Results 211 to 220 of about 12,378 (273)

Jump-start crowdsensing: A three-layer incentive framework for mobile crowdsensing

2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS), 2017
In the past decade, with the rapid development of wireless communication and sensor technology, ubiquitous smartphones equipped with increasingly rich sensors have more powerful computing and sensing abilities. Thus, mobile crowdsensing has received extensive attentions from both industry and academia.
null Yatong Chen, null Huangxun Chen, null Shuo Yang, null Xiaofeng Gao, null Fan Wu   +4 more
openaire   +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

Incentive mechanisms for crowdsensing

2018 IEEE Long Island Systems, Applications and Technology Conference (LISAT), 2018
Crowd sensing is a mechanism that facilitates the company to accomplish by depurating the people. It provides the temporary and voluntary service supporter. However, crowdsensing experiences the problem due to user selection and payment determination. Thus problems deteriorate the incompleteness of task at present.
Razque, Abdul, Amsaad, Fathi, Jiazhi, Li, Huayue, Gu, Hassan, Abdalraouf   +4 more
openaire   +2 more sources

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

Federated Learning Meets Urban Opportunistic Crowdsensing in 6G Networks: Opportunities, Challenges, and Optimization Potentials

IEEE Network
6G networks are envisioned to enable the Internet of Things (IoTs) and foster ubiquitous sensing. Urban opportunistic crowdsensing, which leverages participants carrying mobile sensing units (MSUs) in their daily activities to collect data, enables low ...
Wenjun Zhang, Xiaoli Liu, Chao Zhu, Samu Varjonen, Fangxin Wang, Sasu Tarkoma   +5 more
semanticscholar   +1 more source

Density-aware compressive crowdsensing

Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks, 2017
Crowdsensing systems collect large-scale sensor data from mobile devices to provide a wide-area view of phenomena including traffic, noise and air pollution. Because such data often exhibits sparse structure, it is natural to apply compressive sensing (CS) for data sampling and recovery.
Xiaohong Hao, Liwen Xu, Nicholas D. Lane, Xin Liu, Thomas Moscibroda   +4 more
openaire   +1 more source

Privacy-Preserving and Reliable Truth Discovery for Heterogeneous Fog-Based Crowdsensing

IEEE Transactions on Dependable and Secure Computing
Truth discovery is an effective technique for resolving data conflicts in crowdsensing. Fog-based mobile crowdsensing utilizes low-latency and high-efficiency communications capabilities of fog computing to achieve large-scale data sensing at a low cost.
Yihuai Liang, Yan Li, Byeong-Seok Shin
semanticscholar   +1 more source

Secure mobile crowdsensing game

2015 IEEE International Conference on Communications (ICC), 2015
By recruiting sensor-equipped smartphone users to report sensing data, mobile crowdsensing (MCS) provides location-based services such as environmental monitoring. However, due to the distributed and potentially selfish nature of smartphone users, mobile crowdsensing applications are vulnerable to faked sensing attacks by users who bid a low price in ...
Liang Xiao, Jinliang Liu, Qiangda Li, H. Vincent Poor   +3 more
openaire   +1 more source

Location Privacy Preservation Crowdsensing With Federated Reinforcement Learning

IEEE Transactions on Dependable and Secure Computing
Crowdsensing has become a popular method of sensing data collection while facing the problem of protecting participants’ location privacy. Existing location-privacy crowdsensing mechanisms focus on static tasks and participants without considering ...
Zhichao You, Xuewen Dong, Ximeng Liu, Sheng Gao, Yongzhi Wang, Yulong Shen   +5 more
semanticscholar   +1 more source

Joint Sensing and Computation Incentive Mechanism for Mobile Crowdsensing Networks: A Multiagent Reinforcement Learning Approach

IEEE Internet of Things Journal
Mobile crowdsensing (MCS) is a novel sensing paradigm by utilizing mobile users (MUs) to collect data from environment. Considering the finite sensing and computing resources of MUs, it is crucial to inspire MUs to take part in crowdsensing willingly. In
Nan Zhao, Yiling Sun, Yiyang Pei, Dusist Niyato   +3 more
semanticscholar   +1 more source