Results 141 to 150 of about 5,365 (199)

A secure and efficient user selection scheme in vehicular crowdsensing. [PDF]

Sci Rep
Zhang M, Ye Q, Yuan Z, Deng K.
europepmc   +1 more source

RCoD: Reputation-Based Context-Aware Data Fusion for Mobile IoT. [PDF]

Sensors (Basel)
Tasnim S, Pissinou N, Iyengar SS, Boroojeni KG, Ahmed K.   +4 more
europepmc   +1 more source

Examining spatiotemporal crowdsensing and caching for population-dynamic OTT content delivery. [PDF]

Sci Rep
Kim HS, Jang Y, Choi YJ, Kim HK, Kim S, Lee SH.   +5 more
europepmc   +1 more source

Geo-temporal vehicular environmental sensing dataset. [PDF]

Data Brief
Colarusso C, Consales M, Dalia G, Falco I, Goglia L, Mazzitelli FC, Perugini PA, Zimeo E.   +7 more
europepmc   +1 more source

An Adaptive Temporal Convolutional Network Autoencoder for Malicious Data Detection in Mobile Crowd Sensing. [PDF]

Sensors (Basel)
Owoh N, Riley J, Ashawa M, Hosseinzadeh S, Philip A, Osamor J.   +5 more
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AI and Data-Driven Advancements in Industry 4.0. [PDF]

Sensors (Basel)
Pang Y, Huang T, Wang Q.
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Optimizing Collaborative Crowdsensing: A Graph Theoretical Approach to Team Recruitment and Fair Incentive Distribution. [PDF]

Sensors (Basel)
Liu H, Zhang C, Chen X, Tai W.
europepmc   +1 more source

Task Assignment and Path Planning Mechanism Based on Grade-Matching Degree and Task Similarity in Participatory Crowdsensing. [PDF]

Sensors (Basel)
He X, Wang Y, Zhao X, Huang T, Yu Y.
europepmc   +1 more source

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On the Challenges of Mobile Crowdsensing for Traffic Estimation

Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems, 2017
Traffic congestion adversely impacts our lives. Traffic estimation resorting to mobile (crowdsensing) probes is a challenging task. We present key challenges for accurate and real-time traffic estimation resorting to crowdsensing data, namely data sparsity, user trip diversity, population bias, data quality, among others.
Daniela Socas Gil, Pedro M. d'Orey, Ana Aguiar   +2 more
openaire   +1 more source