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A Deep Learning-Based Mobile Crowdsensing Scheme by Predicting Vehicle Mobility

https://doi.org/10.1109/tits.2020.3023446
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Trust based energy efficient data collection with unmanned aerial vehicle in edge network
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Comprehensive tempo-spatial data collection in crowd sensing using a heterogeneous sensing vehicle selection method
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A trust-based minimum cost and quality aware data collection scheme in P2P network
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Opportunistic communication in smart city: Experimental insight with small-scale taxi fleets as data carriers
inferredcites →https://doi.org/10.1016/j.future.2018.06.021openalex-snapshot
A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial–temporal data features
inferredcites →https://doi.org/10.1016/j.ins.2020.01.043openalex-snapshot
Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting
inferredcites →https://doi.org/10.1109/icc.2017.7996756openalex-snapshot
Performance evaluation of movement prediction techniques for vehicular networks
inferredcites →https://doi.org/10.1109/iccse.2016.7581655openalex-snapshot
Data gathering in urban vehicular network based on daily movement patterns
inferredcites →https://doi.org/10.1109/infocom.2015.7218612openalex-snapshot
User recruitment for mobile crowdsensing over opportunistic networks
inferredcites →https://doi.org/10.1109/infocom.2015.7218644openalex-snapshot
High quality participant recruitment in vehicle-based crowdsourcing using predictable mobility
inferredcites →https://doi.org/10.1109/iotm.001.1900013openalex-snapshot
Leveraging Crowdsourcing and Crowdsensing Data for HADR Operations in a Smart City Environment
inferredcites →https://doi.org/10.1109/iscc.2015.7405541openalex-snapshot
On the performance of localization prediction methods for vehicular Ad Hoc Networks
inferredcites →https://doi.org/10.1109/jiot.2018.2864341openalex-snapshot
Task Allocation in Mobile Crowd Sensing: State-of-the-Art and Future Opportunities
inferredcites →https://doi.org/10.1109/jiot.2019.2904704openalex-snapshot
Improving IoT Data Quality in Mobile Crowd Sensing: A Cross Validation Approach
inferredcites →https://doi.org/10.1109/jiot.2019.2951857openalex-snapshot
A Novel Load Balancing and Low Response Delay Framework for Edge-Cloud Network Based on SDN
inferredcites →https://doi.org/10.1109/mass.2019.00017openalex-snapshot
Cost-Efficient Worker Trajectory Planning Optimization in Spatial Crowdsourcing Platforms
inferredcites →https://doi.org/10.1109/percom.2012.6199855openalex-snapshot
A location-based incentive mechanism for participatory sensing systems with budget constraints
inferredcites →https://doi.org/10.1109/tbdata.2018.2865755openalex-snapshot
Cost-Efficient Heterogeneous Worker Recruitment under Coverage Requirement in Spatial Crowdsourcing
inferredcites →https://doi.org/10.1109/tifs.2019.2903659openalex-snapshot
Trust Evaluation Mechanism for User Recruitment in Mobile Crowd-Sensing in the Internet of Things
inferredcites →https://doi.org/10.1109/tii.2018.2810291openalex-snapshot
Notice of Retraction: Intelligent Transportation System in Macao Based on Deep Self-Coding Learning
inferredcites →https://doi.org/10.1109/tii.2018.2877217openalex-snapshot
Distributed Real-Time IoT for Autonomous Vehicles
inferredcites →https://doi.org/10.1109/tii.2019.2936507openalex-snapshot
DeepVM: RNN-Based Vehicle Mobility Prediction to Support Intelligent Vehicle Applications
inferredcites →https://doi.org/10.1109/tits.2014.2345663openalex-snapshot
Traffic Flow Prediction With Big Data: A Deep Learning Approach
inferredcites →https://doi.org/10.1109/tits.2017.2727053openalex-snapshot
Reputation-Aware, Trajectory-Based Recruitment of Smart Vehicles for Public Sensing
inferredcites →https://doi.org/10.1109/tits.2017.2749459openalex-snapshot
A Survey of the Connected Vehicle Landscape—Architectures, Enabling Technologies, Applications, and Development Areas
inferredcites →https://doi.org/10.1109/tits.2019.2900481openalex-snapshot
Deep Irregular Convolutional Residual LSTM for Urban Traffic Passenger Flows Prediction
inferredcites →https://doi.org/10.1109/tits.2019.2906038openalex-snapshot
Real-Time Sensor Anomaly Detection and Identification in Automated Vehicles
inferredcites →https://doi.org/10.1109/tits.2020.2964410openalex-snapshot
Preserving Privacy in the Internet of Connected Vehicles
inferredcites →https://doi.org/10.1109/tmc.2018.2865355openalex-snapshot
Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing
inferredcites →https://doi.org/10.1109/tmc.2019.2898950openalex-snapshot
HyTasker: Hybrid Task Allocation in Mobile Crowd Sensing
inferredcites →https://doi.org/10.1109/tmc.2019.2938509openalex-snapshot
Distributed and Energy-Efficient Mobile Crowdsensing with Charging Stations by Deep Reinforcement Learning
inferredcites →https://doi.org/10.1109/tnet.2018.2828415openalex-snapshot
Context-Aware Hierarchical Online Learning for Performance Maximization in Mobile Crowdsourcing
inferredcites →https://doi.org/10.1109/tpds.2013.118openalex-snapshot
Trajectory Improves Data Delivery in Urban Vehicular Networks
inferredcites →https://doi.org/10.1109/trustcom/bigdatase.2019.00113openalex-snapshot
Differentially Private Streaming to Untrusted Edge Servers in Intelligent Transportation System
inferredcites →https://doi.org/10.1109/tsc.2019.2922177openalex-snapshot
Enabling Verifiable and Dynamic Ranked Search over Outsourced Data
inferredcites →https://doi.org/10.1109/tsc.2019.2953033openalex-snapshot
Blockchain Empowered Arbitrable Data Auditing Scheme for Network Storage as a Service
inferredcites →https://doi.org/10.1109/tvt.2019.2910568openalex-snapshot
ROVERS: Incentive-Based Recruitment of Connected Vehicles for Urban Big Data Collection
inferredcites →https://doi.org/10.1109/wcnc.2014.6953021openalex-snapshot
Optimal recruitment of smart vehicles for reputation-aware public sensing
inferredcites →https://doi.org/10.1145/2770876openalex-snapshot
SmartRoad
inferredcites →https://doi.org/10.1145/2996913.2997016openalex-snapshot
DNN-based prediction model for spatio-temporal data
inferredcites →https://doi.org/10.1162/neco.1997.9.8.1735openalex-snapshot
Long Short-Term Memory
inferredcites →https://doi.org/10.48550/arxiv.1409.3215openalex-snapshot
Sequence to Sequence Learning with Neural Networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Systematic Survey on Ai-Based Mobile Crowd Sensing/Sourcing Solutions: Applications and Securitychallenges
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Airborne Sensing Application: Reusing Delivery Drones
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Incentive Scheme Based on Privacy Protection for Mobile Crowd-Sensing
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Robust Online Crowdsourcing with Strategic Workers
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TSPR: A Trusted Service Prerequest System for Efficient Data Processing in C-ITS
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Predictive Recruitment in Vehicular Crowdsensing Based on Spatial Sensing Strength Analysis Method
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Portfolio-Based User Recruitment Mechanism for Crowdsensing in Uncertain and Unpredictable Environments
inferred← citeshttps://doi.org/10.1109/tvt.2024.3437636openalex-snapshot
A Cooperative Content Dissemination Framework for Fog-Based Internet of Vehicles
inferred← citeshttps://doi.org/10.1109/tvt.2025.3626007openalex-snapshot
Graph-Meta-Reinforcement-Learning-Based Algorithm for Task Offloading in Vehicular Edge–Cloud Collaboration
inferred← citeshttps://doi.org/10.1109/twc.2024.3372033openalex-snapshot
Mean Field Game-Based Waveform Precoding Design for Mobile Crowd Integrated Sensing, Communication, and Computation Systems
inferred← citeshttps://doi.org/10.1145/3514105.3514121openalex-snapshot
Cooperative Sensing Task Assignment Algorithm for Heterogeneous Devices Based on Internet of Things
inferred← citeshttps://doi.org/10.1145/3679016openalex-snapshot
Marginal Effect-aware Multiple-Vehicle Scheduling for Road Data Collection: A Near-optimal Result
inferred← citeshttps://doi.org/10.1155/2021/6621659openalex-snapshot
Participant Recruitment Method Aiming at Service Quality in Mobile Crowd Sensing
inferred← citeshttps://doi.org/10.1155/2022/2568503openalex-snapshot
Prediction‐Based Resource Deployment and Task Scheduling in Edge‐Cloud Collaborative Computing
inferred← citeshttps://doi.org/10.1177/15501329221133291openalex-snapshot
Teletraffic analysis of a mobile crowdsensing system: The pedestrian-to-vehicle scenario
inferred← citeshttps://doi.org/10.1177/15501477211007412openalex-snapshot
Parameter analysis and optimization of polling-based medium access control protocol for multi-sensor communication
inferred← citeshttps://doi.org/10.1186/s13638-020-01848-5openalex-snapshot
Computation offloading through mobile vehicles in IoT-edge-cloud network
inferred← citeshttps://doi.org/10.2139/ssrn.5115843openalex-snapshot
Development of Task Allocation in Mobile Crowdsensing Using User Mobility Prediction, Participation Rate Prediction, and Population-Based Metaheuristic Algorithms
inferred← citeshttps://doi.org/10.32604/cmc.2023.034993openalex-snapshot
An Incentive Mechanism Model for Crowdsensing with Distributed Storage in Smart Cities
inferred← citeshttps://doi.org/10.3390/a17040166openalex-snapshot
Predicting the Aggregate Mobility of a Vehicle Fleet within a City Graph
inferred← citeshttps://doi.org/10.3390/app112110497openalex-snapshot
A Novel Multi-Objective and Multi-Constraint Route Recommendation Method Based on Crowd Sensing
inferred← citeshttps://doi.org/10.48550/arxiv.2108.02443openalex-snapshot
JOET: Sustainable Vehicle-assisted Edge Computing for Internet of Vehicles
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