Results 201 to 210 of about 1,414,604 (271)

An Ordered Submodularity-Based Budget-Feasible Mechanism for Opportunistic Mobile Crowdsensing Task Allocation and Pricing

IEEE Transactions on Mobile Computing
Mobile crowdsensing services are divided into two categories: opportunistic and participatory. In opportunistic mobile crowdsensing services, users do not need to specify the crowdsensing tasks to be completed.
Jixian Zhang, Yan Zhang, Hao Wu, Weidong Li   +3 more
semanticscholar   +1 more source

Privacy-Preserving Truth Discovery Based on Secure Multi-Party Computation in Vehicle-Based Mobile Crowdsensing

IEEE transactions on intelligent transportation systems (Print)
Vehicle-based mobile crowdsensing has gained widespread attention due to its low cost and efficient data collection mode. One common method to improve the accuracy of sensing data in this context is truth discovery.
Tao Peng, Wentao Zhong, Guojun Wang, Entao Luo, Shui Yu, Yining Liu, Yi Yang, Xuyun Zhang   +7 more
semanticscholar   +1 more source

BlockSense: Towards Trustworthy Mobile Crowdsensing via Proof-of-Data Blockchain

IEEE Transactions on Mobile Computing
Mobile crowdsensing (MCS) can promote data acquisition and sharing among mobile devices. Traditional MCS platforms are based on a triangular structure consisting of three roles: data requester, worker (i.e., sensory data provider) and MCS platform ...
Junqin Huang, L. Kong, Long Cheng, Hongning Dai, M. Qiu, Guihai Chen, Xuemei Liu, Gang Huang   +7 more
semanticscholar   +1 more source

Investigating mobile crowdsensing application performance

Proceedings of the third ACM international symposium on Design and analysis of intelligent vehicular networks and applications, 2013
Mobile Crowdsensing (MCS) is an emerging distributed paradigm lying at the intersection between the Internet of Things and the volunteer/crowd-based approach. MCS applications are usually deployed on contributing nodes such as smart devices and mobiles, equipped by sensing resources that sample the physical environment and provide the sensed data, once
DISTEFANO, SALVATORE, LONGO, FRANCESCO, SCARPA, Marco Lucio   +2 more
openaire   +1 more source

Preliminary Study on Dynamic Updates of Spatial Digital Twins via 3D Mobile Crowdsensing

IEEE International Conference on Consumer Electronics
A city-scale spatial digital twin is an emerging technology for creating smart cities by replicating urban spaces in the virtual environment. However, building such digital twins requires vast amounts of spatial data to accurately represent the physical ...
Kenta Hasegawa, Tatsuya Kase, Kaito Watanabe, Takumi Miyoshi, Taku Yamazaki   +4 more
semanticscholar   +1 more source

MWRS: A MAB-Based Worker Recruitment Scheme With Tripartite Stackelberg Game for Reliable Mobile Crowdsensing

IEEE Transactions on Mobile Computing
Mobile Crowdsensing (MCS) has emerged as a compelling paradigm for data sensing and collection, leveraging the widespread adoption of mobile devices and the active participation of numerous users.
Ouyang Yan, Feng Zeng, Neal N. Xiong, Anfeng Liu, W. Pedrycz   +4 more
semanticscholar   +1 more source

Unknown Worker Recruitment With Long-Term Incentive in Mobile Crowdsensing

IEEE Transactions on Mobile Computing
Many mobile crowdsensing applications require efficient recruitment of workers whose qualities are often unknown a priori. While prior research has explored multi-armed bandit-based mechanisms with short-term incentives to address this unknown worker ...
Qihang Zhou, Xinglin Zhang, Zheng Yang
semanticscholar   +1 more source

Ensuring Threshold AoI for UAV-Assisted Mobile Crowdsensing by Multi-Agent Deep Reinforcement Learning With Transformer

IEEE/ACM Transactions on Networking
Unmanned aerial vehicle (UAV) crowdsensing (UCS) is an emerging data collection paradigm to provide reliable and high quality urban sensing services, with age-of-information (AoI) requirement to measure data freshness in real-time applications.
Hao Wang, C. Liu, Haoming Yang, Guoren Wang, K. Leung   +4 more
semanticscholar   +1 more source

Sparse Mobile Crowdsensing for Cost-Effective Traffic State Estimation With Spatio–Temporal Transformer Graph Neural Network

IEEE Internet of Things Journal
Recently, mobile crowdsensing (MCS) has emerged as a promising solution for traffic state estimation (TSE), which provides real-time and accurate traffic information for supporting diversified intelligent transportation systems (ITSs) applications ...
Jianzhe Xue, Yunting Xu, Wen-Chao Wu, Tianqi Zhang, Qinghong Shen, Haibo Zhou, Weihua Zhuang   +6 more
semanticscholar   +1 more source

Spatiotemporal-Aware Privacy-Preserving Task Matching in Mobile Crowdsensing

IEEE Internet of Things Journal
Task matching is widely used for participant selection in mobile crowdsensing (MCS). However, accurate task matching relies on collecting a large amount of user information, which has the risk of privacy leakage. Existing privacy-preserving task matching
Tao Peng, Wentao Zhong, Guojun Wang, Shaobo Zhang, Entao Luo, Tian Wang   +5 more
semanticscholar   +1 more source