This paper presents a data-driven analysis of the evolving landscape of DSbD from 2019 to 2024, gathering insights from textual documents referencing DSbD. Using a combination of text mining techniques and network analysis, a large corpus of textual documents was examined to identify key entities, including organisations, individuals, and the relationships between them.

This paper presents a data-driven framework to identify and quantify the influence of such features on the visibility and impact of technology-related tweets. The quantitative analysis from statistical modelling reveals that certain content-based features, like the number of words and pronouns used, positively correlate with the impressions of tweets, with increases of up to 2.8%.

This paper explores security vulnerabilities that target RF communications on popular commercial IoT devices such as Wi-Fi, Zigbee, and 433 Mz. Using software-defined radio, a range of attacks were deployed against the devices, including jamming, replay attacks, packet manipulation, protocol reverse engineering, and harmonic frequency attacks. The results demonstrated that all devices used were susceptible to jamming attacks, and in some cases, they were rendered inoperable and required a hard reset to function correctly again.