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

被引:50
作者
Hussain, Ayaz [1 ]
Draz, Umar [2 ,3 ]
Ali, Tariq [4 ]
Tariq, Saman [1 ]
Irfan, Muhammad [4 ]
Glowacz, Adam [5 ]
Daviu, Jose Alfonso Antonino [6 ]
Yasin, Sana [7 ,8 ]
Rahman, Saifur [4 ]
机构
[1] Univ Management & Technol Sialkot, Dept Comp Sci, Sialkot 51310, Pakistan
[2] Univ Sahiwal, Dept Comp Sci, Sahiwal 57000, Pakistan
[3] COMSATS Univ Islamabad, CS Dept, Lahore Campus, Lahore 54000, Pakistan
[4] Najran Univ, Coll Engn, Elect Engn Dept, Najran 61441, Saudi Arabia
[5] AGH Univ Sci & Technol, Fac Elect Engn Automat Comp Sci & Biomed Engn, Dept Automat Control & Robot, Al A Mickiewicza 30, PL-30059 Krakow, Poland
[6] Univ Politecn Valencia, Dept Elect Engn, Inst Tecnol Energia, Camino Vera S-N, Valencia 46022, Spain
[7] Super Univ, Dept Comp Sci & Informat Technol, Gold Campus, Lahore 54000, Pakistan
[8] COMSATS Univ Islamabad, Comp Sci Dept, Lahore Campus, Lahore 54000, Pakistan
关键词
Internet of Things; air monitoring; forecasting; air pollutant; smart bin; machine learning;
D O I
10.3390/en13153930
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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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页数:22
相关论文
共 45 条
  • [1] Smart Homes that Monitor Breathing and Heart Rate
    Adib, Fadel
    Mao, Hongzi
    Kabelac, Zachary
    Katabi, Dina
    Miller, Robert C.
    [J]. CHI 2015: PROCEEDINGS OF THE 33RD ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, 2015, : 837 - 846
  • [2] IoT-Based Smart Waste Bin Monitoring and Municipal Solid Waste Management System for Smart Cities
    Ali, Tariq
    Irfan, Muhammad
    Alwadie, Abdullah Saeed
    Glowacz, Adam
    [J]. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2020, 45 (12) : 10185 - 10198
  • [3] Participants Ranking Algorithm for Crowdsensing in Mobile Communication
    Ali, Tariq
    Noureen, Javeria
    Draz, Umar
    Shaf, Ahmad
    Yasin, Sana
    Ayaz, Muhammad
    [J]. EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS, 2018, 4 (16): : 1 - 5
  • [4] Ali T, 2018, INT J ADV COMPUT SC, V9, P399
  • [5] Anagnostopoulos T.V., 2014, P INT C NEXT GEN WIR
  • [6] Anitha A., 2017, IOP Conference Series: Materials Science and Engineering, V263, DOI 10.1088/1757-899X/263/4/042027
  • [7] [Anonymous], 2012, World Bank Annual Report
  • [8] Bharadwaj AS, 2016, ANNU IEEE IND CONF
  • [9] Bharadwaj B., 2017, P 2017 2 INT C COMP
  • [10] Chakma A., 2017, P 2017 IEEE INT C IM