Blockchain-Empowered Collaborative Task Offloading for Cloud-Edge-Device Computing

Delay-Aware and Energy-Efficient Computation Offloading in Mobile-Edge Computing Using Deep Reinforcement Learning

(2021) Laha Ale et al.   IEEE Transactions on Cognitive Communications and Networking

Privacy-Preserving Blockchain-Based Federated Learning for Marine Internet of Things

(2021) Zhenquan Qin et al.   IEEE Transactions on Computational Social Systems

Trusted Resource Allocation Based on Smart Contracts for Blockchain-Enabled Internet of Things

(2021) Hongju Cheng et al.   IEEE Internet of Things Journal

Trustworthy Blockchain-Empowered Collaborative Edge Computing-as-a-Service Scheduling and Data Sharing in the IIoE

(2021) Fenhua Bai et al.   IEEE Internet of Things Journal

Efficient Compute-Intensive Job Allocation in Data Centers via Deep Reinforcement Learning

(2020) Deliang Yi et al.   IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS

Joint Resource Allocation and Incentive Design for Blockchain-Based Mobile Edge Computing

(2020) Wen Sun et al.   IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS

Collaborate Edge and Cloud Computing With Distributed Deep Learning for Smart City Internet of Things

(2020) Huaming Wu et al.   IEEE Internet of Things Journal

Blockchain at the Edge: Performance of Resource-Constrained IoT Networks

(2020) Sudip Misra et al.   IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS

BC-Mobile Device Cloud: A Blockchain-Based Decentralized Truthful Framework for Mobile Device Cloud

(2020) Mu Wang et al.   IEEE Transactions on Industrial Informatics

Blockchain-Based Edge Computing Resource Allocation in IoT: A Deep Reinforcement Learning Approach

(2020) Ying He et al.   IEEE Internet of Things Journal

EEDTO: An Energy-Efficient Dynamic Task Offloading Algorithm for Blockchain-Enabled IoT-Edge-Cloud Orchestrated Computing

(2020) Huaming Wu et al.   IEEE Internet of Things Journal

A Blockchained Federated Learning Framework for Cognitive Computing in Industry 4.0 Networks

(2020) Youyang Qu et al.   IEEE Transactions on Industrial Informatics

Distributed Multi-Cloud Multi-Access Edge Computing by Multi-Agent Reinforcement Learning

(2020) Yutong Zhang et al.   IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS

Distributed Task Migration Optimization in MEC by Extending Multi-Agent Deep Reinforcement Learning Approach

(2020) Chubo Liu et al.   IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS

Augmented Queue-Based Transmission and Transcoding Optimization for Livecast Services Based on Cloud-Edge-Crowd Integration

(2020) Xingyan Chen et al.   IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY

Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing Systems

(2020) Ming Tang et al.   IEEE TRANSACTIONS ON MOBILE COMPUTING

D2D Communications Meet Mobile Edge Computing for Enhanced Computation Capacity in Cellular Networks

(2019) Yinghui He et al.   IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS

Learning-Based Computation Offloading for IoT Devices With Energy Harvesting

(2019) Minghui Min et al.   IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY

Adaptive Federated Learning in Resource Constrained Edge Computing Systems

(2019) Shiqiang Wang et al.   IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS

Joint Task Assignment and Resource Allocation for D2D-Enabled Mobile-Edge Computing

(2019) Hong Xing et al.   IEEE TRANSACTIONS ON COMMUNICATIONS

Cost-Driven Off-Loading for DNN-Based Applications Over Cloud, Edge, and End Devices

(2019) Bing Lin et al.   IEEE Transactions on Industrial Informatics

Energy-Efficient Device-to-Device Edge Computing Network: An Approach Offloading Both Traffic and Computation

(2018) Jinming Wen et al.   IEEE COMMUNICATIONS MAGAZINE

Optimized Computation Offloading Performance in Virtual Edge Computing Systems Via Deep Reinforcement Learning

(2018) Xianfu Chen et al.   IEEE Internet of Things Journal

The Software-Defined Vehicular Cloud: A New Level of Sharing the Road

(2017) Insun Jang et al.   IEEE Vehicular Technology Magazine