Adaptive and Robust Routing With Lyapunov-Based Deep RL in MEC Networks Enabled by Blockchains
暂无分享,去创建一个
Qi Qi | Zhu Han | Jianxin Liao | Jingyu Wang | Zirui Zhuang | J. Liao | Jingyu Wang | Q. Qi | Zhu Han | Zirui Zhuang
[1] Leandros Tassiulas,et al. Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks , 1992 .
[2] A. Tulino,et al. Joint Service Placement and Request Routing in Multi-cell Mobile Edge Computing Networks , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.
[3] Victor C. M. Leung,et al. Performance Optimization for Blockchain-Enabled Industrial Internet of Things (IIoT) Systems: A Deep Reinforcement Learning Approach , 2019, IEEE Transactions on Industrial Informatics.
[4] Nei Kato,et al. A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues , 2017, IEEE Communications Surveys & Tutorials.
[5] Tarik Taleb,et al. On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration , 2017, IEEE Communications Surveys & Tutorials.
[6] Jianwei Wang,et al. 6G Technologies: Key Drivers, Core Requirements, System Architectures, and Enabling Technologies , 2019, IEEE Vehicular Technology Magazine.
[7] Victor C. M. Leung,et al. Topology control in mobile Ad Hoc networks with cooperative communications , 2012, IEEE Wireless Communications.
[8] Federico Chiariotti,et al. D-DASH: A Deep Q-Learning Framework for DASH Video Streaming , 2017, IEEE Transactions on Cognitive Communications and Networking.
[9] Wenzhong Li,et al. Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing , 2015, IEEE/ACM Transactions on Networking.
[10] Nadia Nouali-Taboudjemat,et al. Lifetime-Aware Backpressure—A New Delay-Enhanced Backpressure-Based Routing Protocol , 2019, IEEE Systems Journal.
[11] Yonggang Wen,et al. A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks , 2018, IEEE Access.
[12] Roy D. Yates,et al. Real-time status: How often should one update? , 2012, 2012 Proceedings IEEE INFOCOM.
[13] Qi Qi,et al. Service Function Chain Embedding for NFV-Enabled IoT Based on Deep Reinforcement Learning , 2019, IEEE Communications Magazine.
[14] Eytan Modiano,et al. Power allocation and routing in multibeam satellites with time-varying channels , 2003, TNET.
[15] Georgios Kambourakis,et al. DDoS in the IoT: Mirai and Other Botnets , 2017, Computer.
[16] Lin Chen,et al. On Security Analysis of Proof-of-Elapsed-Time (PoET) , 2017, SSS.
[17] Josep Mangues-Bafalluy,et al. Distributed Lyapunov drift-plus-penalty routing for WiFi mesh networks with adaptive penalty weight , 2012, 2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM).
[18] Lacra Pavel,et al. A stability analysis with time-delay of primal-dual power control in optical links , 2009, Autom..
[19] Babak Hossein Khalaj,et al. Delay Analysis of Network Coding in Multicast Networks With Markovian Arrival Processes: A Practical Framework in Cache-Enabled Networks , 2018, IEEE Transactions on Vehicular Technology.
[20] R. Bellman. A Markovian Decision Process , 1957 .
[21] Zhu Han,et al. Contract-Based Approach for Security Deposit in Blockchain Networks with Shards , 2019, 2019 IEEE International Conference on Blockchain (Blockchain).
[22] Minghua Chen,et al. When Backpressure Meets Predictive Scheduling , 2013, IEEE/ACM Transactions on Networking.
[23] Aziz Mohaisen,et al. RouteChain: Towards Blockchain-based Secure and Efficient BGP Routing , 2019, 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC).
[24] Xu Chen,et al. In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning , 2018, IEEE Network.
[25] Michael J. Neely,et al. Stability and Probability 1 Convergence for Queueing Networks via Lyapunov Optimization , 2012, J. Appl. Math..
[26] H. Okamura,et al. Markovian Arrival Process Parameter Estimation With Group Data , 2009, IEEE/ACM Transactions on Networking.
[27] Jong Hyuk Park,et al. Adaptive data rate control in low power wide area networks for long range IoT services , 2017, J. Comput. Sci..
[28] Sergio Barbarossa,et al. Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing , 2014, IEEE Transactions on Signal and Information Processing over Networks.
[29] Giuliano Casale. Building accurate workload models using Markovian arrival processes , 2011, SIGMETRICS '11.
[30] Nail Akar,et al. Markov fluid queue model of an energy harvesting IoT device with adaptive sensing , 2017, Perform. Evaluation.
[31] Choong Seon Hong,et al. Meta-Learning-Based Deep Learning Model Deployment Scheme for Edge Caching , 2019, 2019 15th International Conference on Network and Service Management (CNSM).
[32] Stephen Dawson,et al. Markovian Workload Characterization for QoS Prediction in the Cloud , 2011, 2011 IEEE 4th International Conference on Cloud Computing.
[33] A.M. Gonzalez,et al. Modeling and forecasting electricity prices with input/output hidden Markov models , 2005, IEEE Transactions on Power Systems.
[34] Christoph Lindemann,et al. Modeling IP traffic using the batch Markovian arrival process , 2003, Perform. Evaluation.
[35] Jun Zhang,et al. Stochastic Joint Radio and Computational Resource Management for Multi-User Mobile-Edge Computing Systems , 2017, IEEE Transactions on Wireless Communications.
[36] Pingzhi Fan,et al. 6G Wireless Networks: Vision, Requirements, Architecture, and Key Technologies , 2019, IEEE Vehicular Technology Magazine.
[37] Leandros Tassiulas,et al. Backpressure on the Backbone: A Lightweight, Non-Intrusive Traffic Engineering Approach , 2016, IEEE Transactions on Network and Service Management.
[38] Geoffrey Ye Li,et al. Deep Reinforcement Learning for Resource Allocation in V2V Communications , 2017, 2018 IEEE International Conference on Communications (ICC).
[39] B. Liang,et al. Mobile Edge Computing , 2020, Encyclopedia of Wireless Networks.
[40] Victor C. M. Leung,et al. Joint Topology Control and Authentication Design in Mobile Ad Hoc Networks With Cooperative Communications , 2012, IEEE Transactions on Vehicular Technology.
[41] F. Richard Yu,et al. Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues and Challenges , 2019, IEEE Communications Surveys & Tutorials.
[42] Xin Wang,et al. Learning and Management for Internet of Things: Accounting for Adaptivity and Scalability , 2018, Proceedings of the IEEE.
[43] Elisa Bertino,et al. Botnets and Internet of Things Security , 2017, Computer.
[44] Tong Zhang,et al. NFV Closed-loop Automation Experiments using Deep Reinforcement Learning , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).
[45] R. E. Kalman,et al. Control System Analysis and Design Via the “Second Method” of Lyapunov: I—Continuous-Time Systems , 1960 .
[46] Michael J. Neely,et al. Optimal Backpressure Routing for Wireless Networks with Multi-Receiver Diversity , 2006, 2006 40th Annual Conference on Information Sciences and Systems.
[47] Amie Corso,et al. Performance Analysis of Proof-of-Elapsed-Time (PoET) Consensus in the Sawtooth Blockchain Framework , 2019 .
[48] Victor C. M. Leung,et al. A Mobile Edge Computing (MEC)-Enabled Transcoding Framework for Blockchain-Based Video Streaming , 2020, IEEE Wireless Communications.
[49] Mubashir Husain Rehmani,et al. Applications of Blockchains in the Internet of Things: A Comprehensive Survey , 2019, IEEE Communications Surveys & Tutorials.
[50] Xiaofei Wang,et al. Federated Deep Reinforcement Learning for Internet of Things With Decentralized Cooperative Edge Caching , 2020, IEEE Internet of Things Journal.
[51] Shiwen He,et al. A Trusted Routing Scheme Using Blockchain and Reinforcement Learning for Wireless Sensor Networks , 2019, Sensors.
[52] Ness B. Shroff,et al. Minimizing the Age of Information Through Queues , 2017, IEEE Transactions on Information Theory.
[53] Min Chen,et al. Narrow Band Internet of Things , 2017, IEEE Access.
[54] Albert Cabellos-Aparicio,et al. A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization , 2017, ArXiv.
[55] Leandros Tassiulas,et al. Resource Allocation and Cross-Layer Control in Wireless Networks , 2006, Found. Trends Netw..
[56] Michael J. Neely,et al. Opportunistic scheduling with worst case delay guarantees in single and multi-hop networks , 2011, 2011 Proceedings IEEE INFOCOM.
[57] Marco Pavone,et al. Cellular Network Traffic Scheduling With Deep Reinforcement Learning , 2018, AAAI.
[58] Lei Zheng,et al. A Distributed Demand Response Control Strategy Using Lyapunov Optimization , 2014, IEEE Transactions on Smart Grid.
[59] Khaled Ben Letaief,et al. A Lyapunov Optimization Approach for Green Cellular Networks With Hybrid Energy Supplies , 2015, IEEE Journal on Selected Areas in Communications.
[60] Ee-Chien Chang,et al. Towards Scaling Blockchain Systems via Sharding , 2018, SIGMOD Conference.
[61] Panagiotis D. Christofides,et al. Lyapunov-Based Model Predictive Control of Nonlinear Systems Subject to Data Losses , 2007, IEEE Transactions on Automatic Control.
[62] Guy Shani,et al. Noname manuscript No. (will be inserted by the editor) A Survey of Point-Based POMDP Solvers , 2022 .
[63] Hao Che,et al. Adaptive control algorithms for decentralized optimal traffic engineering in the Internet , 2004, IEEE/ACM Transactions on Networking.
[64] Albert Cabellos-Aparicio,et al. Feature Engineering for Deep Reinforcement Learning Based Routing , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).
[65] Eytan Modiano,et al. Minimizing the Age of Information in Wireless Networks with Stochastic Arrivals , 2019, IEEE Transactions on Mobile Computing.
[66] Hao Yu,et al. A New Backpressure Algorithm for Joint Rate Control and Routing With Vanishing Utility Optimality Gaps and Finite Queue Lengths , 2018, IEEE/ACM Transactions on Networking.
[67] Chi Harold Liu,et al. Experience-driven Networking: A Deep Reinforcement Learning based Approach , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.
[68] Miao Pan,et al. Dynamic Cache Placement, Node Association, and Power Allocation in Fog Aided Networks , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).
[69] Zhu Han,et al. When Mobile Blockchain Meets Edge Computing , 2017, IEEE Communications Magazine.