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Uç/Sis Bilişim Kullanarak Verileri İzlemeye Yönelik Veri Merkezi Ağ Topolojileri Üzerine Çalışma

Year 2024, EARLY VIEW, 1 - 1

Pedro Juan Roig Salvador Alcaraz Katja Gılly Cristina Bernad Carlos Juiz

https://doi.org/10.2339/politeknik.1327987

Abstract

Uygun bir veri merkezi ağ topolojisinin seçilmesi, sorun teşhisi ve önlenmesi gerçekleştirmek amacıyla herhangi bir uzaktan bilişim ortamından elde edilen sensör verilerinin birleştirilmesi için gözetim ve izleme süreçleriyle uğraşırken çok önemlidir. Bu çalışmada, Switch merkezli veya sunucu merkezli olanları temsil eden uç/sis bilişime bağlı bir veri merkezi içindeki bileşenleri birbirine bağlamak için en yaygın kullanılan topolojilerden bazılarının performanslarını ölçerek gözden geçirilmiş ve istatistiksel bir bakış açısıyla analiz edilmiştir ve sonuç olarak sunucu merkezli olanların daha iyi performans gösterdiği bulunmuştur.

Keywords

Uç/Sis Bilişim Sorun Teşhisi ve Tahmini Endüstri 4.0/5.0 Nesnelerin İnterneti Sensör Veri Füzyonu

References

  • [1] Sharma A., Singh B.J. Evolution of Industrial Revolutions: A Review. International Journal of Innovative Technology and Exploring Engineering, 9(11): 66–73, (2020).
  • [2] Bula P., Niedzielski B. Industrial revolution – from Industry 1.0 to Industry 4.0. Management, Organisations and Artificial Intelligence, chapter 1, Routledge, London, UK, (2021).
  • [3] Kjelsrud, J. A Secure Transition from Industry 3.0 to Industry 4.0 for Manufactures. Recommendations from a security perspective. Master's Degree Thesis in Informatics, University of Oslo, Norway, (2022).
  • [4] Xu X., Lu Y., Vogel-Heuser B, Wang L. Industry 4.0 and Industry 5.0 - Inception, conception and perception. Journal of Manufacturing Systems, 61: 530–535, (2021).
  • [5] Chourasia S., Tyagi A., Pandey S.M., Walia R.S., Murtaza Q. Sustainability of Industry 6.0 in Global Perspective: Benefits and Challenges. Journal of Metrology Society of India, 37(2): 443–452, (2022).
  • [6] Yang F, Gu S. Industry 4.0, a revolution that requires technology and national strategies. Complex & Intelligent Systems, 7: 1311–1325, (2021).
  • [7] Javaid M., Haleem A., Singh R.P., Suman R, Santibáñez-González E. Understanding the adoption of Industry 4.0 technologies in improving environmental sustainability. Sustainable Operations and Computers, 3: 203–217, (2022).
  • [8] Alberro L., Castro A., Grampin E. Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review. Future Internet, 14(1): 0029, (2022).
  • [9] Couto R.S. et al. Reliability and Survivability Analysis of Data Center Network Topologies. Journal of Network and Systems Management, 24: 346–392, (2016).
  • [10] Cortés-Castillo A. Various Network Topologies and an Analysis Comparative Between Fat-Tree and BCube for a Data Center Network: An Overview. Proceedings of the IEEE Cloud Summit, Fairfax, VA, USA, (2022).
  • [11] Alqahtani J., Hamdaoui B. Rethinking Fat-Tree Topology Design for Cloud Data Centers. Proceedings of IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates, (2018).
  • [12] Han X. et al. Study of data center communication network topologies using complex network propagation model. Frontiers in Physics, 11: 1174099, (2023).
  • [13] Negara E.S., Keni K., Andryni R. BCube and DCell Topology Data Center Infrastructures Performance. IOP Conference Series: Materials Science and Engineering, 852: 012129, (2019).
  • [14] Lin W., Li X.Y., Chang J.M., Jia X. Constructing Multiple CISTs on BCube-Based Data Center Networks in the Occurrence of Switch Failures. IEEE Transactions on Computers, 72: 1971–1984, (2023).
  • [15] Ahmed R.E, Helal H. New Fault-Tolerant Datacenter Network Topologies. Journal of Communications, 13(6): 259–265, (2018).
  • [16] Shilenge M.C., Telukdarie A. Optimization of Operational and Information Technology Integration Towards Industry 4.0. Proceedings of IEEE 31st International Symposium on Industrial Electronics (ISIE), Anchorage, AK, USA, (2022).
  • [17] Felser M., Rentschler M., Kleineberg O. Coexistence Standardization of Operation Technology and Information Technology. Proceedings of the IEEE, 107(6): 962–976, (2019).
  • [18] Kuppusamy E., Mariappan K. Integration of Operation Technology (OT) and Information Technology (IT) Through Intelligent Automation in Manufacturing Industries. Advances in Manufacturing Technology XXXIV, chapter 42, IOS Press, Amsterdam, The Netherlands, (2021).
  • [19] Kebande V.R. Industrial internet of things (IIoT) forensics: The forgotten concept in the race towards industry 4.0. Forensic Science International: Reports, 5: 100257, (2022).
  • [20] Murray G., Johnstone M.N., Valli C. The convergence of IT and OT in critical infrastructure. Proceedings of 15th Australian Information Security Management Conference, 2017, Perth, Western Australia, (2017).
  • [21] Dos Santos D.R., Dagrada M., Costante E. Leveraging operational technology and the Internet of things to attack smart buildings. Journal of Computer Virology and Hacking Techniques, 17: 1–20, (2021).
  • [22] Sonkor M.S., García de Soto B. Operational Technology on Construction Sites: A Review from the Cybersecurity Perspective. Journal of Construction Engineering and Management, 147(12): 04021172, (2021).
  • [23] Klinc R., Turk Z. Construction 4.0 - Digital Transformation of One of the Oldest Industries. Economic and Business Review, 21(3): 0092 (2019).
  • [24] Vogel F., Hamann, H. Legal Linguistics in Times of Language Models and Text Automation. International Journal of Language & Law, 12: 1–7, (2023).
  • [25] Thoppilan R. et al. LaMDA: Language Models for Dialog Applications. arXiv, 2201.08239v3: 1–47, (2022).
  • [26] Darici M.B., “Performance Analysis of Combination of CNN-based Models with Adaboost Algorithm to Diagnose Covid-19 Disease”, Journal of Polytechnic, 26(1): 179–190, (2023).
  • [27] Akundi A. et al. State of Industry 5.0 - Analysis and Identification of Current Research Trends. Applied System Innovation, 5(1): 0027, (2022).
  • [28] Bonyuet D. Overview and Impact of Blockchain on Auditing. The International Journal of Digital Accounting Research: 20, 31-43, (2020).
  • [29] Ferrer-Sapena A., Sánchez-Pérez E.A. Applications of blockchain technology in scientific documentation: current situation and perspectives. El profesional de la información, 28(2): e280210, (2019).
  • [30] Bahga A., Madisetti V.K. Blockchain Platform for Industrial Internet of Things. Journal of Software Engineering and Applications, 9(10): 910036, (2016).
  • [31] Khan S.N., Loukil F., Ghedira-Guegan C., Benkhelifa E., Bani-Hani A. Blockchain smart contracts: Applications, challenges, and future trends. Peer-to-Peer Networking and Applications, 14: 2901–2925, (2021).
  • [32] Wang Q., Zhu X., Ni Y., Gu L., Zhu H. Blockchain for the IoT and industrial IoT: A review. Internet of Things, 10: 100081, (2020).
  • [33] Kumar R.L., Khan F., Kadry F., Rho S. A Survey on blockchain for industrial Internet of Things. Alexandria Engineering Journal, 61(8): 6001–6022, (2022).
  • [34] Lucio Y.LL., Villalba K.M., Donado S.A. Adaptive Blockchain Technology for a Cybersecurity Framework in IIoT. IEEE Revista Iberoamericana de Tecnologías del Aprendizaje, 17(2): 178 –184, (2022).
  • [35] Siegfried N., Rosenthal T., Benlian A. Blockchain and the Industrial Internet of Things: A requirement taxonomy and systematic fit analysis. Journal of Enterprise Information Management, 35(6): 1454–1476, (2022).
  • [36] Latif S., Idrees Z., Huma Z., Ahmad J. Blockchain technology for the industrial Internet of Things: A comprehensive survey on security challenges, architectures, applications, and future research directions. Transactions on Emerging Telecommunications Technologies, 32(11): e4337, (2021).
  • [37] Dwivedi S.K., Roy P., Karda C., Agrawal S., Amin R. Blockchain-Based Internet of Things and Industrial IoT: A Comprehensive Survey. Security and Communication Networks, 2021: 7142048, (2021).
  • [38] Wang G., Shi Z., Nixon M., Han S. ChainSplitter: Towards Blockchain-Based Industrial IoT Architecture for Supporting Hierarchical Storage. Proceedings of IEEE International Conference on Blockchain, Atlanta, GA, USA, (2019).
  • [39] Xu X, Zheng Z, Yang S, Shao H. A Novel Blockchain Framework for Industrial IoT Edge Computing. Sensors, 20(7): 2061, (2020).
  • [40] Singh A., Raghav S., Senger P.K., Kumar A. Blockchain and Edge computing for Industrial Internet of Things (IIoT) Applications. Asian Journal of Convergence in Technology, 8(1): 0016, (2022).
  • [41] Greeshma P.P, Rajashekar J.S. Blockchain for Industrial Internet of Things. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 10(IX): 0147, (2022).
  • [42] Kumar K.S., Kumar V.K., Ilamparithi T., Prabhu S.R.B., Kumar R.D. Emerging Trends and Research Issues on Blockchain Technology for 5G-Enabled Industrial IoT. Blockchain Technology, chapter 13, 1st edition, CRC Press, Boca Raton, FL, USA, (2020).
  • [43] Sharma R. Blockchain for Industrial Internet of Things (IIoT). Blockchain and AI Technology in the Industrial Internet of Things, chapter 3, 1st edition, IGI Global, Hershey, PA, USA, (2021).
  • [44] Adhikari N., Ramkumar M. IoT and Blockchain Integration: Applications, Opportunities, and Challenges. Network, 3(1): 115–141, (2023).
  • [45] Sandner P., Gross J., Richter R. Convergence of Blockchain, IoT, and AI. Frontiers in Blockchain, 3, 522600, (2020).
  • [46] Zhao S., Li S., Yao Y. Blockchain Enabled Industrial Internet of Things Technology. IEEE Transactions on Computational Social Systems, 6(6): 1442–1453, (2019).
  • [47] Baalamurugan K.M. et al. Blockchain-enabled K-harmonic framework for industrial IoT-based systems. Scientific Reports, 13: 1004, (2023).
  • [48] Alladi T., Chamola V., Parizi R.M., Choo K.K. Blockchain Applications for Industry 4.0 and Industrial IoT: A Review. IEEE Access, 7: 176935–176951, (2019).
  • [49] Adeyeri M.K. From Industry 3.0 to Industry 4.0: Smart Predictive Maintenance System as Platform for Leveraging. Arctic Journal, 71(11): 64–81, (2018).
  • [50] Doraiswami R., Cheded L. Fault Detection and Isolation. Fault Diagnosis and Detection (FDD), 1st edition, IntechOpen, London, UK, (2017).
  • [51] Bagchi S., Cheng Q.S. Computational modeling of consistent observation of asynchronous distributed computation on N–manifold. Cogent Engineering, 5(1): 1528029, (2018).
  • [52] Zhang Y., Luo L., Ji X., Dai Y. Improved Random Forest Algorithm Based on Decision Paths for Fault Diagnosis of Chemical Process with Incomplete Data. Sensors, 21(20): 6715, (2021).
  • [53] Durrant-Whyte H., Henderson T.C. Multisensor Data Fusion. Handbook of Robotics. Springer, Berlin, Heidelberg, 585–610, (2008).
  • [54] Naqvi R.A., Arsalan M., Qaiser T., Khan T.M., Razzak I. Sensor Data Fusion Based on Deep Learning for Computer Vision Applications and Medical Applications. Sensors, 22(20): 8058, (2022).
  • [55] Lee S.K., Hong S.H., Jun W.H., Hong Y.S. Multi-Sensor Data Fusion with a Reconfigurable Module and Its Application to Unmanned Storage Boxes. Sensors, 22(14): 5388, (2022).
  • [56] https://www.thinkautonomous.ai/blog/9-types-of-sensor-fusion-algorithms/, “9 types of sensor fusion algorithms” (2021), accessed on July 7th, 2023.
  • [57] Yadav P., Mishra A., Kim S. A Comprehensive Survey on Multi-Agent Reinforcement Learning for Connected and Automated Vehicles. Sensors, 23(10): 4710 (2023).
  • [58] Castanedo F. A Review of Data Fusion Techniques. The Scientific World Journal, 2013: 704504, (2013).
  • [59] https://www.bosch-mobility-solutions.com/en/solutions/sensors/sensor-data-fusion/, “Sensor data fusion” (2023), accessed on July 7th, 2023.
  • [60] Aroulanandam V.V. et al. Sensor data fusion for optimal robotic navigation using regression based on an IOT system. Measurement: Sensors, 24: 100598, (2022).
  • [61] Varghese J.P., Sundaramoorthy K., Sankaran A. Development and Validation of a Load Flow Based Scheme for Optimum Placing and Quantifying of Distributed Generation for Alleviation of Congestion in Interconnected Power Systems. Energies, 16(6): 2536, (2023).
  • [62] Zonta T. et al. Predictive maintenance in the Industry 4.0: A systematic literature review. Computers & Industrial Engineering, 150: 106889, (2020).
  • [63] Kashinath S.A. et al. Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis. IEEE Access, 9: 51258–51276, (2021).
  • [64] Karaahmetoglu E., Ersöz S., Türker A.K., Ates V. and Inal A.F., “Evaluation of Profession Predictions for Today and the Future with Machine Learning Methods: Emperical Evidence From Turkey”, Journal of Polytechnic, 26(1): 107–124, (2023).
  • [65] Achouch M. et al. On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges. Applied Sciences, 12(16): 8081, (2022).
  • [66] Roig P.J., Alcaraz S., Gilly K., Bernad C., Juiz C. Modeling an edge computing arithmetic framework for IoT. Sensors, 22(3): 1084 (2022).
  • [67] Roig P.J., Alcaraz S., Gilly K., Bernad C., Juiz C. Arithmetic Framework to Optimize Packet Forwarding among End Devices in Generic Edge. Sensors, 22(2): 421 (2022).
  • [68] Sunyaev A. Fog and Edge Computing. Internet Computing, chapter 8, Springer, Cham, Switzerland, (2020).
  • [69] Roig P.J., Alcaraz S., Gilly K., Bernad C., Juiz C. Modeling of a Generic Edge Computing Application Design. Sensors, 21(21): 7276 (2021).
  • [70] Firouzi F., Farahani B., Panahi E., Barzegari M. Task Offloading for Edge-Fog-Cloud Interplay in the Healthcare Internet of Things (IoT). Proceedings of IEEE International Conference on Omni-Layer Intelligent Systems (COINS), Barcelona, Spain, (2021).
  • [71] Roig P.J., Alcaraz S., Gilly K., Bernad C., Juiz C. Formal Algebraic Model of an Edge Data Center with a Redundant Ring Topology. Network, 3(1): 142–157 (2023).
  • [72] Roig P.J. Formal Algebraic Modelling of a Fog Computer Network Architecture. PhD Thesis in Information and Communication Technologies, University of the Balearic Islands, Spain, (2022).
  • [73] Al-Makhlafi M., Gu H., Yu X., Lu Y. P-Cube: A New Two-Layer Topology for Data Center Networks Exploiting Dual-Port Servers. IEICE Transactions on Communications, E103-B(9): 940–950, (2020).
  • [74] Cortés-Castillo, A. Various Network Topologies and an Analysis Comparative Between Fat-Tree and BCube for a Data Center Network: An Overview. Proceeding of IEEE Cloud Summit, Fairfax, VA, USA, (2022).
  • [75] Roig P.J., Alcaraz S., Gilly K., Bernad C., Juiz C. Arithmetic Study about Efficiency in Network Topologies for Data Centers. Network, 3(3): 298–325, (2023).
  • [76] Roig P.J., Alcaraz S., Gilly K., Juiz C. Arithmetic Study about Energy Save in Switches for some Data Centre Topologies. Journal of Polytechnic, 25(2): 785–797 (2022).
  • [77] Roig P.J., Alcaraz S., Gilly K., Bernad C., Juiz C. Edge Data Center Organization and Optimization by Using Cage Graphs. Network, 3(1): 93–114, (2023).
  • [78] Al-Fares M., Loukissas A., Vahdat A. A Scalable, Commodity Data Center Network Architecture. Proceedings of SIGCOMM 2008, Seattle, WA, USA, (2008).
  • [79] Alizadeh M., Edsall T. On the Data Path Performance of Leaf-Spine Datacenter Fabrics. Proceedings of IEEE 21st Annual Symposium on High-Performance Interconnects, San Jose, CA, USA, (2013).
  • [80] Guo C. et al. BCube: A High Performance, Server-centric Network Architecture for Modular Data Centers. Proceedings of SIGCOMM 2009, Barcelona, Spain, (2009).
  • [81] Guo C. et al. DCell: A Scalable and Fault-Tolerant Network Structure for Data Centers. Proceedings of SIGCOMM 2008, Seattle, WA, USA, (2008).
  • [82] Li D., Guo C., Wu H., Tan K., Zhang Y., Lu S. FiConn: Using Backup Port for Server Interconnection in Data Centers. Proceedings of INFOCOM 2009, Rio de Janeiro, Brazil, (2009).
  • [83] Roig P.J., Alcaraz S., Gilly K., Bernad C., Juiz C. Features of data center network topologies fit for IIoT deployments. Advances and Challenges in Science and Technology, 9: 29–48 (2023).
  • [84] Bornholdt H., Röbert K., Breitbach M., Fischer M., Edinger J. Measuring the Edge: A Performance Evaluation of Edge Offloading. Proceedings of IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), Atlanta, GA, USA, (2023).

Study on Data Center Network Topologies for Monitoring Data using Edge/Fog Computing

Year 2024, EARLY VIEW, 1 - 1

Pedro Juan Roig Salvador Alcaraz Katja Gılly Cristina Bernad Carlos Juiz

https://doi.org/10.2339/politeknik.1327987

Abstract

The election of an appropriate data center network topology is key when dealing with surveillance and monitoring processes, such as those devoted to obtaining relevant data for sensor data fusion in any type of remote computing environment so as to perform fault diagnosis and prevention. In this paper, some of the most commonly used topologies to interconnect nodes within a data center bound to edge/fog computing, representing either switch-centric ones or server-centric ones, are reviewed and analyzed from a statistical point of view in order to measure their performance, resulting in server-centric ones doing it better.

Keywords

Edge/Fog Computing Fault Diagnosis and Prediction Industry 4.0/5.0 Internet of Things Sensor Data Fusion

References

  • [1] Sharma A., Singh B.J. Evolution of Industrial Revolutions: A Review. International Journal of Innovative Technology and Exploring Engineering, 9(11): 66–73, (2020).
  • [2] Bula P., Niedzielski B. Industrial revolution – from Industry 1.0 to Industry 4.0. Management, Organisations and Artificial Intelligence, chapter 1, Routledge, London, UK, (2021).
  • [3] Kjelsrud, J. A Secure Transition from Industry 3.0 to Industry 4.0 for Manufactures. Recommendations from a security perspective. Master's Degree Thesis in Informatics, University of Oslo, Norway, (2022).
  • [4] Xu X., Lu Y., Vogel-Heuser B, Wang L. Industry 4.0 and Industry 5.0 - Inception, conception and perception. Journal of Manufacturing Systems, 61: 530–535, (2021).
  • [5] Chourasia S., Tyagi A., Pandey S.M., Walia R.S., Murtaza Q. Sustainability of Industry 6.0 in Global Perspective: Benefits and Challenges. Journal of Metrology Society of India, 37(2): 443–452, (2022).
  • [6] Yang F, Gu S. Industry 4.0, a revolution that requires technology and national strategies. Complex & Intelligent Systems, 7: 1311–1325, (2021).
  • [7] Javaid M., Haleem A., Singh R.P., Suman R, Santibáñez-González E. Understanding the adoption of Industry 4.0 technologies in improving environmental sustainability. Sustainable Operations and Computers, 3: 203–217, (2022).
  • [8] Alberro L., Castro A., Grampin E. Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review. Future Internet, 14(1): 0029, (2022).
  • [9] Couto R.S. et al. Reliability and Survivability Analysis of Data Center Network Topologies. Journal of Network and Systems Management, 24: 346–392, (2016).
  • [10] Cortés-Castillo A. Various Network Topologies and an Analysis Comparative Between Fat-Tree and BCube for a Data Center Network: An Overview. Proceedings of the IEEE Cloud Summit, Fairfax, VA, USA, (2022).
  • [11] Alqahtani J., Hamdaoui B. Rethinking Fat-Tree Topology Design for Cloud Data Centers. Proceedings of IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates, (2018).
  • [12] Han X. et al. Study of data center communication network topologies using complex network propagation model. Frontiers in Physics, 11: 1174099, (2023).
  • [13] Negara E.S., Keni K., Andryni R. BCube and DCell Topology Data Center Infrastructures Performance. IOP Conference Series: Materials Science and Engineering, 852: 012129, (2019).
  • [14] Lin W., Li X.Y., Chang J.M., Jia X. Constructing Multiple CISTs on BCube-Based Data Center Networks in the Occurrence of Switch Failures. IEEE Transactions on Computers, 72: 1971–1984, (2023).
  • [15] Ahmed R.E, Helal H. New Fault-Tolerant Datacenter Network Topologies. Journal of Communications, 13(6): 259–265, (2018).
  • [16] Shilenge M.C., Telukdarie A. Optimization of Operational and Information Technology Integration Towards Industry 4.0. Proceedings of IEEE 31st International Symposium on Industrial Electronics (ISIE), Anchorage, AK, USA, (2022).
  • [17] Felser M., Rentschler M., Kleineberg O. Coexistence Standardization of Operation Technology and Information Technology. Proceedings of the IEEE, 107(6): 962–976, (2019).
  • [18] Kuppusamy E., Mariappan K. Integration of Operation Technology (OT) and Information Technology (IT) Through Intelligent Automation in Manufacturing Industries. Advances in Manufacturing Technology XXXIV, chapter 42, IOS Press, Amsterdam, The Netherlands, (2021).
  • [19] Kebande V.R. Industrial internet of things (IIoT) forensics: The forgotten concept in the race towards industry 4.0. Forensic Science International: Reports, 5: 100257, (2022).
  • [20] Murray G., Johnstone M.N., Valli C. The convergence of IT and OT in critical infrastructure. Proceedings of 15th Australian Information Security Management Conference, 2017, Perth, Western Australia, (2017).
  • [21] Dos Santos D.R., Dagrada M., Costante E. Leveraging operational technology and the Internet of things to attack smart buildings. Journal of Computer Virology and Hacking Techniques, 17: 1–20, (2021).
  • [22] Sonkor M.S., García de Soto B. Operational Technology on Construction Sites: A Review from the Cybersecurity Perspective. Journal of Construction Engineering and Management, 147(12): 04021172, (2021).
  • [23] Klinc R., Turk Z. Construction 4.0 - Digital Transformation of One of the Oldest Industries. Economic and Business Review, 21(3): 0092 (2019).
  • [24] Vogel F., Hamann, H. Legal Linguistics in Times of Language Models and Text Automation. International Journal of Language & Law, 12: 1–7, (2023).
  • [25] Thoppilan R. et al. LaMDA: Language Models for Dialog Applications. arXiv, 2201.08239v3: 1–47, (2022).
  • [26] Darici M.B., “Performance Analysis of Combination of CNN-based Models with Adaboost Algorithm to Diagnose Covid-19 Disease”, Journal of Polytechnic, 26(1): 179–190, (2023).
  • [27] Akundi A. et al. State of Industry 5.0 - Analysis and Identification of Current Research Trends. Applied System Innovation, 5(1): 0027, (2022).
  • [28] Bonyuet D. Overview and Impact of Blockchain on Auditing. The International Journal of Digital Accounting Research: 20, 31-43, (2020).
  • [29] Ferrer-Sapena A., Sánchez-Pérez E.A. Applications of blockchain technology in scientific documentation: current situation and perspectives. El profesional de la información, 28(2): e280210, (2019).
  • [30] Bahga A., Madisetti V.K. Blockchain Platform for Industrial Internet of Things. Journal of Software Engineering and Applications, 9(10): 910036, (2016).
  • [31] Khan S.N., Loukil F., Ghedira-Guegan C., Benkhelifa E., Bani-Hani A. Blockchain smart contracts: Applications, challenges, and future trends. Peer-to-Peer Networking and Applications, 14: 2901–2925, (2021).
  • [32] Wang Q., Zhu X., Ni Y., Gu L., Zhu H. Blockchain for the IoT and industrial IoT: A review. Internet of Things, 10: 100081, (2020).
  • [33] Kumar R.L., Khan F., Kadry F., Rho S. A Survey on blockchain for industrial Internet of Things. Alexandria Engineering Journal, 61(8): 6001–6022, (2022).
  • [34] Lucio Y.LL., Villalba K.M., Donado S.A. Adaptive Blockchain Technology for a Cybersecurity Framework in IIoT. IEEE Revista Iberoamericana de Tecnologías del Aprendizaje, 17(2): 178 –184, (2022).
  • [35] Siegfried N., Rosenthal T., Benlian A. Blockchain and the Industrial Internet of Things: A requirement taxonomy and systematic fit analysis. Journal of Enterprise Information Management, 35(6): 1454–1476, (2022).
  • [36] Latif S., Idrees Z., Huma Z., Ahmad J. Blockchain technology for the industrial Internet of Things: A comprehensive survey on security challenges, architectures, applications, and future research directions. Transactions on Emerging Telecommunications Technologies, 32(11): e4337, (2021).
  • [37] Dwivedi S.K., Roy P., Karda C., Agrawal S., Amin R. Blockchain-Based Internet of Things and Industrial IoT: A Comprehensive Survey. Security and Communication Networks, 2021: 7142048, (2021).
  • [38] Wang G., Shi Z., Nixon M., Han S. ChainSplitter: Towards Blockchain-Based Industrial IoT Architecture for Supporting Hierarchical Storage. Proceedings of IEEE International Conference on Blockchain, Atlanta, GA, USA, (2019).
  • [39] Xu X, Zheng Z, Yang S, Shao H. A Novel Blockchain Framework for Industrial IoT Edge Computing. Sensors, 20(7): 2061, (2020).
  • [40] Singh A., Raghav S., Senger P.K., Kumar A. Blockchain and Edge computing for Industrial Internet of Things (IIoT) Applications. Asian Journal of Convergence in Technology, 8(1): 0016, (2022).
  • [41] Greeshma P.P, Rajashekar J.S. Blockchain for Industrial Internet of Things. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 10(IX): 0147, (2022).
  • [42] Kumar K.S., Kumar V.K., Ilamparithi T., Prabhu S.R.B., Kumar R.D. Emerging Trends and Research Issues on Blockchain Technology for 5G-Enabled Industrial IoT. Blockchain Technology, chapter 13, 1st edition, CRC Press, Boca Raton, FL, USA, (2020).
  • [43] Sharma R. Blockchain for Industrial Internet of Things (IIoT). Blockchain and AI Technology in the Industrial Internet of Things, chapter 3, 1st edition, IGI Global, Hershey, PA, USA, (2021).
  • [44] Adhikari N., Ramkumar M. IoT and Blockchain Integration: Applications, Opportunities, and Challenges. Network, 3(1): 115–141, (2023).
  • [45] Sandner P., Gross J., Richter R. Convergence of Blockchain, IoT, and AI. Frontiers in Blockchain, 3, 522600, (2020).
  • [46] Zhao S., Li S., Yao Y. Blockchain Enabled Industrial Internet of Things Technology. IEEE Transactions on Computational Social Systems, 6(6): 1442–1453, (2019).
  • [47] Baalamurugan K.M. et al. Blockchain-enabled K-harmonic framework for industrial IoT-based systems. Scientific Reports, 13: 1004, (2023).
  • [48] Alladi T., Chamola V., Parizi R.M., Choo K.K. Blockchain Applications for Industry 4.0 and Industrial IoT: A Review. IEEE Access, 7: 176935–176951, (2019).
  • [49] Adeyeri M.K. From Industry 3.0 to Industry 4.0: Smart Predictive Maintenance System as Platform for Leveraging. Arctic Journal, 71(11): 64–81, (2018).
  • [50] Doraiswami R., Cheded L. Fault Detection and Isolation. Fault Diagnosis and Detection (FDD), 1st edition, IntechOpen, London, UK, (2017).
  • [51] Bagchi S., Cheng Q.S. Computational modeling of consistent observation of asynchronous distributed computation on N–manifold. Cogent Engineering, 5(1): 1528029, (2018).
  • [52] Zhang Y., Luo L., Ji X., Dai Y. Improved Random Forest Algorithm Based on Decision Paths for Fault Diagnosis of Chemical Process with Incomplete Data. Sensors, 21(20): 6715, (2021).
  • [53] Durrant-Whyte H., Henderson T.C. Multisensor Data Fusion. Handbook of Robotics. Springer, Berlin, Heidelberg, 585–610, (2008).
  • [54] Naqvi R.A., Arsalan M., Qaiser T., Khan T.M., Razzak I. Sensor Data Fusion Based on Deep Learning for Computer Vision Applications and Medical Applications. Sensors, 22(20): 8058, (2022).
  • [55] Lee S.K., Hong S.H., Jun W.H., Hong Y.S. Multi-Sensor Data Fusion with a Reconfigurable Module and Its Application to Unmanned Storage Boxes. Sensors, 22(14): 5388, (2022).
  • [56] https://www.thinkautonomous.ai/blog/9-types-of-sensor-fusion-algorithms/, “9 types of sensor fusion algorithms” (2021), accessed on July 7th, 2023.
  • [57] Yadav P., Mishra A., Kim S. A Comprehensive Survey on Multi-Agent Reinforcement Learning for Connected and Automated Vehicles. Sensors, 23(10): 4710 (2023).
  • [58] Castanedo F. A Review of Data Fusion Techniques. The Scientific World Journal, 2013: 704504, (2013).
  • [59] https://www.bosch-mobility-solutions.com/en/solutions/sensors/sensor-data-fusion/, “Sensor data fusion” (2023), accessed on July 7th, 2023.
  • [60] Aroulanandam V.V. et al. Sensor data fusion for optimal robotic navigation using regression based on an IOT system. Measurement: Sensors, 24: 100598, (2022).
  • [61] Varghese J.P., Sundaramoorthy K., Sankaran A. Development and Validation of a Load Flow Based Scheme for Optimum Placing and Quantifying of Distributed Generation for Alleviation of Congestion in Interconnected Power Systems. Energies, 16(6): 2536, (2023).
  • [62] Zonta T. et al. Predictive maintenance in the Industry 4.0: A systematic literature review. Computers & Industrial Engineering, 150: 106889, (2020).
  • [63] Kashinath S.A. et al. Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis. IEEE Access, 9: 51258–51276, (2021).
  • [64] Karaahmetoglu E., Ersöz S., Türker A.K., Ates V. and Inal A.F., “Evaluation of Profession Predictions for Today and the Future with Machine Learning Methods: Emperical Evidence From Turkey”, Journal of Polytechnic, 26(1): 107–124, (2023).
  • [65] Achouch M. et al. On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges. Applied Sciences, 12(16): 8081, (2022).
  • [66] Roig P.J., Alcaraz S., Gilly K., Bernad C., Juiz C. Modeling an edge computing arithmetic framework for IoT. Sensors, 22(3): 1084 (2022).
  • [67] Roig P.J., Alcaraz S., Gilly K., Bernad C., Juiz C. Arithmetic Framework to Optimize Packet Forwarding among End Devices in Generic Edge. Sensors, 22(2): 421 (2022).
  • [68] Sunyaev A. Fog and Edge Computing. Internet Computing, chapter 8, Springer, Cham, Switzerland, (2020).
  • [69] Roig P.J., Alcaraz S., Gilly K., Bernad C., Juiz C. Modeling of a Generic Edge Computing Application Design. Sensors, 21(21): 7276 (2021).
  • [70] Firouzi F., Farahani B., Panahi E., Barzegari M. Task Offloading for Edge-Fog-Cloud Interplay in the Healthcare Internet of Things (IoT). Proceedings of IEEE International Conference on Omni-Layer Intelligent Systems (COINS), Barcelona, Spain, (2021).
  • [71] Roig P.J., Alcaraz S., Gilly K., Bernad C., Juiz C. Formal Algebraic Model of an Edge Data Center with a Redundant Ring Topology. Network, 3(1): 142–157 (2023).
  • [72] Roig P.J. Formal Algebraic Modelling of a Fog Computer Network Architecture. PhD Thesis in Information and Communication Technologies, University of the Balearic Islands, Spain, (2022).
  • [73] Al-Makhlafi M., Gu H., Yu X., Lu Y. P-Cube: A New Two-Layer Topology for Data Center Networks Exploiting Dual-Port Servers. IEICE Transactions on Communications, E103-B(9): 940–950, (2020).
  • [74] Cortés-Castillo, A. Various Network Topologies and an Analysis Comparative Between Fat-Tree and BCube for a Data Center Network: An Overview. Proceeding of IEEE Cloud Summit, Fairfax, VA, USA, (2022).
  • [75] Roig P.J., Alcaraz S., Gilly K., Bernad C., Juiz C. Arithmetic Study about Efficiency in Network Topologies for Data Centers. Network, 3(3): 298–325, (2023).
  • [76] Roig P.J., Alcaraz S., Gilly K., Juiz C. Arithmetic Study about Energy Save in Switches for some Data Centre Topologies. Journal of Polytechnic, 25(2): 785–797 (2022).
  • [77] Roig P.J., Alcaraz S., Gilly K., Bernad C., Juiz C. Edge Data Center Organization and Optimization by Using Cage Graphs. Network, 3(1): 93–114, (2023).
  • [78] Al-Fares M., Loukissas A., Vahdat A. A Scalable, Commodity Data Center Network Architecture. Proceedings of SIGCOMM 2008, Seattle, WA, USA, (2008).
  • [79] Alizadeh M., Edsall T. On the Data Path Performance of Leaf-Spine Datacenter Fabrics. Proceedings of IEEE 21st Annual Symposium on High-Performance Interconnects, San Jose, CA, USA, (2013).
  • [80] Guo C. et al. BCube: A High Performance, Server-centric Network Architecture for Modular Data Centers. Proceedings of SIGCOMM 2009, Barcelona, Spain, (2009).
  • [81] Guo C. et al. DCell: A Scalable and Fault-Tolerant Network Structure for Data Centers. Proceedings of SIGCOMM 2008, Seattle, WA, USA, (2008).
  • [82] Li D., Guo C., Wu H., Tan K., Zhang Y., Lu S. FiConn: Using Backup Port for Server Interconnection in Data Centers. Proceedings of INFOCOM 2009, Rio de Janeiro, Brazil, (2009).
  • [83] Roig P.J., Alcaraz S., Gilly K., Bernad C., Juiz C. Features of data center network topologies fit for IIoT deployments. Advances and Challenges in Science and Technology, 9: 29–48 (2023).
  • [84] Bornholdt H., Röbert K., Breitbach M., Fischer M., Edinger J. Measuring the Edge: A Performance Evaluation of Edge Offloading. Proceedings of IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), Atlanta, GA, USA, (2023).
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Details

Primary Language English
Subjects Electrical Energy Transmission, Networks and Systems
Journal Section Research Article
Authors

Pedro Juan Roig 0000-0002-8391-8946

Salvador Alcaraz 0000-0003-3701-5583

Katja Gılly 0000-0002-8985-0639

Cristina Bernad 0000-0001-9537-415X

Carlos Juiz 0000-0001-6517-5395

Early Pub Date December 6, 2023
Publication Date
Submission Date July 19, 2023
Published in Issue Year 2024 EARLY VIEW

Cite

APA Roig, P. J., Alcaraz, S., Gılly, K., Bernad, C., et al. (2023). Study on Data Center Network Topologies for Monitoring Data using Edge/Fog Computing. Politeknik Dergisi1-1. https://doi.org/10.2339/politeknik.1327987
AMA Roig PJ, Alcaraz S, Gılly K, Bernad C, Juiz C. Study on Data Center Network Topologies for Monitoring Data using Edge/Fog Computing. Politeknik Dergisi. Published online December 1, 2023:1-1. doi:10.2339/politeknik.1327987
Chicago Roig, Pedro Juan, Salvador Alcaraz, Katja Gılly, Cristina Bernad, and Carlos Juiz. “Study on Data Center Network Topologies for Monitoring Data Using Edge/Fog Computing”. Politeknik Dergisi, December (December 2023), 1-1. https://doi.org/10.2339/politeknik.1327987.
EndNote Roig PJ, Alcaraz S, Gılly K, Bernad C, Juiz C (December 1, 2023) Study on Data Center Network Topologies for Monitoring Data using Edge/Fog Computing. Politeknik Dergisi 1–1.
IEEE P. J. Roig, S. Alcaraz, K. Gılly, C. Bernad, and C. Juiz, “Study on Data Center Network Topologies for Monitoring Data using Edge/Fog Computing”, Politeknik Dergisi, pp. 1–1, December 2023, doi: 10.2339/politeknik.1327987.
ISNAD Roig, Pedro Juan et al. “Study on Data Center Network Topologies for Monitoring Data Using Edge/Fog Computing”. Politeknik Dergisi. December 2023. 1-1. https://doi.org/10.2339/politeknik.1327987.
JAMA Roig PJ, Alcaraz S, Gılly K, Bernad C, Juiz C. Study on Data Center Network Topologies for Monitoring Data using Edge/Fog Computing. Politeknik Dergisi. 2023;:1–1.
MLA Roig, Pedro Juan et al. “Study on Data Center Network Topologies for Monitoring Data Using Edge/Fog Computing”. Politeknik Dergisi, 2023, pp. 1-1, doi:10.2339/politeknik.1327987.
Vancouver Roig PJ, Alcaraz S, Gılly K, Bernad C, Juiz C. Study on Data Center Network Topologies for Monitoring Data using Edge/Fog Computing. Politeknik Dergisi. 2023:1-.

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