The FORA Fog Computing Platform for Industrial IoT

Information Systems

Volumn 98, Issue , 2021, Pages

  Pop, Paul ^a   Zarrin, Bahram ^a   Barzegaran, Mohammadreza ^a   Schulte, Stefan ^b   Punnekkat, Sasikumar ^c   Ruh, Jan ^d   Steiner, Wilfried ^d  

^a TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

^b VIENNA UNIVERSITY OF TECHNOLOGY   (Austria)

^c MÄLARDALEN UNIVERSITY   (Sweden)

^d TTTech North America   (Austria)

Author keywords

AADL; Deterministic virtualization; Fog Computing; Industrial IoT; Industry 4.0; Time Sensitive Networking

Indexed keywords

COMPUTER ARCHITECTURE; FAULT TOLERANCE; FOG; IEEE STANDARDS; INDUSTRIAL INTERNET OF THINGS (IIOT); INTEROPERABILITY; MIDDLEWARE; NETWORK SECURITY; PROCESS CONTROL;

ARCHITECTURE ANALYSIS; COMMUNICATION TECHNOLOGIES; DISTRIBUTED MACHINE LEARNING; NON-FUNCTIONAL REQUIREMENTS; OPC UNIFIED ARCHITECTURES; REFERENCE ARCHITECTURE; RESOURCE MANAGEMENT; SAFETY AND SECURITIES;

FOG COMPUTING;

EID: 85100531828     PISSN: 03064379     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.is.2021.101727     Document Type: Article

References (104)

1. Lasi, H., Fettke, P., Kemper, H.G., Feld, T., Hoffmann, M., Industry 4.0. Bus. Inf. Syst. Eng. 6 (2014), 239–242.
2. Williams, T.J., The purdue enterprise reference architecture. Comput. Ind. 24 (1994), 141–158.
3. F. Bonomi, R. Milito, J. Zhu, S. Addepalli, Fog computing and its role in the internet of things, in: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, 2012, pp. 13–16.
4. OpenFog Consortium. OpenFog Reference Architecture for Fog Computing. 2017, Architecture Working Group, 1–162.
5. Bonomi, F., Milito, R.A., Natarajan, P., Zhu, J., Fog computing: A platform for Internet of Things and analytics. Bessis, N., Dobre, C., (eds.) Big Data and Internet of Things: A Roadmap for Smart Environments Studies in Computational Intelligence, vol. 546, 2014, Springer, 169–186, 10.1007/978-3-319-05029-4_7.
6. Puliafito, C., Mingozzi, E., Longo, F., Puliafito, A., Rana, O., Fog computing for the internet of things: A survey. ACM Trans. Internet Technol. 19 (2019), 1–41.
7. Hu, P., Dhelim, S., Ning, H., Qiu, T., Survey on fog computing: architecture, key technologies, applications and open issues. J. Netw. Comput. Appl. 98 (2017), 27–42.
8. TSN Task Group. Time-sensitive networking. 2017 (accessed July 1, 2020). URL: http://www.ieee802.org/1/pages/tsn.html.
9. Dahlman, E., Mildh, G., Parkvall, S., Peisa, J., Sachs, J., Selén, Y., Sköld, J., 5g wireless access: requirements and realization. IEEE Commun. Mag. 52 (2014), 42–47.
10. Mahnke, W., Leitner, S.H., Damm, M., OPC Unified Architecture. 2009, Springer Science & Business Media.
11. Feiler, P.H., Gluch, D.P., Hudak, J.J., The Architecture Analysis & Design Language (AADL): An Introduction: Technical Report. 2006, Carnegie-Mellon Univ Pittsburgh PA Software Engineering Inst.
12. M. Barzegaran, N. Desai, J. Qian, K. Tange, B. Zarrin, P. Pop, J. Kuusela, Fogification of electric drives: An industrial use case, in: Proc. of IEEE International Conference on Emerging Technologies and Factory Automation, 2020a, pp. 77–84.
13. P. Denzler, J. Ruh, M. Kadar, C. Avasalcai, W. Kastner, Towards consolidating industrial use cases on a common fog computing platform, in: Proc. of IEEE International Conference on Emerging Technologies and Factory Automation, 2020, pp. 172–179.
14. M.S. Shaik, V. Struhár, Z. Bakhshi, V.L. Dao, N. Desai, A.V. Papadopoulos, T. Nolte, V. Karagiannis, S. Schulte, A. Venito, G. Fohler, Enabling fog-based industrial robotics systems, in: Proc. of IEEE International Conference on Emerging Technologies and Factory Automation, 2020, pp. 61–68.
15. Dastjerdi, A.V., Gupta, H., Calheiros, R.N., Ghosh, S.K., Buyya, R., Fog Computing: Principles, Architectures, and Applications; Internet of Things. 2016, Elsevier, Amsterdam, The Netherlands.
16. IEEE. 1934-2018 – IEEE standard for adoption of OpenFog reference architecture for fog computing. 2018.
17. Giust, F., Costa-Perez, X., Reznik, A., Multi-access edge computing: An overview of ETSI MEC ISG. IEEE 5G Tech. Focus, 1, 2017.
18. Puliafito, C., Mingozzi, E., Vallati, C., Longo, F., Merlino, G., Companion fog computing: Supporting things mobility through container migration at the edge. 2018 IEEE International Conference on Smart Computing, 2018, 97–105, 10.1109/SMARTCOMP.2018.00079.
19. Habibi, P., Baharlooei, S., Farhoudi, M., Kazemian, S., Khorsandi, S., Virtualized SDN-based end-to-end reference architecture for fog networking. IEEE International Conference on Advanced Information Networking and Applications Workshops, 2018, 61–66, 10.1109/WAINA.2018.00064.
20. de Brito, M.S., Hoque, S., Magedanz, T., Steinke, R., Willner, A., Nehls, D., Keil, O., Schreiner, F., A service orchestration architecture for fog-enabled infrastructures. Second International Conference on Fog and Mobile Edge Computing, 2017, 127–132, 10.1109/FMEC.2017.7946419.
21. Mahmud, R., Koch, F.L., Buyya, R., Cloud-fog interoperability in IoT-enabled healthcare solutions. International Conference on Distributed Computing and Networking, 2018, 32:1–32:10, 10.1145/3154273.3154347.
22. Qi, Q., Tao, F., A smart manufacturing service system based on edge computing, fog computing, and cloud computing. IEEE Access 7 (2019), 86769–86777, 10.1109/ACCESS.2019.2923610.
23. Giang, N.K., Blackstock, M., Lea, R., Leung, V.C., Developing IoT applications in the fog: A distributed dataflow approach. IEEE International Conference on the Internet of Things, 2015, 155–162.
24. Sinaeepourfard, A., Garcia, J., Masip-Bruin, X., Marin-Tordera, E., Data preservation through Fog-to-Cloud (F2C) data management in smart cities. IEEE International Conference on Fog and Edge Computing, 2018, 1–9.
25. Casadei, R., Viroli, M., Coordinating computation at the edge: a decentralized, self-organizing, spatial approach. IEEE International Conference on Fog and Mobile Edge Computing, 2019, 60–67.
26. Rabay'a, A., Schleicher, E., Graffi, K., Fog computing with p2p: Enhancing fog computing bandwidth for iot scenarios. International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2019, 82–89.
27. Karagiannis, V., Schulte, S., Comparison of alternative architectures in fog computing. 4th IEEE International Conference on Fog and Edge Computing, 2020, 19–28.
28. Bellendorf, J., Mann, Z.Á., Classification of optimization problems in fog computing. Future Gener. Comput. Syst. 107 (2020), 158–176, 10.1016/j.future.2020.01.036.
29. Hong, C., Varghese, B., Resource management in fog/edge computing: A survey on architectures, infrastructure, and algorithms. ACM Comput. Surv. 52 (2019), 97:1–97:37, 10.1145/3326066.
30. Arkian, H.R., Diyanat, A., Pourkhalili, A., MIST: Fog-based data analytics scheme with cost-efficient resource provisioning for IoT crowdsensing applications. J. Netw. Comput. Appl. 82 (2017), 152–165.
31. Skarlat, O., Karagiannis, V., Rausch, T., Bachmann, K., Schulte, S., A framework for optimization, service placement, and runtime operation in the fog. 11th IEEE/ACM International Conference on Utility and Cloud Computing, 2018, 164–173.
32. Zhu, C., Tao, J., Pastor, G., Xiao, Y., Ji, Y., Zhou, Q., Li, Y., Ylä-Jääski, A., Folo: Latency and quality optimized task allocation in vehicular fog computing. IEEE Internet Things J. 6 (2019), 4150–4161, 10.1109/JIOT.2018.2875520.
33. Gu, L., Zeng, D., Guo, S., Barnawi, A., Xiang, Y., Cost efficient resource management in fog computing supported medical cyber-physical system. IEEE Trans. Emerg. Top. Comput. 5 (2017), 108–119, 10.1109/TETC.2015.2508382.
34. Zeng, D., Gu, L., Guo, S., Cheng, Z., Yu, S., Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Trans. Comput. 65 (2016), 3702–3712.
35. Wan, J., Chen, B., Wang, S., Xia, M., Li, D., Liu, C., Fog computing for energy-aware load balancing and scheduling in smart factory. IEEE Trans. Ind. Inf. 14 (2018), 4548–4556.
36. Wang, Y., Wang, K., Huang, H., Miyazaki, T., Guo, S., Traffic and computation co-offloading with reinforcement learning in fog computing for industrial applications. IEEE Trans. Ind. Inf. 15 (2019), 976–986, 10.1109/TII.2018.2883991.
37. Jennings, B., Stadler, R., Resource management in clouds: Survey and research challenges. J. Netw. Syst. Manage. 23 (2015), 567–619.
38. Pop, P., Raagaard, M.L., Gutiérrez, M., Steiner, W., Enabling fog computing for industrial automation through Time-Sensitive Networking (TSN). IEEE Commun. Stand. Mag. 2 (2018), 55–61, 10.1109/MCOMSTD.2018.1700057.
39. Raagaard, M.L., Pop, P., Gutiérrez, M., Steiner, W., Runtime reconfiguration of time-sensitive networking (TSN) schedules for fog computing. 2017 IEEE Fog World Congress (FWC), 2017, 1–6.
40. Fizza, K., Auluck, N., Azim, A., Improving the schedubality of real-time tasks using fog computing. IEEE Trans. Serv. Comput., 2020.
41. Gomes, E., Dantas, M.A.R., Plentz, P.D.M., A real-time fog computing approach for healthcare environment. Xhafa, F., Leu, F., Ficco, M., Yang, C., (eds.) 13th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 2018, Springer, 85–95, 10.1007/978-3-030-02607-3_8.
42. Kopetz, H., Poledna, S., In-vehicle real-time fog computing. 2016 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshop, 2016, 162–167.
43. Ning, Z., Huang, J., Wang, X., Vehicular fog computing: Enabling real-time traffic management for smart cities. IEEE Wirel. Commun. 26 (2019), 87–93, 10.1109/MWC.2019.1700441.
44. Verma, P., Sood, S.K., Fog assisted-IoT enabled patient health monitoring in smart homes. IEEE Internet Things J. 5 (2018), 1789–1796, 10.1109/JIOT.2018.2803201.
45. Alcaraz, C., Secure interconnection of IT-OT networks in Industry 4.0. Critical Infrastructure Security and Resilience: Theories, Methods, Tools and Technologies, 2019, Springer, 201–217, 10.1007/978-3-030-00024-0_11.
46. Steiner, W., Poledna, S., Fog computing as enabler for the Industrial Internet of Things. Elektrotech. Inf.tech. 133 (2016), 310–314, 10.1007/s00502-016-0438-2.
47. Gogouvitis, S.V., Mueller, H., Premnadh, S., Seitz, A., Bruegge, B., Seamless computing in industrial systems using container orchestration. Future Gener. Comput. Syst. 109 (2020), 678–688, 10.1016/j.future.2018.07.033.
48. Mueller, H., Gogouvitis, S.V., Seitz, A., Bruegge, B., Seamless computing for industrial systems spanning cloud and edge. IEEE International Conference on High Performance Computing & Simulation, 2017, 209–216, 10.1109/HPCS.2017.40.
49. Meixner, S., Schall, D., Li, F., Karagiannis, V., Schulte, S., Plakidas, K., Automatic application placement and adaptation in cloud-edge environments. IEEE International Conference on Emerging Technologies and Factory Automation, 2019, 1001–1008, 10.1109/ETFA.2019.8869256.
50. Aazam, M., Zeadally, S., Harras, K.A., Deploying fog computing in Industrial Internet of Things and Industry 4.0. IEEE Trans. Ind. Inf. 14 (2018), 4674–4682, 10.1109/TII.2018.2855198.
51. Colelli, R., Panzieri, S., Pascucci, F., Securing connection between IT and OT: the Fog Intrusion Detection System prospective. IEEE Workshop on Metrology for Industry 4.0 and IoT, 2019, 444–448, 10.1109/METROI4.2019.8792884.
52. Li, L., Ota, K., Dong, M., Deep learning for smart industry: Efficient manufacture inspection system with fog computing. IEEE Trans. Ind. Inf. 14 (2018), 4665–4673, 10.1109/TII.2018.2842821.
53. O'donovan, P., Gallagher, C., Bruton, K., O'Sullivan, D.T., A fog computing industrial cyber-physical system for embedded low-latency machine learning Industry 4.0 applications. Manuf. Lett. 15 (2018), 139–142.
54. Fernández-Caramés, T.M., Fraga-Lamas, P., Suárez-Albela, M., Vilar-Montesinos, M., A fog computing and cloudlet based augmented reality system for the Industry 4.0 Shipyard. Sensors, 18, 2018, 10.3390/s18061798.
55. Zhou, Z., Hu, J., Liu, Q., Lou, P., Yan, J., Li, W., Fog computing-based cyber-physical machine tool system. IEEE Access 6 (2018), 44580–44590, 10.1109/ACCESS.2018.2863258.
56. FORA ETN project. Deliverable D5—Fog infrastructure reference architecture document. 2020 (accessed July 1, 2020). URL: http://www.fora-etn.eu/deliverables/.
57. Feiler, P.H., Lewis, B., Vestal, S., The SAE avionics architecture description language (AADL) standard: A basis for model-based architecture-driven embedded systems engineering., 2003, ARMY AVIATION AND MISSILE COMMAND REDSTONE ARSENAL AL.
58. Team, S.A., et al. An Extensible Open Source AADL Tool Environment (OSATE). 2006, Software Engineering Institute.
59. Wang, Y., Ma, D., Zhao, Y., Zou, L., Zhao, X., An aadl-based modeling method for arinc653-based avionics software. IEEE 35th Annual Computer Software and Applications Conference, 2011, 224–229.
60. V. Gavrilut, B. Zarrin, P. Pop, S. Samii, Fault-tolerant topology and routing synthesis for ieee time-sensitive networking, in: Proceedings of the 25th International Conference on Real-Time Networks and Systems, 2017, pp. 267–276.
61. Barzegaran, M., Zarrin, B., Pop, P., Quality-of-control-aware scheduling of communication in TSN-based fog computing platforms using constraint programming. 2nd Workshop on Fog Computing and the IoT, 2020, 3:1–3:9.
62. Steinberg, D., Budinsky, F., Merks, E., Paternostro, M., EMF: Eclipse Modeling Framework. 2008, Pearson Education.
63. TTTech Computertechnik AG. Nerve. 2020 (accessed July 1, 2020). https://www.tttech-industrial.com/products/nerve/.
64. Sandström, K., Vulgarakis, A., Lindgren, M., Nolte, T., Virtualization technologies in embedded real-time systems. IEEE 18th Conference on Emerging Technologies & Factory Automation, 2013, 1–8.
65. Kaiser, R., Wagner, S., The pikeos concept: History and design: SysGO AG White Paper., 2007 Available: http://www.sysgo.com.
66. Stanford-Clark, A., Truong, H.L., MQTT for Sensor Networks (MQTT-SN) Protocol Specification: International business machines (IBM) Corporation Version 1, 2., 2013.
67. Shelby, Z., Hartke, K., Bormann, C., The constrained application protocol (CoAP). 2014.
68. P. Danielis, J. Skodzik, V. Altmann, E.B. Schweissguth, F. Golatowski, D. Timmermann, J. Schacht, Survey on real-time communication via ethernet in industrial automation environments, in: Proceedings of the IEEE Emerging Technology and Factory Automation, 2014, pp. 1–8.
69. Schoeberl, M., Schleuniger, P., Puffitsch, W., Brandner, F., Probst, C.W., Karlsson, S., Thorn, T., Towards a time-predictable dual-issue microprocessor: The patmos approach. Workshop on Bringing Theory to Practice: Predictability and Performance in Embedded Systems, 2011, OASICS, 11–21.
70. S.S. Craciunas, R.S. Oliver, T. AG, An overview of scheduling mechanisms for time-sensitive networks, in: Proceedings of the Real-Time Summer School LÉcole dÉté Temps Réel, 2017, pp. 1551–3203.
71. Adame, T., Carrascosa, M., Bellalta, B., Time-sensitive networking in IEEE 802.11be: On the way to low-latency WiFi 7. 2019 arXiv preprint arXiv:1912.06086.
72. 3rd Generation Partnership Project (3GPP). 5G system (5GS); Time-Sensitive Networking (TSN) Application Function (AF) to Device-Side TSN Translator (DS-TT) and Network-Side TSN Translator (NW-TT) protocol aspects; Stage 3. 2019 Release 16.
73. Rajandekar, A., Sikdar, B., A survey of MAC layer issues and protocols for machine-to-machine communications. IEEE Internet Things J. 2 (2015), 175–186.
74. Vaezi, M., Schober, R., Ding, Z., Poor, H.V., Non-orthogonal multiple access: Common myths and critical questions. IEEE Wirel. Commun. 26 (2019), 174–180.
75. Karagiannis, V., Compute node communication in the fog: Survey and research challenges. Workshop on Fog Computing and the IoT, 2019, 36–40.
76. Chiang, M., Ha, S., Chih-Lin, I., Risso, F., Zhang, T., Clarifying fog computing and networking: 10 questions and answers. IEEE Commun. Mag. 55 (2017), 18–20, 10.1109/MCOM.2017.7901470.
77. Alt, B., Weckesser, M., Becker, C., Hollick, M., Kar, S., Klein, A., Klose, R., Kluge, R., Koeppl, H., Koldehofe, B., KhudaBukhsh, W.R., Luthra, M., Mousavi, M., Mühlhäuser, M., Pfannemüller, M., Rizk, A., Schürr, A., Steinmetz, R., Transitions: A protocol-independent view of the future internet. Proc. IEEE 107 (2019), 835–846, 10.1109/JPROC.2019.2895964.
78. Crespí, R.S., Armentia, A., Sarachaga, I., Casquero, O., Pérez, F., Marcos, M., OPC UA aggregation server in the fog. 24th IEEE International Conference on Emerging Technologies and Factory Automation, 2019, 1256–1260, 10.1109/ETFA.2019.8869358.
79. Pardo-Castellote, G., OMG data-distribution service: Architectural overview. 23rd International Conference on Distributed Computing Systems Workshops, 2003, 200–206, 10.1109/ICDCSW.2003.1203555.
80. Pahl, C., Brogi, A., Soldani, J., Jamshidi, P., Cloud container technologies: a state-of-the-art review. IEEE Trans. Cloud Comput., 2017.
81. Mavridis, I., Karatza, H.D., Combining containers and virtual machines to enhance isolation and extend functionality on cloud computing. Future Gener. Comput. Syst. 94 (2019), 674–696, 10.1016/j.future.2018.12.035.
82. Barzegaran, M., Cervin, A., Pop, P., Performance optimization of control applications on fog computing platforms using scheduling and isolation. IEEE Access 8 (2020), 104085–104098.
83. Kadar, M., Tverdyshev, S., Fohler, G., System calls instrumentation for intrusion detection in embedded mixed-criticality systems. 4th International Workshop on Security and Dependability of Critical Embedded Real-Time Systems, 2019, Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik.
84. Avasalcai, C., Dustdar, S., Latency-aware distributed resource provisioning for deploying IoT applications at the edge of the network. Advances in Information and Communication, 2020, Springer International Publishing, Cham, 377–391.
85. Karagiannis, V., Desai, N., Schulte, S., Punnekkat, S., Addressing the node discovery problem in fog computing. 2nd Workshop on Fog Computing and the IoT, 2020, Schloss Dagstuhl-Leibniz-Zentrum für Informatik.
86. Karagiannis, V., Schulte, S., Leitão, J., Preguiça, N., Enabling fog computing using self-organizing compute nodes. IEEE 3rd International Conference on Fog and Edge Computing, 2019, 1–10.
87. Group, O.M., About the OPC-UA/DDS gateway specification. 2020 (accessed July 1, 2020). https://www.omg.org/spec/DDS-OPCUA/About-DDS-OPCUA/.
88. Bini, E., Buttazzo, G., Eker, J., Schorr, S., Guerra, R., Fohler, G., Arzen, K.E., Romero, V., Scordino, C., Resource management on multicore systems: The actors approach. IEEE Micro 31 (2011), 72–81.
89. Cheminod, M., Durante, L., Valenzano, A., Review of security issues in industrial networks. IEEE Trans. Ind. Inf. 9 (2013), 277–293, 10.1109/TII.2012.2198666.
90. Yi, S., Qin, Z., Li, Q., Security and privacy issues of fog computing: A survey. Xu, K., Zhu, H., (eds.) Wireless Algorithms, Systems, and Applications, 2015, Springer International Publishing, Cham, 685–695.
91. McKinsey & Company. Industry 4.0 How to navigate digitization of the manufacturing sector. 2020 (accessed July 1, 2020). https://www.mckinsey.de/files/mck_industry_40_report.pdf.
92. Adali, T., Miller, D.J., Diamantaras, K.I., Larsen, J., Trends in machine learning for signal processing [in the spotlight]. IEEE Signal Process. Mag. 28 (2011), 193–196, 10.1109/MSP.2011.942319.
93. Pahl, C., Containerization and the PaaS cloud. IEEE Cloud Comput. 2 (2015), 24–31, 10.1109/MCC.2015.51.
94. Lear, E., Droms, R., Romascanu, D., RFC 8520: Manufacturer usage description specification. Internet Engineering Task Force, 2019 March.
95. Desai, N., Punnekkat, S., Enhancing fault detection in time sensitive networks using machine learning. 12th International Conference on COMmunication Systems & NETworkS, 2020, 714–719.
96. Qian, J., Sengupta, S., Hansen, L.K., Active learning solution on distributed edge computing. 2019 arXiv preprint arXiv:1906.10718.
97. Qian, J., Gochhayat, S.P., Hansen, L.K., Distributed active learning strategies on edge computing. 6th IEEE International Conference on Cyber Security and Cloud Computing/5th IEEE International Conference on Edge Computing and Scalable Cloud, 2019, 221–226.
98. Qian, J., Hansen, L.K., Fafoutis, X., Tiwari, P., Pandey, H.M., Robustness analytics to data heterogeneity in edge computing. Comput. Commun. 164 (2020), 229–239.
99. Buttazzo, G.C., Hard Real-Time Computing Systems: Predictable Scheduling Algorithms and Applications. Vol. 24. 2011, Springer Science & Business Media.
100. Boldea, I., Nasar, S.A., Electric Drives. 2016, CRC press.
101. Storey, N.R., Safety Critical Computer Systems. 1996, Addison-Wesley Longman Publishing Co., Inc.
102. A. Cervin, P. Pazzaglia, M. Barzegaran, R. Mahfouzi, Using JitterTime to analyze transient performance in adaptive and reconfigurable control systems, in: Proc. of IEEE International Conference on Emerging Technologies and Factory Automation, 2019, pp. 1025–1032.
103. M. Barzegaran, V. Karagiannis, C. Avasalcai, P. Pop, S. Schulte, S. Dustdar, Towards extensibility-aware scheduling of industrial applications on fog nodes, in: Proc. of 4th IEEE International Conference on Edge Computing, 2020b, pp. 1–9.
104. Reusch, N., Craciunas, S.S., Pop, P., Towards Security-Aware Scheduling for TSN-Based Distributed Cyber-Physical Systems: Technical Report., 2020, Technical University of Denmark.