Next Generation Edge-Cloud Continuum Architecture for Structural Health Monitoring

被引:5
作者
Gigli, Lorenzo [1 ,2 ]
Zyrianoff, Ivan [1 ,2 ]
Zonzini, Federica [1 ,3 ]
Bogomolov, Denis [1 ]
Testoni, Nicola [1 ,3 ]
Felice, Marco Di [1 ,2 ]
De Marchi, Luca [1 ,3 ]
Augugliaro, Giuseppe [4 ]
Mennuti, Canio [4 ]
Marzani, Alessandro [5 ]
机构
[1] Univ Bologna, Adv Res Ctr Elect Syst Informat & Commun Technol E, I-40136 Bologna, Italy
[2] Univ Bologna, Dept Comp Sci & Engn, I-40136 Bologna, Italy
[3] Univ Bologna, Dept Elect Elect & Informat Engn, I-40136 Bologna, Italy
[4] INAIL Technol Innovat Dept, I-00078 Rome, Italy
[5] Univ Bologna, Dept Civil Chem Environm & Mat Engn, I-40136 Bologna, Italy
关键词
Computer architecture; Monitoring; Sensors; Cloud computing; Software; Vibrations; Next generation networking; Edge-cloud continuum; Internet of Things; interoperability; structural health monitoring; IDENTIFICATION; SYSTEM; MIGRATION; WEB;
D O I
10.1109/TII.2023.3337391
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Assessing the integrity of industrial and civil appliances has become a priority worldwide. Noteworthy, this goal requires a strong synergy between multiple tools, disciplines, and approaches to be attained via a joint hardware-software co-design of the different Structural Health Monitoring (SHM) system components. This work proposes the MAC4PRO architecture, a sensor-to-cloud monitoring platform that seamlessly integrates sensing and software technologies for accurate data measurement, transmission, and analysis. The developed solution stands out for its interoperability and versatility, making it a promising candidate for integration in the next generation of smart structures. Our platform was validated during extensive experimental campaigns targeted at various industrial scenarios. The results show that the MAC4PRO architecture can identify subtle changes, such as 1mm size leakage events in pipeline circuits, or less than 1% frequency drifts in civil buildings after seismic excitation, while ensuring more than 90% reduction in the edge-to-cloud data transfer process.
引用
收藏
页码:5874 / 5887
页数:14
相关论文
共 45 条
  • [1] Aguzzi C., 2021, P IEEE ANN CONS COMM, P1, DOI DOI 10.1109/CCNC49032
  • [2] From Cloud to Edge: Seamless Software Migration at the Era of the Web of Things
    Aguzzi, Cristiano
    Gigli, Lorenzo
    Sciullo, Luca
    Trotta, Angelo
    Di Felice, Marco
    [J]. IEEE ACCESS, 2020, 8 : 228118 - 228135
  • [3] [Anonymous], 2008, Health monitoring of bridges
  • [4] Sensing and Decision Making in Cyber-Physical Systems: The Case of Structural Event Monitoring
    Bhuiyan, Md Zakirul Alam
    Wu, Jie
    Wang, Guojun
    Cao, Jiannong
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2016, 12 (06) : 2103 - 2114
  • [5] Systematic sensor placement for structural anomaly detection in the absence of damaged states
    Bigoni, Caterina
    Zhang, Zhenying
    Hesthaven, Jan S.
    [J]. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2020, 371
  • [6] Bogomolov D., 2021, P 10 INT C STRUCT HL, P383
  • [7] Brincker R, 2000, P SOC PHOTO-OPT INS, V4062, P625
  • [8] Casas J.R., 2017, Frontiers in Built Environment, V3, P1, DOI [DOI 10.3389/FBUIL.2017, DOI 10.3389/FBUIL.2017.00004]
  • [9] Dynamic Task Allocation and Service Migration in Edge-Cloud IoT System Based on Deep Reinforcement Learning
    Chen, Yan
    Sun, Yanjing
    Wang, Chenyang
    Taleb, Tarik
    [J]. IEEE INTERNET OF THINGS JOURNAL, 2022, 9 (18) : 16742 - 16757
  • [10] Deep learning for seismic structural monitoring by accounting for mechanics-based model uncertainty
    Cheraghzade, Milad
    Roohi, Milad
    [J]. JOURNAL OF BUILDING ENGINEERING, 2022, 57