Leveraging Semantic Technologies for Collaborative Inference of Threatening IoT Dependencies

Abstract

IoT Device Management (DM) refers to the remote administration of customer devices. In practice, DM is ensured by multiple actors such as operators or device manufacturers, each operating independently via their DM solution. These siloed DM solutions are limited in addressing IoT threats related to device dependencies, such as cascading failures, as these threats spread across devices managed by different DM actors, and their mitigation can no longer be performed without collaborative DM efforts. The first step toward collaborative mitigation of these threats is the identification of threatening dependency topology. However, this task is challenging, requiring the inference of dependencies from the data held by different actors. In this work, we propose a collaborative framework that infers the threatening topology of dependencies by accessing and aggregating data from legacy DM solutions. It combines the assets of Semantic Web standards and Digital Twin technology to capture on-demand the topology of dependencies, and it is designed to be used in business applications such as customer care to enhance customer Quality of Experience. We integrate our solution within the in-use Orange's Digital Twin platform Thing in the future and demonstrate its effectiveness by automatically inferring threatening dependencies in the two settings: a simulated smart home scenario managed by ground-truth DM solutions, such as Orange's implementation of the USP Controller and Samsung's SmartThings Platform , and a realistic smart home called DOMUS testbed.