World Wide Web

, Volume 21, Issue 1, pp 7–32 | Cite as

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

  • Richard K. Lomotey
  • Joseph C. Pry
  • Chenshean Chai
Part of the following topical collections:
  1. Special Issue on Security and Privacy of IoT


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.


IoT Mobile devices Provenance Data propagation Visualization Traceability 



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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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Richard K. Lomotey
    • 1
  • Joseph C. Pry
    • 1
  • Chenshean Chai
    • 1
  1. 1.Department of Information Sciences and Technology (IST)Pennsylvania State UniversityBeaver Campus MonacaUSA

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