Syed Amir Ali Shah , Ata Ullah , Fazli Subhan , NZ Jhanjhi , Mehedi Masud , Abdulmajeed Alqhatani
{"title":"Truth discovery for mobile workers in edge-assisted mobile crowdsensing","authors":"Syed Amir Ali Shah , Ata Ullah , Fazli Subhan , NZ Jhanjhi , Mehedi Masud , Abdulmajeed Alqhatani","doi":"10.1016/j.icte.2024.06.007","DOIUrl":null,"url":null,"abstract":"<div><div>The proliferation of mobile phones has led to the rise of mobile crowdsensing systems. However, many of these systems rely on the deep cloud, which can be complex and challenging to scale. To improve the performance of crowdsensing at the edge cloud, truth-discovery methods are commonly employed. These methods typically involve updating either the truth or the weight associated with a user’s task. While some edge cloud-based crowdsensing systems exist, they do not provide incentives to users based on their experience. In this report, we present a new approach to truth discovery and incentive-giving that considers both the user’s experience and the accuracy of their submitted data. Our modified truth-discovery algorithm updates both the weight and truth concurrently, with greater incentives offered to users who have completed more tasks and whose submitted data is close to the estimated truth. We have run simulations to show how well our suggested strategy works to enhance the incentive system for experienced users.</div></div>","PeriodicalId":48526,"journal":{"name":"ICT Express","volume":"10 5","pages":"Pages 1087-1093"},"PeriodicalIF":4.1000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ICT Express","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2405959524000778","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
The proliferation of mobile phones has led to the rise of mobile crowdsensing systems. However, many of these systems rely on the deep cloud, which can be complex and challenging to scale. To improve the performance of crowdsensing at the edge cloud, truth-discovery methods are commonly employed. These methods typically involve updating either the truth or the weight associated with a user’s task. While some edge cloud-based crowdsensing systems exist, they do not provide incentives to users based on their experience. In this report, we present a new approach to truth discovery and incentive-giving that considers both the user’s experience and the accuracy of their submitted data. Our modified truth-discovery algorithm updates both the weight and truth concurrently, with greater incentives offered to users who have completed more tasks and whose submitted data is close to the estimated truth. We have run simulations to show how well our suggested strategy works to enhance the incentive system for experienced users.
期刊介绍:
The ICT Express journal published by the Korean Institute of Communications and Information Sciences (KICS) is an international, peer-reviewed research publication covering all aspects of information and communication technology. The journal aims to publish research that helps advance the theoretical and practical understanding of ICT convergence, platform technologies, communication networks, and device technologies. The technology advancement in information and communication technology (ICT) sector enables portable devices to be always connected while supporting high data rate, resulting in the recent popularity of smartphones that have a considerable impact in economic and social development.