Adaptive and Robust Routing With Lyapunov-Based Deep RL in MEC Networks Enabled by Blockchains

[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.