Results 231 to 240 of about 1,414,604 (271)
Some of the next articles are maybe not open access.
Hybrid User-Based Task Assignment for Mobile Crowdsensing: Problem and Algorithm
IEEE Internet of Things JournalWith the rapid growth of Internet of Things and proliferation of handheld smart devices, mobile crowdsensing has been regarded as an effective sensing paradigm due to its high scalability, low cost, and wide coverage.
Kun Liu +4 more
semanticscholar +1 more source
Online User Recruitment With Adaptive Budget Segmentation in Sparse Mobile Crowdsensing
IEEE Internet of Things JournalSparse mobile crowdsensing (MCS) is a cost-effective data collection paradigm that aims to recruit users to collect data from a part of sensing subareas and infer the rest.
Xianwei Guo +5 more
semanticscholar +1 more source
Lightweight and Privacy-Preserving Dual Incentives for Mobile Crowdsensing
IEEE Transactions on Cloud ComputingIncentive plays an important role in mobile crowdsensing (MCS), as it impels mobile users to participate in sensing tasks and provide high-quality sensing data.
Lin Wan +6 more
semanticscholar +1 more source
Mobile Crowdsensing with Imagery Tasks
IEEE/WIC/ACM International Conference on Web Intelligence, 2021Justas Dautaras, Mihhail Matskin
openaire +1 more source
Quality-Aware Incentive Mechanism for Efficient Federated Learning in Mobile Crowdsensing
IEEE Transactions on Vehicular TechnologyFederated Learning (FL), through which mobile users (MUs) optimize a shared model without revealing the private raw data, has opened up possibilities for mobile crowdsensing (MCS). However, challenges for the MCS system with FL still exist in terms of an
Hui Zhang +5 more
semanticscholar +1 more source
Robust Data Inference and Cost-Effective Cell Selection for Sparse Mobile Crowdsensing
IEEE/ACM Transactions on NetworkingSparse Mobile CrowdSensing (MCS) aims to reduce sensing cost while ensuring high task quality by intelligently selecting small regions for sensing and accurately inferring the remaining areas.
Chengxin Li +5 more
semanticscholar +1 more source
Cooperative Computing for Mobile Crowdsensing: Design and Optimization
IEEE Transactions on Mobile ComputingWith the increasing number of mobile devices, mobile crowdsensing (MCS) has garnered significant attention in research. However, computing infrastructures such as edge/cloud nodes, which are necessary for processing sensor data, are not always readily ...
Xin Xie +4 more
semanticscholar +1 more source
Location Privacy Protection in Mobile Crowdsensing
2018With the increasingly popularity of user-centric mobile sensing and computing devices, e.g., smart phones, in-vehicle sensing devices and wearable devices, our knowledge of the physical world is extended by opening a new door to collect and process data about social events and natural phenomena [1, 2].
Xiaodong Lin, Jianbing Ni, Xuemin Shen
openaire +1 more source
CBDTF: A Distributed and Trustworthy Data Trading Framework for Mobile Crowdsensing
IEEE Transactions on Vehicular TechnologyMobile crowdsensing (MCS) has emerged as a new sensing paradigm that relies on the sensing capabilities of the crowd to aggregate data. Unlike traditional MCS systems, where sensing data are traded via a third-party sensing platform, we propose a ...
Bo Gu +4 more
semanticscholar +1 more source
Συμπεριφορικά μοντέλα στο mobile crowdsensing
2019Η εφαρμογή της θεωρίας λήψης ανθρώπινων αποφάσεων στο πεδίο των εργασιών Mobile Crowdsening (MCS tasks) αποτελεί ένα συγκερασμό των επιστημών της Συμπεριφοράς και της Πληροφορικής. Τα μοντέλα συμπεριφοράς σύμφωνα με τα οποία αποφασίζουν οι άνθρωποι έχουν αποτελέσει αντικείμενο μελέτης της γνωστικής ψυχολογίας, του marketing και των συμπεριφορικών ...
openaire +1 more source