AIDA - A holistic AI-driven networking and processing framework for industrial IoT applications

[1]  A. Nee,et al.  Microservices-based cloud-edge collaborative condition monitoring platform for smart manufacturing systems , 2022, Int. J. Prod. Res..

[2]  Anandarup Mukherjee,et al.  SEGA: Secured Edge Gateway Microservices Architecture for IIoT-Based Machine Monitoring , 2022, IEEE Transactions on Industrial Informatics.

[3]  Mohammed A. Al-Sharafi,et al.  A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data , 2021, Applied Sciences.

[4]  Tim Leinmüller,et al.  Dynamic Scheduling and Routing for TSN based In-vehicle Networks , 2021, 2021 IEEE International Conference on Communications Workshops (ICC Workshops).

[5]  Yuqian Lu,et al.  Digital Twin-driven online anomaly detection for an automation system based on edge intelligence , 2021 .

[6]  Rudrajeet Pal,et al.  Demystifying process-level scalability challenges in fashion remanufacturing: An interdependence perspective , 2021 .

[7]  Victor C. M. Leung,et al.  Net-in-AI: A Computing-Power Networking Framework with Adaptability, Flexibility, and Profitability for Ubiquitous AI , 2021, IEEE Network.

[8]  G. Fortino,et al.  Smart Anomaly Detection in Sensor Systems , 2020, Inf. Fusion.

[9]  Muhammad Usman,et al.  Maintaining SmartX multi‐view visibility for OF@TEIN+ distributed cloud‐native edge boxes , 2020, Trans. Emerg. Telecommun. Technol..

[10]  Sameep Mehta,et al.  Overview and Importance of Data Quality for Machine Learning Tasks , 2020, KDD.

[11]  Paul Pop,et al.  The FORA Fog Computing Platform for Industrial IoT , 2020, Inf. Syst..

[12]  Zerui Li,et al.  Well Logging Based Lithology Identification Model Establishment Under Data Drift: A Transfer Learning Method , 2020, Sensors.

[13]  Muhammad Usman,et al.  SmartX Multi‐View Visibility Framework for unified monitoring of SDN‐enabled multisite clouds , 2019, Trans. Emerg. Telecommun. Technol..

[14]  Guangquan Zhang,et al.  Learning under Concept Drift: A Review , 2019, IEEE Transactions on Knowledge and Data Engineering.

[15]  Sazia Mahfuz,et al.  A Survey of Distributed Data Stream Processing Frameworks , 2019, IEEE Access.

[16]  Christoph Hochreiner,et al.  Towards a Framework for Data Stream Processing in the Fog , 2019, Informatik Spektrum.

[17]  A. Adebiyi,et al.  Big data stream analysis: a systematic literature review , 2019, Journal of Big Data.

[18]  Tarik Taleb,et al.  EiF: Toward an Elastic IoT Fog Framework for AI Services , 2019, IEEE Communications Magazine.

[19]  Jaime G. Carbonell,et al.  Low-Dimensional Density Ratio Estimation for Covariate Shift Correction , 2019, AISTATS.

[20]  Domenico Talia,et al.  New Landscapes of the Data Stream Processing in the era of Fog Computing , 2019, Future Gener. Comput. Syst..

[21]  Paul Pop,et al.  AVB-Aware Routing and Scheduling of Time-Triggered Traffic for TSN , 2018, IEEE Access.

[22]  Jesús Montes,et al.  FMonE: A Flexible Monitoring Solution at the Edge , 2018, Wirel. Commun. Mob. Comput..

[23]  JongWon Kim,et al.  Interactive Visualization of SDN-Enabled Multisite Cloud Playgrounds Leveraging SmartX MultiView Visibility Framework , 2018, Comput. J..

[24]  Florian Pethig,et al.  A Survey of Internet of Things and Big Data integrated Solutions for Industrie 4.0 , 2018, 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA).

[25]  Paul Pop,et al.  Enabling Fog Computing for Industrial Automation Through Time-Sensitive Networking (TSN) , 2018, IEEE Communications Standards Magazine.

[26]  Paul Pop,et al.  Scheduling in time sensitive networks (TSN) for mixed-criticality industrial applications , 2018, 2018 14th IEEE International Workshop on Factory Communication Systems (WFCS).

[27]  Silviu S. Craciunas,et al.  IEEE 802.1Qbv Gate Control List Synthesis Using Array Theory Encoding , 2018, 2018 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS).

[28]  Gürkan Solmaz,et al.  FogFlow: Easy Programming of IoT Services Over Cloud and Edges for Smart Cities , 2018, IEEE Internet of Things Journal.

[29]  Xiao Liu,et al.  SmartVM: A Multi-Layer Microservice-Based Platform for Deploying SaaS , 2017, 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC).

[30]  Dirk Timmermann,et al.  ILP-based joint routing and scheduling for time-triggered networks , 2017, RTNS.

[31]  Paul Pop,et al.  Runtime reconfiguration of time-sensitive networking (TSN) schedules for Fog Computing , 2017, 2017 IEEE Fog World Congress (FWC).

[32]  Omair Ahmad Khan,et al.  Software Defined Network (SDN) Based Internet of Things (IoT): A Road Ahead , 2017, ICFNDS.

[33]  Aniruddha S. Gokhale,et al.  Dynamic Resource Management Across Cloud-Edge Resources for Performance-Sensitive Applications , 2017, 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID).

[34]  Christos Tselios,et al.  On QoE-awareness through virtualized probes in 5G networks , 2016, 2016 IEEE 21st International Workshop on Computer Aided Modelling and Design of Communication Links and Networks (CAMAD).

[35]  Schahram Dustdar,et al.  VISP: An Ecosystem for Elastic Data Stream Processing for the Internet of Things , 2016, 2016 IEEE 20th International Enterprise Distributed Object Computing Conference (EDOC).

[36]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[37]  Daniel Pakkala,et al.  Reference Architecture and Classification of Technologies, Products and Services for Big Data Systems , 2015, Big Data Res..

[38]  Liming Zhu,et al.  Four-Fold Auto-Scaling on a Contemporary Deployment Platform Using Docker Containers , 2015, ICSOC.

[39]  Akihiro Nakao,et al.  GENI: A federated testbed for innovative network experiments , 2014, Comput. Networks.

[40]  M. Kawanabe,et al.  Direct importance estimation for covariate shift adaptation , 2008 .

[41]  Motoaki Kawanabe,et al.  Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation , 2007, NIPS.

[42]  Klaus-Robert Müller,et al.  Covariate Shift Adaptation by Importance Weighted Cross Validation , 2007, J. Mach. Learn. Res..

[43]  J. Feld,et al.  PROFINET - scalable factory communication for all applications , 2004, IEEE International Workshop on Factory Communication Systems, 2004. Proceedings..

[44]  L. Breiman.  Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[45]  A. Brunstrom,et al.  A Survey on Observability of Distributed Edge & Container-Based Microservices , 2022, IEEE Access.

[46]  Philippe Martin,et al.  Accessing the Cluster , 2020 .