Robust Mobile Crowd Sensing: When Deep Learning Meets Edge Computing

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Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing

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A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

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A Survey on Green 6G Network: Architecture and Technologies

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Toward Edge Intelligence: Multiaccess Edge Computing for 5G and Internet of Things

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6G Wireless Systems: A Vision, Architectural Elements, and Future Directions

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Computation Resource Allocation and Task Assignment Optimization in Vehicular Fog Computing: A Contract-Matching Approach

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Security and privacy in 6G networks: New areas and new challenges

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Leveraging deep learning and IoT big data analytics to support the smart cities development: Review and future directions

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Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge

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Learning in the Air: Secure Federated Learning for UAV-Assisted Crowdsensing

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Deep Reinforcement Learning for Internet of Things: A Comprehensive Survey

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Secure and Efficient Vehicle-to-Grid Energy Trading in Cyber Physical Systems: Integration of Blockchain and Edge Computing

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Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing

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Security Requirements and Challenges of 6G Technologies and Applications

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Comprehensive Survey on Machine Learning in Vehicular Network: Technology, Applications and Challenges

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Energy-Efficient Edge Computing Service Provisioning for Vehicular Networks: A Consensus ADMM Approach

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Deep Learning for Security in Digital Twins of Cooperative Intelligent Transportation Systems

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An extended machine learning technique for polycystic ovary syndrome detection using ovary ultrasound image

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NOMA Assisted Multi-Task Multi-Access Mobile Edge Computing via Deep Reinforcement Learning for Industrial Internet of Things

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Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective

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Access Control and Resource Allocation for M2M Communications in Industrial Automation

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DNNOff: Offloading DNN-Based Intelligent IoT Applications in Mobile Edge Computing

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Reliable Task Offloading for Vehicular Fog Computing Under Information Asymmetry and Information Uncertainty

Zhou, Z., Liao, H., Zhao, X., Ai, B., & Guizani, M. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2926732 · Cited by 142

Energy-Efficient Distributed Mobile Crowd Sensing: A Deep Learning Approach

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Drones’ Edge Intelligence Over Smart Environments in B5G: Blockchain and Federated Learning Synergy

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Distributed and Energy-Efficient Mobile Crowdsensing with Charging Stations by Deep Reinforcement Learning

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Task Offloading and Trajectory Control for UAV-Assisted Mobile Edge Computing Using Deep Reinforcement Learning

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Anomaly Detection in 6G Networks Using Machine Learning Methods

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Towards Personalized Privacy-Preserving Incentive for Truth Discovery in Mobile Crowdsensing Systems

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Blockchain-Based Mobile Crowd Sensing in Industrial Systems

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Energy-Efficient Resource Allocation for Energy Harvesting-Based Cognitive Machine-to-Machine Communications

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Extraction and Interpretation of Deep Autoencoder-based Temporal Features from Wearables for Forecasting Personalized Mood, Health, and Stress

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Resource Sharing and Task Offloading in IoT Fog Computing: A Contract-Learning Approach

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Matching-Based Task Offloading for Vehicular Edge Computing

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Automatic Modulation Classification for MIMO Systems via Deep Learning and Zero-Forcing Equalization

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Deep Learning-Based Resource Allocation for 5G Broadband TV Service

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A Systematic Literature Review on Distributed Machine Learning in Edge Computing

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Multiagent Actor-Critic Network-Based Incentive Mechanism for Mobile Crowdsensing in Industrial Systems

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Stackelberg Game-Based Computation Offloading in Social and Cognitive Industrial Internet of Things

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Social-Aware Incentive Mechanism for Vehicular Crowdsensing by Deep Reinforcement Learning

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Salient Object Detection in the Distributed Cloud-Edge Intelligent Network

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On the Physical Layer of Digital Twin: An Integrated Sensing and Communications Perspective

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Pyramid: Enabling Hierarchical Neural Networks with Edge Computing

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When Crowdsensing Meets Smart Cities: A Comprehensive Survey and New Perspectives

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Task Offloading in Vehicular Mobile Edge Computing: A Matching-Theoretic Framework

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Dependency-Aware Computation Offloading in Mobile Edge Computing: A Reinforcement Learning Approach

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Autodidactic Neurosurgeon: Collaborative Deep Inference for Mobile Edge Intelligence via Online Learning

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Ultra-Low Latency Multi-Task Offloading in Mobile Edge Computing

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Multi-level graph neural network for text sentiment analysis

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Hierarchical Task Offloading for Vehicular Fog Computing Based on Multi-Agent Deep Reinforcement Learning

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Optimal Online Data Partitioning for Geo-Distributed Machine Learning in Edge of Wireless Networks

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Privacy Preserving Participant Recruitment for Coverage Maximization in Location Aware Mobile Crowdsensing

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Distributed Sensing Using Smart End-User Devices: Pathway to Federated Learning for Autonomous IoT

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Resource Management for Computation Offloading in D2D-Aided Wireless Powered Mobile-Edge Computing Networks

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BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing

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Crowdsensing 2.0

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Multimodal pedestrian detection using metaheuristics with deep convolutional neural network in crowded scenes

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Information-Centric Networking: Research and Standardization Status

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Toward Incentivizing Fog-Based Privacy-Preserving Mobile Crowdsensing in the Internet of Vehicles

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CystNet: An AI driven model for PCOS detection using multilevel thresholding of ultrasound images

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Federated learning framework for mobile edge computing networks

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Towards Robust Task Assignment in Mobile Crowdsensing Systems

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CNN-based intelligent safety surveillance in green IoT applications

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Mobile Crowdsensing for Data Freshness: A Deep Reinforcement Learning Approach

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Human-in-the-Loop Wireless Communications: Machine Learning and Brain-Aware Resource Management

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Joint Optimization for Task Offloading in Edge Computing: An Evolutionary Game Approach

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An Energy-Efficient Cross-Layer-Sensing Clustering Method Based on Intelligent Fog Computing in WSNs

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A New Vehicular Fog Computing Architecture for Cooperative Sensing of Autonomous Driving

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A Deep Learning Approach for Task Offloading in Multi-UAV Aided Mobile Edge Computing

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E^2 C-Chain: A Two-Stage Incentive Education Employment and Skill Certification Blockchain

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Asynchronous federated learning with directed acyclic graph-based blockchain in edge computing: Overview, design, and challenges

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When Connected and Automated Vehicles Meet Mobile Crowdsensing: A Perception and Transmission Framework in the Metaverse

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Optimal Pricing and Service Selection in the Mobile Cloud Architectures

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Blockchain-Based Certification for Education, Employment, and Skill with Incentive Mechanism

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Quality-Aware Sparse Data Collection in MEC-Enhanced Mobile Crowdsensing Systems

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On Enabling Mobile Crowd Sensing for Data Collection in Smart Agriculture: A Vision

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A Survey on Technological Trends to Enhance Spectrum-Efficiency in 6G Communications

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A UAV-Assisted Truth Discovery Approach With Incentive Mechanism Design in Mobile Crowd Sensing

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Enabling AI in future wireless networks: a data life cycle perspective

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Machine learning-based diffusion model for prediction of coronavirus-19 outbreak

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A Novel Data Fusion Strategy Based on Extreme Learning Machine Optimized by Bat Algorithm for Mobile Heterogeneous Wireless Sensor Networks

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An Edge Computing-Based Photo Crowdsourcing Framework for Real-Time 3D Reconstruction

Yu, S., Chen, X., Wang, S., Pu, L., & Wu, D. (2020) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.3007654 · Cited by 35

AI Inspired Intelligent Resource Management in Future Wireless Network

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Data Sensing and Offloading in Edge Computing Networks: TDMA or NOMA?

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Invited Paper: AI-Based Security Design of Mobile Crowdsensing Systems: Review, Challenges and Case Studies

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Licensed and Unlicensed Spectrum Management for Cognitive M2M: A Context-Aware Learning Approach

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HSM-SMCS: Task Assignment Based on Hybrid Sensing Modes in Sparse Mobile Crowdsensing

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