Abstract
The data crowdsourcing paradigm applied in coastal and marine monitoring and management has been developed only recently due to the challenges of the marine environment. The pervasive internet of things technology is contributing to increase the number of connected instrumented devices available for data crowd-sourcing. A main issue in the fog/edge/cloud paradigm is that collected data need to be moved from tiny low power devices to cloud resources in order to be processed. This paper is about the DYNAMO data transfer framework enabling the data transfer feature in a internet of floating things scenario. The proposed framework is our solution to mitigate the effects of extreme and delay tolerant environments.
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
Notes
References
Aloi, G., et al.: Enabling IoT interoperability through opportunistic smartphone-based mobile gateways. J. Netw. Comput. Appl. 81, 74–84 (2017)
Deyannis, D., Tsirbas, R., Vasiliadis, G., Montella, R., Kosta, S., Ioannidis, S.: Enabling GPU-assisted antivirus protection on android devices through edge offloading. In: Proceedings of the 1st International Workshop on Edge Systems, Analytics and Networking, pp. 13–18. ACM (2018)
Fortino, G., Trunfio, P. (eds.): Internet of Things Based on Smart Objects: Technology, Middleware and Applications. IT. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-00491-4
Fujisaki, E., Okamoto, T., Pointcheval, D., Stern, J.: RSA-OAEP is secure under the RSA assumption. In: Kilian, J. (ed.) CRYPTO 2001. LNCS, vol. 2139, pp. 260–274. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44647-8_16
Gomes, T., Pinto, S., Tavares, A., Cabral, J.: Towards an FPGA-based edge device for the Internet of Things. In: 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA), pp. 1–4. IEEE (2015)
Guo, H., Crossman, J.A., Murphey, Y.L., Coleman, M.: Automotive signal diagnostics using wavelets and machine learning. IEEE Trans. Veh. Technol. 49(5), 1650–1662 (2000)
Heipke, C.: Crowdsourcing geospatial data. ISPRS J. Photogramm. Remote Sens. 65(6), 550–557 (2010)
Kosta, S., Aucinas, A., Hui, P., Mortier, R., Zhang, X.: ThinkAir: dynamic resource allocation and parallel execution in the cloud for mobile code offloading. In: 2012 Proceedings of the IEEE INFOCOM, pp. 945–953. IEEE (2012)
Li, H.: Multi-agent Q-learning of channel selection in multi-user cognitive radio systems: a two by two case. In: 2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009, pp. 1893–1898. IEEE (2009)
Lin, Y.W., Bates, J., Goodale, P.: Co-observing the weather, co-predicting the climate: human factors in building infrastructures for crowdsourced data. Sci. Technol. Stud. 29(3), 10–27 (2016)
Locke, D.: MQ telemetry transport (MQTT) v3. 1 protocol specification. IBM developerWorks Technical Library, p. 15 (2010)
Mao, Y., Zhang, J., Letaief, K.B.: Dynamic computation offloading for mobile-edge computing with energy harvesting devices. IEEE J. Sel. Areas Commun. 34(12), 3590–3605 (2016)
Marcellino, L., et al.: Using GPGPU accelerated interpolation algorithms for marine bathymetry processing with on-premises and cloud based computational resources. In: Wyrzykowski, R., Dongarra, J., Deelman, E., Karczewski, K. (eds.) PPAM 2017. LNCS, vol. 10778, pp. 14–24. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78054-2_2
Montella, R., Kosta, S., Foster, I.: DYNAMO: distributed leisure yacht-carried sensor-network for atmosphere and marine data crowdsourcing applications. In: 2018 IEEE International Conference on Cloud Engineering (IC2E), pp. 333–339. IEEE (2018)
Montella, R., et al.: Accelerating linux and android applications on low-power devices through remote GPGPU offloading. Concurr. Comput. Pract. Exp. 29(24), e4286 (2017)
Montella, R., Ruggieri, M., Kosta, S.: A fast, secure, reliable, and resilient data transfer framework for pervasive IoT applications. In: IEEE INFOCOM 2018-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE (2018)
Pace, P., Aloi, G., Gravina, R., Caliciuri, G., Fortino, G., Liotta, A.: An edge-based architecture to support efficient applications for healthcare industry 4.0. IEEE Trans. Ind. Inf. (2018)
Pham, Q., Malik, T., Foster, I., Di Lauro, R., Montella, R.: SOLE: linking research papers with science objects. In: Groth, P., Frew, J. (eds.) IPAW 2012. LNCS, vol. 7525, pp. 203–208. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34222-6_16
Salim, F., Haque, U.: Urban computing in the wild: a survey on large scale participation and citizen engagement with ubiquitous computing, cyber physical systems, and Internet of Things. Int. J. Hum.-Comput. Stud. 81, 31–48 (2015)
Scott, K.L., Burleigh, S.: Bundle protocol specification. RFC 5050 (2007)
Sen, S., Balasubramanian, A.: A highly resilient and scalable broker architecture for IoT applications. In: 2018 10th International Conference on Communication Systems & Networks (COMSNETS), pp. 336–341. IEEE (2018)
Shelby, Z., Hartke, K., Bormann, C.: The constrained application protocol (CoAP). RFC 5272 (2014)
Singh, M., Rajan, M., Shivraj, V., Balamuralidhar, P.: Secure MQTT for Internet of Things (IoT). In: 2015 Fifth International Conference on Communication Systems and Network Technologies (CSNT), pp. 746–751. IEEE (2015)
Stojmenovic, I., Wen, S.: The Fog computing paradigm: scenarios and security issues. In: 2014 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 1–8. IEEE (2014)
Turner, A.: Introduction to Neogeography. O’Reilly Media Inc., Newton (2006)
Yokotani, T., Sasaki, Y.: Comparison with HTTP and MQTT on required network resources for IoT. In: 2016 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC), pp. 1–6. IEEE (2016)
Zhou, J., Dong, X., Cao, Z., Vasilakos, A.V.: Secure and privacy preserving protocol for cloud-based vehicular DTNs. IEEE Trans. Inf. Forensics Secur. 10(6), 1299–1314 (2015)
Acknowledgments
This work has been supported in part by U.S. National Science Foundation awards 0951576 and 1331782; and by the University of Napoli Parthenope, Italy (project DSTE333 “Modelling Mytilus Farming System with Enhanced Web Technologies” funded by Campania Region/Veterinary sector).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Montella, R., Di Luccio, D., Kosta, S., Giunta, G., Foster, I. (2018). Performance, Resilience, and Security in Moving Data from the Fog to the Cloud: The DYNAMO Transfer Framework Approach. In: Xiang, Y., Sun, J., Fortino, G., Guerrieri, A., Jung, J. (eds) Internet and Distributed Computing Systems. IDCS 2018. Lecture Notes in Computer Science(), vol 11226. Springer, Cham. https://doi.org/10.1007/978-3-030-02738-4_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-02738-4_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02737-7
Online ISBN: 978-3-030-02738-4
eBook Packages: Computer ScienceComputer Science (R0)