The College of Computing and Informatics hosted Dr. Ahcene Bounceur, Associate Professor of Computer Science at the University of Brest (UBO) for virtual seminar via Teams for Faculty, Staff, and students titled "CupCarbon: A Platform for Designing and Simulating IoT and Wireless Sensor Networks Dedicated to Secured Smart City Applications".
The proliferation of radio communication systems and the significant advances in enabling device technologies are paving the roads towards Internet-of-Things (IoT) and opening new horizons for Smart City applications and its services. Such evolution became essential to enhance the security, quality of urban services, reduce costs, and actively engage citizens more than any earlier time.
In this context, novel simulation tools are required to prepare the future deployments of large-scale IoT infrastructure for smart cities in the best conditions in terms of security, reliability, energy consumption, rerouting, and cost.
In this seminar, Dr. Ahcene Bounceur presented new secured and dependable solutions in Wireless Sensors and IoT Networks dedicated to smart-cities and other fields, in addition to a set of tools and numerous scenarios of the new generation networks. He also demonstrated the CupCarbon simulator and Pseudo-polygon, two cutting-edge tools and concepts.
Dr. Ahcene Bounceur used the OpenStreetMap module on CupCarbon to simulate distributed algorithms, radio transmission, propagation and interference, and mobility while considering buildings, roads, and traffic. He then demonstrated how the new CupCarbon interface is ergonomic and simple to use, with the ability to visualize the algorithm's execution and how it helps with the debugging of the algorithm, ensuring that it works in the end.
Afterward, Dr. Ahcene Bounceur went over the Pseudo-polygon theory in greater depth, employing a sub-graph structure that resembles a polygon. He next investigated the concept of polygon modeling as an internal or external polygon, depending on the angles drawn, demonstrating how to use the D-LPCN and D-RRLPCN algorithms to determine the border nodes of any network, as well as their simulation on CupCarbon and implementation in real sensor or IoT nodes, using this technique.
The same techniques may be used for non-covered area identification, data clustering, fingerprint characterization, contour drawing in medical imaging, random key generation, and any other circumstance that can be handled as a Euclidean network, according to the talk.
Dr. Ahcene Bounceur concluded by outlining the initial component of the IoT Digital Twin incorporated in CupCarbon and its use for real-time traffic simulation, which included both actual and virtual cars and routes.