates the weight sensors to estimate the level of the bin and communicate it to the server wirelessly. An automated metal/non-metal segregation using the RLC metal detector and ultrasonic sensor-based bin filling estimation are presented by K. C. Saranya et al [14]. The user notification is generated via a third-party app from the cloud, therefore, risking the data integrity of the whole system.…”

mentioning

confidence: 99%

Power Optimization and Network Congestion Controlling Technique for an IoT Enabled Smartbin for Smart Cities

et al. 2022

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The rapid increase in population leads to a corresponding increase in waste. As an integral application of a smart city, smart waste management framework provides a viable solution of implementing smartbins to monitor the waste and collect it. However, being a battery-operated node in continuous communicating with the cloud, power efficiency and network congestion are two of the major issues faced by the smartbins. Therefore, an algorithm for power optimization and network congestion control has successfully been presented in this article. The results obtained from testing the algorithm under different filling conditions and zero packet loss provide evidence of its practicality to optimize power usage and circumvent network congestion. The proposed algorithm can easily be implemented as an integral part of a smartbin to allow the cities to support smart waste management frameworks with a minimal failure rate and minimal cost of maintenance. Researchers can integrate the algorithm into their frameworks to propose solutions to the ever-increasing problem of waste management.

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Smart Bin with Automated Metal Segregation and Optimal Distribution of the Bins

Cited by 6 publications

References 11 publications

GRUBin: Time-Series Forecasting-Based Efficient Garbage Monitoring and Management System for Smart Cities

GRUBin: Time-Series Forecasting-Based Efficient Garbage Monitoring and Management System for Smart Cities

Artificial intelligence for waste management in smart cities: a review

Power Optimization and Network Congestion Controlling Technique for an IoT Enabled Smartbin for Smart Cities

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