Skip to main content
SpringerLink
Log in

Experimental investigation of aerial–ground network communication towards geospatially large-scale structural health monitoring

  • Original Paper
  • Published:
Journal of Civil Structural Health Monitoring Aims and scope Submit manuscript

Abstract

Recognizing the importance of realizing integrated remote sensing and structural monitoring for civil structures at a geospatially large scale, this paper first proposes a concept design toward developing an aerial–ground wireless sensing network (AG-WSN) system for improving the efficiency of systems-level structural health monitoring. Upon this design, this paper further investigates the communication interference within such sensing network, which if not understood would diminish the practical value of such a system. This interference arises because of the UAV’s and the WSN’s communication protocols, for which most UAVs use 2.4 GHz radio for flight control and Wi-Fi (802.11 b/g/n) for imagery data streaming, and the WSNs often use the low-power ZigBee 802.15.4 protocol at 2.4 GHz as well. With the development of the proposed AG-WSN prototype using commercially available products, this paper experimentally achieves two important findings: (1) the key parameters that affect the short-range Wi-Fi and ZigBee interference and the experimental relations; and (2) the ‘comfort zone’ and the sensitive parameters for the long-range optimal ZigBee transmission between the aerial UAV and the ground sensors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. ASCE (2017) ASCE’s 2017 infrastructure report card (D +). https://www.infrastructurereportcard.org/. Accessed 15 Oct 2018

  2. Lynch JP, Loh KJ (2006) A summary review of wireless sensors and sensor networks for structural health monitoring. Shock Vib Dig 38(2):91–130

    Article  Google Scholar 

  3. Gao Y, Spencer B, Ruiz-Sandoval M (2006) Distributed computing strategy for structural health monitoring. Struct Control Health Monit 13(1):488–507

    Article  Google Scholar 

  4. Nagayama T, Spencer BF Jr (2007) Structural health monitoring using smart sensors. Newmark Structural Engineering Laboratory, University of Illinois at Urbana-Champaign, Urbana-Champaign

    Google Scholar 

  5. Wang Y, Lynch JP, Law KH (2007) A wireless structural health monitoring system with multithreaded sensing devices: design and validation. Struct Infrastruct Eng 3(2):103–120

    Article  Google Scholar 

  6. Zhao J, DeWolf JT (2002) Dynamic monitoring of steel girder highway bridge. J Bridge Eng 7(6):350–356

    Article  Google Scholar 

  7. Morgenthal G, Hallermann N (2014) Quality assessment of unmanned aerial vehicle (UAV) based visual inspection of structures. Adv Struct Eng 17(3):289–302

    Article  Google Scholar 

  8. Metni N, Hamel T (2007) A UAV for bridge inspection: visual servoing control law with orientation limits. Autom Constr 17(1):3–10

    Article  Google Scholar 

  9. Yeum CM, Dyke SJ (2015) Vision-based automated crack detection for bridge inspection. Comput Aided Civil Infrastruct Eng 30(10):759–770

    Article  Google Scholar 

  10. Chen L, Warner J, Yung PL, Zhou D, Heinzelman W, Demirkol I, Muncuk U, Chowdhury K, Basagni S (2015) Reach 2-mote: a range-extending passive wake-up wireless sensor node. ACM Trans Sen Netw 11(4):1–33. https://doi.org/10.1145/2829954

    Article  Google Scholar 

  11. Fodor K, Vidács A (2007) Efficient routing to mobile sinks in wireless sensor networks. In: Proceedings of the 3rd international conference on wireless internet. Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (ICST), Article no 32

  12. Pignaton de Freitas E, Heimfarth T, Netto IF, Lino CE, Pereira CE, Ferreira AM, Wagner FR, Larsson T (2010) UAV relay network to support WSN connectivity. In: Ultra modern telecommunications and control systems and workshops (ICUMT), 2010 International Congress, Moscow, Russia, 2010. IEEE, pp 309–314

  13. Chen Z, Chen J, Chase C (2015) Robotic aerial-imaging and ground-sensing network for use in rapid emergency response. In: The joint 6th international conference on advances in experimental structural engineering (6AESE) and 11th international workshop on advanced smart materials and smart structures technology (11ANCRiSST), Champaign

  14. Lee J-O et al (2010) Vision-based indoor localization for unmanned aerial vehicles. J Aerosp Eng 24(3):373–377

    Article  Google Scholar 

  15. Park J et al (2011) Vision-based SLAM system for small UAVs in GPS-denied environments. J Aerosp Eng 25(4):519–529

    Article  Google Scholar 

  16. Yoon H, Shin J, Spencer BF Jr (2018) Structural displacement measurement using an unmanned aerial system. Comput Aided Civil Infrastruct Eng 33(3):183–192

    Article  Google Scholar 

  17. Yoon H et al (2017) Cross-correlation-based structural system identification using unmanned aerial vehicles. Sensors 17(9):2075

    Article  Google Scholar 

  18. Moreu F, Garg P, Ayorinde E (2018) Railroad bridge inspections for maintenance and replacement prioritization using unmanned aerial systems (UAS) with laser capabilities

  19. Spencer B Jr et al (2017) Next generation wireless smart sensors toward sustainable civil infrastructure. Procedia Eng 171:5–13

    Article  Google Scholar 

  20. Tang S, Chen Z (2017) Level-of-detail assessment of structural surface damage using spatially sequential stereo images and deep learning methods. In: The 11th international workshop on structural health monitoring, Stanford

  21. Sim S-H et al (2011) Decentralized random decrement technique for efficient data aggregation and system identification in wireless smart sensor networks. Prob Eng Mech 26(1):81–91

    Article  Google Scholar 

  22. Sim S-H et al (2010) Automated decentralized modal analysis using smart sensors. Struct Control Health Monit 17(8):872–894

    Article  Google Scholar 

  23. Hou J, Chang B, Cho D-K, Gerla M (2009) Minimizing 802.11 interference on ZigBee medical sensors. In: Proceedings of the fourth international conference on body area networks. Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (ICST), Los Angeles, California 2009, pp 1–8

  24. Huang J, Xing G, Zhou G, Zhou R (2010) Beyond co-existence: Exploiting WiFi white space for Zigbee performance assurance. In: Network protocols (ICNP), 18th IEEE international conference on, Kyoto, Japan 2010. IEEE, pp 305–314

  25. Liang C-JM, Priyantha NB, Liu J, Terzis A (2010) Surviving wi-fi interference in low power zigbee networks. In: Proceedings of the 8th ACM conference on embedded networked sensor systems, Zürich, Switzerland 2010. ACM, pp 309–322

  26. Xu R, Shi G, Luo J, Zhao Z, Shu Y (2011) Muzi: multi-channel zigbee networks for avoiding wifi interference. In: Internet of Things (IThings/CPSCom), 2011 international conference on and 4th international conference on cyber, physical and social computing, Dalian, China 2011. IEEE, pp 323–329

  27. Zhang X, Shin KG (2011) Enabling coexistence of heterogeneous wireless systems: case for zigbee and wifi. In: Proceedings of the twelfth ACM international symposium on mobile ad hoc networking and computing, Paris, France 2011. ACM, pp 1–11

  28. Shin SY, Park HS, Choi S, Kwon WH (2007) Packet error rate analysis of ZigBee under WLAN and Bluetooth interferences. IEEE Trans Wirel Commun 6(8):2825–2830. https://doi.org/10.1109/TWC.2007.06112

    Article  Google Scholar 

  29. Yi P, Iwayemi A, Zhou C (2011) Developing ZigBee deployment guideline under WiFi interference for smart grid applications. IEEE Trans Smart Grid 2(1):110–120

    Article  Google Scholar 

  30. Thonet G, Allard-Jacquin P, Colle P (2008) Zigbee-wifi coexistence. Schneider Electr White Pap Test Rep 1:1–38

    Google Scholar 

  31. Kemkemian S, Nouvel-Fiani M, Cornic P, Le Bihan P, Garrec P (2009) Radar systems for “Sense and avoid” on UAV. In: Radar conference-surveillance for a safer world. RADAR. International, Bordeaux, France 2009. IEEE, pp 1–6

  32. Watanabe Y, Calise A, Johnson E (2007) Vision-based obstacle avoidance for UAVs. In: AIAA guidance, navigation and control conference and exhibit, Hilton Head, South Carolina, 2007. pp 1–11

  33. Dumitrescu A, Mitchell JS (2003) Approximation algorithms for TSP with neighborhoods in the plane. J Algorithms 48(1):135–159

    Article  MathSciNet  Google Scholar 

  34. Zhang X et al (2014) A memetic algorithm for path planning of curvature-constrained UAVs performing surveillance of multiple ground targets. Chin J Aeronaut 27(3):622–633

    Article  Google Scholar 

  35. Boukerche A, Darehshoorzadeh A (2015) Opportunistic routing in wireless networks: models, algorithms, and classifications. ACM Comput Surv CSUR 47(2):22

    Google Scholar 

  36. Rosario D, Zhao Z, Braun T, Cerqueira E, Santos A, Alyafawi I (2014) Opportunistic routing for multi-flow video dissemination over flying ad-hoc networks. In: 2014 IEEE 15th international symposium on, Sydney, Australia 2014. IEEE, pp 1–6

  37. Rosário D et al (2014) A beaconless opportunistic routing based on a cross-layer approach for efficient video dissemination in mobile multimedia IoT applications. Comput Commun 45:21–31

    Article  Google Scholar 

Download references

Acknowledgements

This material is partially based upon work supported by the National Science Foundation (NSF) under Award Number IIA-1355406 and work supported by the United States Department of Agriculture’s National Institute of Food and Agriculture (USDA-NIFA) under Award Number 2015-68007-23214. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of NSF or USDA-NIFA.

Author information

Authors and Affiliations

  1. Department of Computer Science and Electrical Engineering, University of Missouri-Kansas City, 5100 Rockhill Rd, Kansas, MO, 64110-2499, USA

    Jianfei Chen & Cory Beard

  2. Department of Civil and Mechanical Engineering, University of Missouri-Kansas City, 5100 Rockhill Rd, Kansas, MO, 64110-2499, USA

    ZhiQiang Chen

Authors
  1. Jianfei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. ZhiQiang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cory Beard
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to ZhiQiang Chen.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, J., Chen, Z. & Beard, C. Experimental investigation of aerial–ground network communication towards geospatially large-scale structural health monitoring. J Civil Struct Health Monit 8, 823–832 (2018). https://doi.org/10.1007/s13349-018-0310-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13349-018-0310-7

Keywords

Search

Navigation