Modern Internet of Things (IoT) environments with thousands of low-end and diverse IoT nodes with complex interactions among them and often deployed in remote and/or wild locations present some unique challenges that make traditional node compromise detection services less effective. This article presents the design, implementation, and evaluation of a fog-based architecture that utilizes the concept of a digital twin to detect compromised IoT nodes exhibiting malicious behaviors by either producing erroneous data and/or being used to launch network intrusion attacks to hijack other nodes eventually causing service disruption. By defining a digital twin of an IoT infrastructure at a fog server, the architecture is focused on monitoring relevant information to save energy and storage space. This article presents a prototype implementation for the architecture utilizing malicious behavior datasets to perform misbehaving node classification. An extensive accuracy and system performance evaluation was conducted based on this prototype. Results show good accuracy and negligible overhead especially when employing deep learning techniques, such as multilayer perceptron.

This paper presents a system architecture for building digital twins at the edge. In particular, it addresses two major challenges: system scalability and complexity. It introduces two novel architectural components: a Context Aware Communication Component (CACC) that addresses the scalability issue in communication between physical and virtual environments, and second, an application-agnostic methodology, a service registry component for integrating digital twin with EdgeAI (DT-EdgeAI integration) at the edge. The paper describes this architecture in detail and provides a preliminary prototype implementation.

Online reviews have become essential for users to make informed decisions in everyday tasks ranging from planning summer vacations to purchasing groceries and making financial investments. A key problem in using online reviews is the overabundance of online that overwhelms the users. As a result, recommendation systems for providing helpfulness of reviews are being developed. This paper argues that cultural background is an important feature that impacts the nature of a review written by the user, and must be considered as a feature in assessing the helpfulness of online reviews. The paper provides an in-depth study of differences in online reviews written by users from different cultural backgrounds and how incorporating culture as a feature can lead to better review helpfulness recommendations. In particular, we analyze online reviews originating from two distinct cultural spheres, namely Arabic and Western cultures, for two different products, hotels and books. Our analysis demonstrates that the nature of reviews written by users differs based on their cultural backgrounds and that this difference varies based on the specific product being reviewed. Finally, we have developed six different review helpfulness recommendation models that demonstrate that taking culture into account leads to better recommendations.