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dc.contributor.authorMasroor, Hasan
dc.date.accessioned2020-10-17T15:42:26Z
dc.date.available2020-10-17T15:42:26Z
dc.date.issued2020-10-17
dc.identifier.urihttp://dspace.uiu.ac.bd/handle/52243/1895
dc.description.abstractIn order to have a secure, comfortable and healthy life, smart cities are regarded as the future of human habitation. Within the concept of Internet of Things (IoT), the development of sensors and actuators empowers a city to serve its citizens in an intelligent, efficient and automated way. Waste management is one of those services that smart cities should provide. Rapid population growth as well as high urbanization rate and high consumption of resources contribute to the generation of large quantities of waste in a city, which is almost an uncontrollable problem faced by the municipal authorities of Asia's developing countries. Inadequate resources, inadequate financial strength and inadequate manpower take it beyond their ability to adequately handle the waste. Among all the waste management processes, waste collection is a significant process. The major part of the total budget selected for the waste management is used for waste collection only. Besides, long time travelling of the waste collecting trucks in the roads may increase fuel cost, human labor cost, CO2 emissions etc. An effective solution to reduce these costs is an IoT enabled efficient waste collection system. By implementing IoT, the current fill level of waste bins can be detected. Therefore, trucks only visit those bins which are full or almost full thus reducing the length of the waste collection route as well as waste collection cost. In this study, a smart bin is developed to display the process of detecting the current fill level of waste bins. Moreover, to optimize the waste collection routes, different route optimization algorithms such as Dijkstra‟s algorithm, Bellman-Ford algorithm and Genetic algorithm are experimented on the real life waste container locations of Mirpur, Dhaka. The performances of these algorithms are compared and the result shows that, Genetic algorithm performed better than the other route optimization algorithms and generated more optimized waste collection routes in the case of the real life waste container locations of Mirpur, Dhaka.en_US
dc.language.isoenen_US
dc.publisherUIUen_US
dc.subjectIoT, Waste management, Graph Theory, Routingen_US
dc.titleIoT Enabled Efficient Waste Collection Systemen_US
dc.typeOtheren_US


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