Abstract
Vessel Monitoring Systems (VMS) have been adopted by many countries which provide information on the spatial and temporal distribution of fishing activity. Real-time communication and interaction between fishing vessels and shore-based systems is a weakness of traditional vessel monitoring systems. This paper proposes a novel framework of edge computing-based VMS (EC-VMS). The framework of EC-VMS mainly consists of four layers. An edge computing terminal is used on each vessel, and the BeiDou navigation satellite system (BDS) is adopted for communication. Meanwhile, edge computing servers interact with corresponding management vessels and the cloud. In order to decrease the communication cost, a data transmission policy called Adaptable Trajectory Transmission Model (ATTM) is presented in this paper. The experimental results illustrate the efficiency of the proposed EC-VMS, with the average communication time significantly decreased in a typical scenario. Moreover, EC-VMS improves the real-time performance of the system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Stephen, C.V., Stuart, B., Matthew, J.W., Richard, I., David, T., Jason, N.: Individual responses of seabirds to commercial fisheries revealed using GPS tracking, stable isotopes and vessel monitoring systems. J. Appl. Ecol. 47(2), 487–497 (2010)
Lee, J., South, A.B., Jennings, S.: Developing reliable, repeatable, and accessible methods to provide high-resolution estimates of fishing-effort distributions from vessel monitoring system (VMS) data. ICES J. Mar. Sci. 67(6), 1260–1271 (2010)
Ejaz, A., Mubashir, H.R.: Mobile edge computing opportunities, solutions, and challenges. Future Gener. Comput. Syst. 70, 59–63 (2017)
Marzuki, M.I., Gaspar, P., Garello, R.: Fishing gear identification from vessel-monitoring-system-based fishing vessel trajectories. IEEE J. Oceanic Eng. 43(3), 689–699 (2018)
de Souza, E.N., Boerder, K., Matwin, S.: Improving fishing pattern detection from satellite AIS using data mining and machine learning. PLOS ONE 11(7), e0158248 (2016)
Ducharme-Barth, N.D., Shertzer, K.W., Ahrens, R.N.M.: Indices of abundance in the Gulf of Mexico reef fish complex: a comparative approach using spatial data from vessel monitoring systems. Fish. Res. 198, 1–13 (2018)
Watson, J.T., Haynie, A.C.: Using vessel monitoring system data to identify and characterize trips made by fishing vessels in the United States North Pacific. PLOS ONE 11(10), e0165173 (2016)
Watson, J.T., Haynie, A.C., Sullivan, P.J.: Vessel monitoring systems (VMS) reveal an increase in fishing efficiency following regulatory changes in a demersal longline fishery. Fish. Res. 207, 85–94 (2018)
Longepe, N., Hajduch, G., Ardianto, R.: Completing fishing monitoring with spaceborne Vessel Detection System (VDS) and Automatic Identification System (AIS) to assess illegal fishing in Indonesia. Marine Pollution Bulletin 131(SI), 33–39 (2018)
Al-Zaidi, R., Woods, J., Al-Khalidi, M.: Next generation marine data networks in an IoT environment. In: Second International Conference on Fog and Mobile Edge Computing 2017, FMEC, pp. 50–55. IEEE, Valencia (2017)
Lu, C., Li, X., Nilsson, T.: Real-time retrieval of precipitable water vapor from GPS and BeiDou observations. J. Geodesy 89(9), 843–856 (2015)
Zhang, Y., Chen, S., Hong, Z.: Feasibility of oil slick detection using BeiDou-R coastal simulation. Math. Prob. Eng. 4, 1–8 (2017)
Yu, F., Hu, X., Dong, S.: Design of a low-cost oil spill tracking buoy. J. Mar. Sci. Technol. 23(1), 188–200 (2018)
Wang, L., Li, L., Qiu, R.: Edge computing-based differential positioning method for BeiDou navigation satellite system. KSII Trans. Internet Inf. Syst. 13(1), 69–85 (2019)
Satyanarayanan, M.: The emergence of edge computing. Computer 50(1), 30–39 (2017)
Shi, W., Cao, J., Zhang, Q.: Edge computing: vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016)
Zeydan, E., Bastug, E., Bennis, M.: Big data caching for networking: moving from cloud to edge. IEEE Commun. Mag. 54(9), 36–42 (2016)
Rahmani, A.M., Gia, T.N., Negash, B.: Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: a fog computing approach. Future Gener. Comput. Syst. 78, 641–658 (2018)
Taleb, T., Dutta, S., Ksentini, A.: Mobile edge computing potential in making cities smarter. IEEE Commun. Mag. 55(3), 38–43 (2017)
Premsankar, G., Di Francesco, M., Taleb, T.: Edge computing for the internet of things: a case study. IEEE Internet Things J. 5(2), 1275–1284 (2018)
Trajcevski, G., Cao, H., Scheuermann, P., Wolfson, O., Vaccaro, D.: On-line data reduction and the quality of history in moving objects databases. In: Proceedings of the 5th ACM International Workshop on Data Engineering for Wireless and Mobile Access, MobiDE 2006, pp. 19–26. ACM, Chicago (2006)
Muckell, J., Hwang, J., Patil, V., Lawson, C., Ping, F., Ravi, S.: SQUISH: an online approach for GPS trajectory compression. In: Proceedings of the 2nd International Conference and Exhibition on Computing for Geospatial Research & Application, COM.Geo 2011. ACM, Washington DC (2011)
Acknowledgment
This work was supported in part by the Key Research and Development Project of Zhejiang Province (Grant No. 2017C03024), the National Natural Science Foundation of China (Grant No. 61572163) and the Zhejiang Province Research Program (Grant No. 2017C33065).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Zhu, F., Ren, Y., Huang, J., Wan, J., Zhang, H. (2019). An Edge Computing-Based Framework for Marine Fishery Vessels Monitoring Systems. In: Wang, X., Gao, H., Iqbal, M., Min, G. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 292. Springer, Cham. https://doi.org/10.1007/978-3-030-30146-0_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-30146-0_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30145-3
Online ISBN: 978-3-030-30146-0
eBook Packages: Computer ScienceComputer Science (R0)