Results 231 to 240 of about 12,378 (273)

Footstone of Metaverse: A Timely and Secure Crowdsensing

IEEE Network
Recently, the metaverse has been a hot research topic that fuses multiple cutting-edge techniques, including virtual reality (VR), augmented reality (AR), artificial intelligence (AI), Internet of Things (IoT), and 5th generation (5G) networks. Supported
Weizheng Wang, Yaoqi Yang, Zehui Xiong, Dusist Niyato   +3 more
semanticscholar   +1 more source

Research Progress on Incentive Mechanisms in Mobile Crowdsensing

IEEE Internet of Things Journal
With the continuous improvement of the sensing, transmission, storage, and computing capabilities of mobile devices, they have become important tools for perceiving the physical environment and social phenomena.
Enhui Wu, Zhenlong Peng
semanticscholar   +1 more source

Fog-Enabled Privacy-Preserving Multi-Task Data Aggregation for Mobile Crowdsensing

IEEE Transactions on Dependable and Secure Computing
Privacy-preserving data aggregation in mobile crowdsensing (MCS) focuses on mining information from massive sensing data while protecting users’ privacy.
Xingfu Yan, Wing W. Y. Ng, Bowen Zhao, Yuxian Liu, Ying Gao, Xiumin Wang   +5 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

Online Incentive Mechanisms for Socially-Aware and Socially-Unaware Mobile Crowdsensing

IEEE Transactions on Mobile Computing
Mobile crowdsensing (MCS) has been a promising paradigm for gathering sensing data from surrounding environment by leveraging smart devices carried by mobile users and also their subjective initiatives.
Guo Ji, Baoxian Zhang, Guo Zhang, Cheng Li   +3 more
semanticscholar   +1 more source

QoI-Aware Mobile Crowdsensing for Metaverse by Multi-Agent Deep Reinforcement Learning

IEEE Journal on Selected Areas in Communications
Metaverse is expected to provide mobile users with emerging applications both in regular situation like intelligent transportation services and in emergencies like wireless search and disaster response.
Yuxiao Ye, Hao Wang, Chi Harold Liu, Zipeng Dai, Guozheng Li, Guoren Wang, Jian Tang   +6 more
semanticscholar   +1 more source

Crowdsensing smart city parking monitoring

2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), 2015
Free parking search is one of the citizen's daily tasks which influences the most in the overall city performance. Pollution, traffic and productivity are deteriorated by cars looking for a place to park. In this paper, we propose the use of one of the features present in today's mobile phones, the magnetometer, to get a real-time map about free spaces
Felix Jesus Villanueva, David Villa, Maria Jose Santofimia, Jesus Barba, Juan Carlos Lopez   +4 more
openaire   +1 more source

Multitask Data Collection With Limited Budget in Edge-Assisted Mobile Crowdsensing

IEEE Internet of Things Journal
Due to the swift advancement of edge computing and mobile crowdsensing (MCS), edge-assisted MCS (EAMCS) has emerged as a promising paradigm, leveraging sensor-embedded mobile devices for the collection and sharing of environmental data.
Xiaolong Liu, Honglong Chen, Yuping Liu, Wentao Wei, Huansheng Xue, Feng Xia   +5 more
semanticscholar   +1 more source

When Mobile Crowdsensing Meets Privacy

IEEE Communications Magazine, 2019
Mobile crowdsensing (MCS) has now become an effective paradigm to collect massive data for various sensing applications. However, the interactions between mobile users and the platform, and the data release to third parties, pose severe challenges of privacy leakage for MCS systems, such as the leakage of users' identities and locations.
Zhibo Wang, Xiaoyi Pang, Jiahui Hu, Wenxin Liu, Qian Wang, Yanjun Li, Honglong Chen   +6 more
openaire   +1 more source

Smart Parking by Mobile Crowdsensing

International Journal of Smart Home, 2016
An increasing number of mobile applications aim to realize “smart cities” by utilizing contributions from citizens armed with mobile devices like smartphones. However, there are few generally recognized guidelines for developing and deploying crowdsourcingbased solutions in mobile environments.
Xiao Chen, Nianzu Liu
openaire   +1 more source