Role of Modern Technologies and Internet of things in the field of Solid Waste Management
Keywords:
environment, pandemic period, diseases, technologies, internet of thingsAbstract
The process of handling solid waste becomes complex and tedious due to the urbanization and industrialization of the most developing and developed countries. These solid waste issues if it is not addressed properly it affects ecosystem and environment. There is a possibility of many health-oriented issues especially during the pandemic period covid-19. Most of the human beings are struggling with respiratory pulmonary diseases, asthma caused by these solid wastes. Most of the governments are also spending huge amount of money for labors, devices and some technologies to tackle these solid waste issues. There is also an opportunity for the government to generate revenue from these solid wastes by properly sorting these waste into recyclable, nonrecyclable and bio-degradable wastes. But when humans are involved in sorting these waste it will cause some diseases and hygienic problems. So,in order to address the above said issues in this work the role of modern technologies, algorithms and some Internet of things (IoT) methods are discussed. Implementing these technologies in the future will save huge amount of money spent by the government for the solid waste management activities.
References
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[15] Gulab Singh,; Brajesh Singh,; Shubham Rathi,; Saurabh Haris (2014). Solid Waste Management using Shortest Path Algorithm, International Journal of Engineering Science Invention Research & Development, 1(2), 60-64, 2014.
[16] Gutierrez, J.M.; Jensen, M.; Henius, M.;, Riaz, T. (2015). Smart waste collection system based on location intelligence, Procedia Computer Science, 61, 120-127, 2015. https://doi.org/10.1016/j.procs.2015.09.170
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[24] Jenilasree, G.; Shenbagavadivu, N.; Bhuvaneswari, M. (2018). A Study on Automatic Solid Waste Management System for Smart City, International Journal of Creative Research Thoughts, 5(4), 1682-1689, 2018.
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[26] Krizhevsky, A.; Sutskever, I.; Hinton, G.E. (2012). ImageNet classification with deep convolutional neural networks, Neural Information Processing System Conference, 1097-1105, 2012.
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[28] Kusum Lata,; Singh, S.K. (2016). IOT based smart waste management system using wireless sensor network and embedded linux board, International Journal of Current Trends in Engineering & Research, 2(7), 210-214, 2016.
[29] López, J.G.; Imine, M.; Rumín, R.C.; .Pedersen, J.M.; Madsen, O.B. (2008). Multilevel network characterization using regular topologies, Computational Network, 173-187, 2008.
[30] Lu, J.W.; Chang, N.B.; Liao, L.; Liao, M.Y. (2017). Smart and Green Urban Solid Waste Collection Systems: Advances, Challenges, and Perspectives, Technical journal of the IEEE Systems, 11, 2804-2817, 2017. https://doi.org/10.1109/JSYST.2015.2469544
[31] LuHuida,; FanBingchen,; YuanLiheng,; LiYanan (2012). RFID-Based Hazardous Waste Management Platform Establishment, Technical journal of the IEEE Systems, 11, 2804-2817, 2017.
[32] Mahmuda Akhtara,; Hannan, M.A; Begum Hassan Basri, R.A. (2017). Backtracking search algorithm in CVRP models for efficient solid waste collection and route optimization, Procedia Engineering, 29, 4-8, 2012.
[33] Manqele, L.; Adeogun, R.; Dlodlo, M.; Coetzee, L. (2017). Multi-objective decision- making framework for effective waste collection in smart cities, Global Wireless Summit (GWS), 155-159, 2017. https://doi.org/10.1109/GWS.2017.8300475
[34] Marandi, F.; Ghomi, S. (2016). Time series forecasting and analysis of municipal solid waste generation in Tehran city, 12th International Conference on Industrial Engineering (ICIE), 14- 18, 2016. https://doi.org/10.1109/INDUSENG.2016.7519343
[35] Maria Grazia Gnoni,; Gianni Lettera,; Alessandra Rollo (2013). A feasibility study of a RFID traceability system in municipal solid waste management, International Journal of Information Technology and Management, 12, 27-38, 2013. https://doi.org/10.1504/IJITM.2013.051632
[36] Maurizio Giacobbe,; Carlo Puliafito,; Marco Scarpa (2016). The Big Bucket: An IoT Cloud Solution for Smart Waste Management in Smart Cities, European Conference on Service-Oriented and Cloud Computing, ESOCC Advances in Service-Oriented and Cloud Computing, 43- 58, 2016. https://doi.org/10.1007/978-3-319-72125-5_4
[37] Maryam Abbasi,; Ali El Hanandeh (2016). Forecasting municipal solid waste generation using artificial intelligence modelling approaches, Waste Management, 56, 13-22, 2016. https://doi.org/10.1016/j.wasman.2016.05.018
[38] Mahmudul Hasan Russel,; Mehdi Hasan Chowdhury,; Md. Shekh Naim Uddin,; Ashif Newaz (2013). Development of Automatic Smart Waste Sorter Machine, International Conference on Mechanical, Industrial and Materials Engineering, 1-7, 2013.
[39] Mitton, N.; Papavassiliou, S.; Puliafito, A.; Trivedi, K.S. (2012). Combining Cloud and sensors in a smart city environment, Journal on Wireless Communications and Networking, 2012(247), 1-10, 2012. https://doi.org/10.1186/1687-1499-2012-247
[40] Mohammad Ali Abdoli,; Maliheh Falah Nezhad,; Reza Salehi Sede,; Sadegh Behboudian. (2011). Long term forecasting of solid waste generation by the artificial neural networks, Environmental Progress & Sustainable Energy, 31(4), 628-636, 2011. https://doi.org/10.1002/ep.10591
[41] Omara, A.; Gulen, D.; Kantarci, B.; Oktug,S. (2018). Trajectory Assisted Municipal Agent Mobility A Sensor-Driven Smart Waste Management System, Journal of Sensors and Actuator Networks, 7, 29-37, 2018. https://doi.org/10.3390/jsan7030029
[42] [Online]. Available: http://data.kk.dk, Open Data Copenhagen.
[43] Parkash, P.V. (2016). IoT Based Waste Management for Smart City, International Journal of Innovation Research, Computational Communication Engineering, 7, 29-37, 2016.
[44] Petit, J. (2001). Experiments on the minimum linear arrangement problem, Journal of Experimental Algorithmics, 1-33, 2001.
[45] Praveen Kumar Gupta,; Vidhya Shree,; Lingayya Hiremath,; Sindhu Rajendran (2019). The Use of Modern Technology in Smart Waste Management and Recycling: Artificial Intelligence and Machine Learning, Studies in Computational Intelligence, 1, 173-188, 2019. https://doi.org/10.1007/978-3-030-12500-4_11
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