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Traceability and visual analytics for the Internet-of-Things (IoT) architecture

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Abstract

There are several billion network-oriented devices in use today that are facilitated to inter-communicate; thereby forming a giant neural-like architecture known as the Internet-of-Things (IoT). The benefits of the IoT cut across all spectrums of our individual lives, corporate culture, and societal co-existence. This is because IoT devices support health tracking, security monitoring, consumer tracking, forecasting, and so on. However, the huge interconnectedness in IoT architectures complicates traceability and faulty data propagation is not easily detected since there are challenges with data origin authentication. Thus, this research proposes a provenance technique to deal with these issues. The technique is based on associative rules and lexical chaining methodologies, which enable traceability through the identification of propagation routes of data and object-to-object communications. Through visualization tools, the proposed methodologies also enabled us to determine linkability and unlinkability between IoT devices in a network which further leads to mechanisms to check correctness in sensor data propagation.

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References

  1. Al-Azez, Z.T., Lawey, A.Q., El-Gorashi, T.E.H., Elmirghani, J.M.H.: Virtualization framework for energy efficient IoT networks, cloud networking (CloudNet), 2015 I.E. 4th International Conference on, pp. 74–77. Niagara Falls, ON (2015). doi:10.1109/CloudNet.2015.7335284

    Book  Google Scholar 

  2. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M.: Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutorials. 17(4), 2347–2376. ISSN 1553-877x (2015). doi:10.1109/COMST.2015.2444095

    Article  Google Scholar 

  3. Al-Fuqaha, A., Khreishah, A., Guizani, M., Rayes, A., Mohammadi, M.: Toward better horizontal integration among IoT services. IEEE Commun. Mag. 53(9), 72–79 (2015). doi:10.1109/MCOM.2015.7263375

    Article  Google Scholar 

  4. Amsterdamer, Y., Deutch, D., Milo, T., Tannen, V.: On provenance minimization. Database Syst. ACM Trans. 37(4), 36 (2012). doi:10.1145/2389241.2389249

    Article  Google Scholar 

  5. Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010). doi:10.1016/j.comnet.2010.05.010

    Article  MATH  Google Scholar 

  6. Banda, G., Chaitanya, K. and Mohan, H.: An IoT protocol and framework for OEMs to make IoT-enabled devices forward compatible, 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Bangkok, (2015), pp. 824–832, doi:10.1109/SITIS.2015.106.

  7. Bauer, S., & Schreckling, D.:Data provenance in the internet of things. EU Project COMPOSE, Conference Seminar (2013)

  8. Boman, J., Taylor, J., Ngu, A. H., Flexible IoT middleware for integration of things and applications, collaborative computing: networking, applications and Worksharing (CollaborateCom), 2014 International Conference on, Miami, FL, (2014), pp. 481–488, doi: 10.4108/icst.collaboratecom.2014.257533.

  9. CouchDB, http://docs.couchdb.org/en/1.6.1/intro/tour.html

  10. Cui, Z., Lv, H., Yin, C., Gao, G., Zhou, C.: Efficient key management for IOT owner in the cloud, big data and cloud computing (BDCloud), 2015 I.E. fifth International Conference on, Dalian, (2015), pp. 56–61, doi:10.1109/BDCloud.2015.40.

  11. Davidson, S. B., and Freire, J. Provenance and scientific workflows: challenges and opportunities. In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, SIGMOD '08, pages 1345–1350, 09 (2008). doi: 10.1145/1376616.1376772.

  12. Farris, I., Militano, L., Nitti, M., Atzori, L. and Iera, A.: Federated edge-assisted mobile clouds for service provisioning in heterogeneous IoT environments, Internet of Things (WF-IoT), 2015 I.E. 2nd World Forum on, Milan, (2015), pp. 591–596, doi: 10.1109/WF-IoT.2015.7389120.

  13. Genquan, R. Li, Z., Jianmin, W., and Yinbo, L.: One method for provenance tracking of product lifecycle data in collaborative service environment. Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2011 International Conference on, pp.347–356, 10–12 (2011)

  14. Georgakopoulos, D., Jayaraman, P. P., Zhang, M., Ranjan, R.: Discovery-driven service oriented IoT architecture, 2015 I.E. Conference on Collaboration and Internet Computing (CIC), Hangzhou, (2015), pp. 142–149, doi:10.1109/CIC.2015.34.

  15. Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2(1), 193–218 (1985)

    Article  MATH  Google Scholar 

  16. Lawabni, A. E., Changjin H., Du, D. H. C., Tewfik, A. H.: A novel update propagation module for the data provenance problem: a contemplating vision on realizing data provenance from models to storage. In Proceedings of the 22nd IEEE / 13th NASA Goddard Conference on Mass Storage Systems and Technologies, CIKM '11, pages 61–69, 11–14 (2005). doi: 10.1109/MSST.2005.2.

  17. Lee, Y. K., Lim, J. D., Jeon, Y. S., Kim, J. N.: Technology trends of access control in IoT and requirements analysis, Information and Communication Technology Convergence (ICTC), 2015 International Conference on, Jeju, (2015). pp. 1031–1033, doi: 10.1109/ICTC.2015.7354730.

  18. Liu, J., Ahmed, E., Shiraz, M., Gani, A., Buyya, R., Qureshi, A.: Application partitioning algorithms in mobile cloud computing: taxonomy, review and future directions. J. Netw. Comput. Appl. 48, 99–117 (2015)

    Article  Google Scholar 

  19. Loo, J., Aiash, M.: Challenges and solutions for secure information centric networks: a case study of the NetInf architecture. J. Netw. Comput. Appl. 50, 64–72 (2015)

    Article  Google Scholar 

  20. Lucia, B.; Ceze, L.: Data provenance tracking for concurrent programs. In Proceedings of the 2015 IEEE/ACM International Symposium on Code Generation and Optimization, CGO (2015), pages 146–156, 7–11 2015. doi: 10.1109/CGO.2015.7054195.

  21. Marjit, U., Sarkar, A., Biswas, U.: A novel approach to develop linked data with provenance. Computing, communication and applications (ICCCA), 2012 International Conference on, pp.1-5, 22-24 Feb. (2012), doi: 10.1109/ICCCA.2012.6179211.

  22. Nastic, S., Copil, G., Truong, H. L., Dustdar, S.: Governing elastic IoT cloud systems under uncertainty, 2015 I.E. 7th International Conference on cloud computing Technology and Science (CloudCom), Vancouver, BC, (2015), pp.131–138, doi: 10.1109/CloudCom.2015.77.

  23. NXP, "What the Internet of Things (IoT) Needs to become a reality" white paper, Document Number: INTOTHNGSWP REV 2, May 2014, Availa-ble Online: http://www.nxp.com/files/32bit/doc/white_paper/INTOTHNGSWP.pdf (2014). Last Accessed date: April 02 2016.

  24. Object Management Group (OMG), Data distribution service (DDS), version 1.4, http://www.omg.org/ spec/DDS/1.4

  25. Park, J., Nguyen, D., Sandhu, R.: On data provenance in group-centric secure collaboration. Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom), 2011 7th International Conference on, pp.221–230, 15–18 (2011)

  26. Remus, S., Biemann, C.: Three knowledge-free methods for automatic lexical chain extraction, Proceedings of NAACL-HLT 2013, pages 989–999, Atlanta, Georgia, 9–14. Association for Computational Linguistics (2013)

  27. Rose, K., Eldridge, S., Chapin, L.: The Internet of Things (IoT): an overview, internet society, white paper, date: 15 Oct 2015, Available Online: http://www.internetsociety.org/sites/default/files/ISOC-IoT-Overview-20151221-en.pdf (2015). Last Accessed date: April 04 2016

  28. SensorTag, http://www.ti.com/tool/cc2650stk#0

  29. She, W., I-Ling, Y., Bastani, F., Tran, B., Thuraisingham, B.: Role-based integrated access control and data provenance for SOA based net-centric systems. Service Oriented System Engineering (SOSE), 2011 I.E. 6th International Symposium on, pp.225–234, 12–14 (2011)

  30. Sultana, S., Shehab, M., Bertino, E.: Secure provenance transmission for streaming data. Knowledge and Data Engineering, IEEE Transactions on, vol.PP, no.99, (2012), pp.1, doi: 10.1109/TKDE.2012.31

  31. Sun, L., Park, J., Nguyen, D., Sandhu, R.: A provenance-aware access control framework with typed provenance. IEEE Transactions on Dependable and Secure Computing, vol.PP, no.99, pages 1–14, 09, 2015. doi: 10.1109/TDSC.2015.2410793

  32. Tan, V., Groth, P., Miles, S., Jiang, S., Munroe, S., Tsasakou, S., Moreau, L.: Security issues in a SOA-based provenance system, provenance and annotation of data. Lect. Notes Comput. Sci. 4145, 203–211 (2006)

    Article  Google Scholar 

  33. Zhang, O.Q., Kirchberg, M., Ko, R.K.L., Lee, B. S.: How to track Your data: the case for cloud computing provenance. In Proceedings of the 2011 I.E. third International Conference on cloud computing Technology and Science, CloudCom 2011, pages 446–453, (2011). doi: 10.1109/CloudCom.2011.66.

  34. Zhang, O.Q., Ko, R.K.L., Kirchberg, M., Suen, C. H., Jagadpramana, P., Lee, B. S.: How to track your data: rule-based data provenance tracing algorithms. In Proceedings of the 2012 I.E. 11th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2012, pages 1429–1437, 25–27 June (2012). doi: 10.1109/TrustCom.2012.175.

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Acknowledgements

The authors wish to thank Sriramoju Sumanth. This work was supported in part by a grant from the Pennsylvania State University.

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Authors and Affiliations

  1. Department of Information Sciences and Technology (IST), Pennsylvania State University, Beaver Campus Monaca, PA, 15061, USA

    Richard K. Lomotey, Joseph C. Pry & Chenshean Chai

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  1. Richard K. Lomotey
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Correspondence to Richard K. Lomotey.

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This article belongs to the Topical Collection: Special Issue on Security and Privacy of IoT

Guest Editors: Tarik Taleb, Zonghua Zhang, and Hua Wang

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Lomotey, R.K., Pry, J.C. & Chai, C. Traceability and visual analytics for the Internet-of-Things (IoT) architecture. World Wide Web 21, 7–32 (2018). https://doi.org/10.1007/s11280-017-0461-1

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