Smart City Gnosys

Smart city article details

Title	Lorawan-Aided Waste-To-Energy Concept Model In Smart Cities
ID_Doc	35650
Authors	Ak E.; Kaya K.; Yaslan Y.; Oktug S.F.
Year	2021
Published	Proceedings of the International Conference on Computer, Information, and Telecommunication Systems, CITS 2021
DOI	http://dx.doi.org/10.1109/CITS52676.2021.9618578
Abstract	With the use of sensor networks and machine learning (ML) techniques in data analysis, the impact of the works for smart cities is getting greater. As a sub-field o f s mart cities, waste management, and related waste transformations study and plan waste collection, disposal, and recycling. Especially, waste to energy transformation composes the major part of waste disposal. Predicting the energy to be obtained from waste and planning the energy supply accordingly depend on estimating the amount of waste and knowing its content. However, energy prediction from solid waste suffers from weak forecasting models, which lead to misguided management strategies in smart cities. Internet of Things (IoT) technologies like low-power, wide-area networking protocol (LoRaWAN) offer new opportunities to collect, monitor, and analyze data in smart cities, including waste management. In this study, we propose the LoRaWAN-aided Waste-to-Energy Concept Model to build the waste-to-energy prediction model with the provided smart city use case using LoRa network as an underlying data collection step. Consequently, we benefit from the pre-trained Gradient Boosting Regression (GBR) model whose process details are provided in our previous study, to predict municipal solid waste using daily data along with other relevant variables such as temperature.
Author Keywords	MSW; Smart City; Waste Management; Waste-To-Energy

Similar Articles

Id	Similarity	Authors	Title	Published
34053	0.903	Sanjay V.; Khamparia A.; Gupta D.; Kumar A.; Yang T.; Rathore R.S.	Iot-Driven Waste Management In Smart Cities: Real-Time Monitoring And Optimization	Lecture Notes in Networks and Systems, 1293 (2025)
52262	0.897	Namoun A.; Tufail A.; Khan M.Y.; Alrehaili A.; Syed T.A.; BenRhouma O.	Solid Waste Generation And Disposal Using Machine Learning Approaches: A Survey Of Solutions And Challenges	Sustainability (Switzerland), 14, 20 (2022)
61985	0.894	Belsare K.; Singh M.; Gandam A.; Samudrala V.; Singh R.; F. Soliman N.; Das S.; D. Algarni A.	Wireless Sensor Network-Based Machine Learning Framework For Smart Cities In Intelligent Waste Management	Heliyon, 10, 16 (2024)
6690	0.894	Shah K.B.; Visalakshi S.; Guragain D.P.; Panigrahi R.	Advancing Smart City Sustainability With Internet Of Things And Artificial Intelligence Aided Low-Cost Digital Twin Systems For Waste Management	Microsystem Technologies (2024)
15550	0.894	Yadav S.; Nithya P.; Manikumar T.; Suganthi D.; Vijayaragavan P.; Chandra Sekhar S.	Conceptualizing Core Aspects In Circular Economy With Waste Recycling In Smart Cities Based On Lstm And Auto Encoder Approach	7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023 - Proceedings (2023)
61448	0.892	Addas A.; Khan M.N.; Naseer F.	Waste Management 2.0 Leveraging Internet Of Things For An Efficient And Eco-Friendly Smart City Solution	PLoS ONE, 19, 7 July (2024)
35641	0.888	De Almeida L.G.; De Campos M.G.; Borin J.F.	Lorawan Infrastructure For Urban Waste Management: A Simulation Study	2023 IEEE World Forum on Internet of Things: The Blue Planet: A Marriage of Sea and Space, WF-IoT 2023 (2023)
51237	0.888	Helmy M.; Hassan M.S.	Smart Monitoring Of Waste Bins And Municipal Solid Waste Forecast Using Lstm For Smart Cities	AIP Conference Proceedings, 3034, 1 (2024)
35903	0.887	Chen X.	Machine Learning Approach For A Circular Economy With Waste Recycling In Smart Cities	Energy Reports, 8 (2022)
33604	0.886	Karri M.; Priya C.K.G.; Raha S.; Kumar P.; Gajbhare B.P.; Bhatnagar V.	Iot And Lstm-Mlp Integration For Efficient Waste-To-Energy Power Generation In Smart Cities	IEMECON 2024 - 12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks (2024)