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

[1]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[2]  F. Richard Yu,et al.  Optimal Joint Session Admission Control in Integrated WLAN and CDMA Cellular Networks with Vertical Handoff , 2007, IEEE Transactions on Mobile Computing.

[3]  Nan Zhao,et al.  Integrated Networking, Caching, and Computing for Connected Vehicles: A Deep Reinforcement Learning Approach , 2018, IEEE Transactions on Vehicular Technology.

[4]  Yuandong Tian,et al.  Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning , 2016, ICLR.

[5]  Mohsen Guizani,et al.  Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications , 2015, IEEE Communications Surveys & Tutorials.

[6]  Yan Wang,et al.  Computation Offloading with Multiple Agents in Edge-Computing–Supported IoT , 2019, ACM Trans. Sens. Networks.

[7]  Xiaofei Wang,et al.  Hierarchical Edge Caching in Device-to-Device Aided Mobile Networks: Modeling, Optimization, and Design , 2018, IEEE Journal on Selected Areas in Communications.

[8]  Xiaofei Wang,et al.  Federated Deep Reinforcement Learning for Internet of Things With Decentralized Cooperative Edge Caching , 2020, IEEE Internet of Things Journal.

[9]  Victor C. M. Leung,et al.  Computation Offloading and Content Caching in Wireless Blockchain Networks With Mobile Edge Computing , 2018, IEEE Transactions on Vehicular Technology.

[10]  Teruo Higashino,et al.  Edge-centric Computing: Vision and Challenges , 2015, CCRV.

[11]  F. Richard Yu,et al.  Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues and Challenges , 2019, IEEE Communications Surveys & Tutorials.

[12]  Xiaofei Wang,et al.  Networking Integrated Cloud–Edge–End in IoT: A Blockchain-Assisted Collective Q-Learning Approach , 2021, IEEE Internet of Things Journal.

[13]  Bingsheng He,et al.  QoS-Aware Resource Allocation for Video Transcoding in Clouds , 2017, IEEE Transactions on Circuits and Systems for Video Technology.

[14]  Hongzi Mao,et al.  Neural Adaptive Video Streaming with Pensieve , 2017, SIGCOMM.

[15]  María Bermúdez-Edo,et al.  A Knowledge-Based Approach for Real-Time IoT Data Stream Annotation and Processing , 2014, 2014 IEEE International Conference on Internet of Things(iThings), and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom).

[16]  F. Richard Yu,et al.  A Survey of Blockchain Technology Applied to Smart Cities: Research Issues and Challenges , 2019, IEEE Communications Surveys & Tutorials.

[17]  Tom Schaul,et al.  Reinforcement Learning with Unsupervised Auxiliary Tasks , 2016, ICLR.

[18]  Xiaoli Chu,et al.  Enabling Low-Latency Applications in LTE-A Based Mixed Fog/Cloud Computing Systems , 2019, IEEE Transactions on Vehicular Technology.

[19]  PRADIP KUMAR SHARMA,et al.  A Software Defined Fog Node Based Distributed Blockchain Cloud Architecture for IoT , 2018, IEEE Access.

[20]  Xu Chen,et al.  In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning , 2018, IEEE Network.

[21]  Haipeng Yao,et al.  Blockchain-Based Software-Defined Industrial Internet of Things: A Dueling Deep ${Q}$ -Learning Approach , 2019, IEEE Internet of Things Journal.

[22]  Xiaoli Chu,et al.  Computation Offloading and Resource Allocation in Mixed Fog/Cloud Computing Systems With Min-Max Fairness Guarantee , 2018, IEEE Transactions on Communications.

[23]  Joel J. P. C. Rodrigues,et al.  Decentralized Consensus for Edge-Centric Internet of Things: A Review, Taxonomy, and Research Issues , 2018, IEEE Access.

[24]  Sergey Levine,et al.  High-Dimensional Continuous Control Using Generalized Advantage Estimation , 2015, ICLR.

[25]  Zhu Han,et al.  Computing Resource Allocation in Three-Tier IoT Fog Networks: A Joint Optimization Approach Combining Stackelberg Game and Matching , 2017, IEEE Internet of Things Journal.

[26]  Awais Ahmad,et al.  Urban planning and building smart cities based on the Internet of Things using Big Data analytics , 2016, Comput. Networks.

[27]  Xianping Guo,et al.  Continuous-Time Controlled Markov Chains with Discounted Rewards , 2003 .

[28]  Xiaofei Wang,et al.  Convergence of Edge Computing and Deep Learning: A Comprehensive Survey , 2019, IEEE Communications Surveys & Tutorials.

[29]  Alex Graves,et al.  Asynchronous Methods for Deep Reinforcement Learning , 2016, ICML.