Smart City Gnosys
Smart city article details
| Title | Bin Bay: An Optimized Smart Waste Disposal System For A Sustainable Urban Life Using Lstm And Fog Computing |
|---|---|
| ID_Doc | 12224 |
| Authors | Duela S.; Sara D.; Prabavathi |
| Year | 2022 |
| Published | Journal of Computer Science, 18, 11 |
| DOI | http://dx.doi.org/10.3844/jcssp.2022.1110.1120 |
| Abstract | To create green and smart cities, a smart and planned Waste Management System (WMS) is a vital requirement. With the increase in the urban population and service delivery difficulties, smart cities are integrating technologies such as smart waste bins and Radio Frequency Identification (RFID) tags into their day-to-day operations to make solid waste management and control more effective. Fog computing in waste management is becoming common in smart utilities as it provides benefits like a minimal amount of data sent to the cloud, reduced bandwidth, and Low data latency. The proposed work focuses on residential customers using waste collection services. Smart Waste Disposal System for a sustainable urban life using Fog Computing called Bin Bay is proposed to optimize the collection of waste with the shortest path using Google Map API support from Fog Layer for which four major parameters are considered namely: The distance between truck and bin, the status of the bin (what percentage of it is filled), the capacity of the truck and the position of the truck. The Bin Bay model helps to save time by determining the most efficient waste pickup route for waste disposal. The RFID Reader and Scanner help in updating the fog nodes with the date, time, and volume of trash pickup which could be further analyzed to keep on-time service delivery. Also, predictions on future waste growth shall be determined by doing in-depth analysis in the cloud using LSTM which will intelligently learn and predict the waste patterns based on the summary submitted by the aggregate node of the fog layer and since the optimized routes lead to less fuel consumption, CO2 emission also shall be greatly reduced © 2022 Shiny Duela, Dioline Sara and Prabavathi. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license |
| Author Keywords | CO<sub>2</sub> Emission; Fog Computing; Internet of Things; Sensors; Supervised Learning Algorithm; Waste Management |
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