Simone Grimaldi

TL;DR: Results prove that scalability is especially challenging for Bluetooth mesh since it is prone to broadcast storm, hindering the communication reliability for denser deployments, and introduce randomization in these timing parameters, as well as varying the duration of the Advertising Events.

TL;DR: This work develops a lightweight and efficient method targeting interference identification already at the level of single interference bursts, capable of real-time identification of IEEE 802.11b/g/n, 802.15.1, and Bluetooth Low Energy wireless standards, enabling isolation and extraction of standard-specific traffic statistics even in the case of heavy concurrent interference.

TL;DR: The obtained results show that the fast classification together with a contained sampling frequency ensure the suitability of the method for TSCH-based IWSAN, and the proposed mechanism enables the classification of interference from IEEE 802.11 networks and microwave ovens, while ensuring high classification accuracy with a sensing duration below 300 ms.

TL;DR: In this article, the authors analyze the critical role of real-time interference detection and classification mechanisms that rely on only IIoT devices, without the added complexity of specialized hardware, and analyze the tradeoffs between classification performance and feasibility.

TL;DR: The authors analyze the critical role of real-time interference detection and classification mechanisms that rely on IIoT devices only, without the added complexity of specialized hardware, and explain how to use such mechanisms for enabling IIeT networks to construct and maintain multidimensional interference maps at run-time in an autonomous fashion.