Description

This tutorial is a part of IEEE Education Week Sri Lanka which was held at TRACE Expert City on the 24 th & 25 th of April 2024. Abstract: The talk on "Signal Processing and Machine Learning for Next-Generation IoT Applications" explored the intersection of advanced signal processing techniques and machine learning (ML) methodologies to address the evolving demands of IoT systems. It provided an overview of how signal processing enables efficient data extraction and transmission, while ML enhances IoT applications through data-driven insights and autonomous decision-making.Key topics included supervised learning approaches for location sensing, predictive maintenance, and healthcare monitoring; unsupervised learning for anomaly detection, clustering, and data visualization; and reinforcement learning for optimization and control in dynamic IoT environments. Case studies demonstrated real-world applications such as indoor localization, ambient assisted living (AAL), environmental monitoring, and structural health monitoring, with an emphasis on overcoming challenges like fluctuating RSSI signals in indoor positioning systems (IPS).The talk also highlighted the limitations of traditional deterministic and probabilistic algorithms and showcased how ML algorithms offer improved accuracy, robustness, and system efficiency. Future research directions were discussed, particularly the integration of Reconfigurable Intelligent Surfaces (RIS) with hybrid localization technologies to enhance localization accuracy in dense IoT environments.This session provided a holistic view of how signal processing and ML are shaping the future of IoT, offering attendees insights into the potential and challenges of developing intelligent, adaptive, and reliable IoT solutions.