Lindelweyizizwe Manqele, Ramoni O. Adeogun, M. Dlodlo, L. Coetzee
{"title":"Multi-objective decision-making framework for effective waste collection in smart cities","authors":"Lindelweyizizwe Manqele, Ramoni O. Adeogun, M. Dlodlo, L. Coetzee","doi":"10.1109/GWS.2017.8300475","DOIUrl":null,"url":null,"abstract":"There are metropolitan areas in smart cities that are experiencing waste collection challenges through ineffective methods of waste collection in resource constrained environments. This paper identified an opportunity to investigate efficient decision-making ways that will make use of data generated by IoT-enabled objects, taking into account the multi-objective goals in a smart city through addressing data loss challenge. Having the list of decision-making algorithms is one thing but choosing which algorithm to use requires intelligence. There is a need for decision-making algorithms that will be sufficiently dynamic to address different levels of data loss inherent in IoT data collection. This paper presents the framework that will enhance the smarter decisions in the smart city.","PeriodicalId":380950,"journal":{"name":"2017 Global Wireless Summit (GWS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Global Wireless Summit (GWS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GWS.2017.8300475","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
There are metropolitan areas in smart cities that are experiencing waste collection challenges through ineffective methods of waste collection in resource constrained environments. This paper identified an opportunity to investigate efficient decision-making ways that will make use of data generated by IoT-enabled objects, taking into account the multi-objective goals in a smart city through addressing data loss challenge. Having the list of decision-making algorithms is one thing but choosing which algorithm to use requires intelligence. There is a need for decision-making algorithms that will be sufficiently dynamic to address different levels of data loss inherent in IoT data collection. This paper presents the framework that will enhance the smarter decisions in the smart city.