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

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.