Congratulations to our IoT lab members Dana Haj Hussein and Ali Farhat for securing joint first place in Carleton University’s Data Day 9.0 Poster Competition hosted by Carleton Science.
The two winning posters presented part of their research work towards the PhD degree. Both PhD candidates are tied for first place of the at large poster competition and each of them will be receiving a monetary prize. Dana’s poster titled “Designing for the Unknown: Paving the Way Towards Intelligent and Autonomous Resource Management in Future Internet of Things Systems” proposes a novel IoT traffic modeling framework and an intelligent edge resource slicing algorithm that addresses IoT data scarcity and optimizes resource allocation on edge-enabled IoT networks. Ali’s Poster titled “Trust-Management Module for IoT Systems: An Interaction-based Machine Learning Approach” discusses part of his PhD research on developing a trust-based solution for IoT systems that utilizes machine learning algorithms. The innovative research work presented by Ali and Dana showcases the high quality of research conducted by our IoT lab members and their contributions towards addressing key challenges in the IoT domain.
Ali’s poster discusses the challenges related to security and privacy in IoT systems and proposes a trust-based solution that establishes trust based on device-system interactions without requiring additional information. The proposed module computes the trust value of an IoT device using communication and security attributes and utilizes a neural network to identify malicious devices or those with low trust value due to performance degradation.
Dana’s poster discusses the challenges of IoT data scarcity and the need for intelligent resource management algorithms to address the complexities of future IoT systems. The contributions of the research presented are a novel IoT traffic modeling framework, TMMSP, that generates synthetic traffic datasets and an intelligent and autonomous edge resource slicing algorithm that optimizes resource allocation on edge enabled IoT networks.