Sathiyamoorthy M

Multi-Agent Reinforcement Learning-based Autonomous Flood Response and Early Warning System of Smart Cities

Deep Learning in IoT: Advancing Automation, Security, and Smart Services

The rapid growth of the Internet of Things (IoT) has significantly improved intelligent automation across domains such as smart healthcare, industrial automation, and smart cities. However, conventional IoT systems continue to face challenges related to limited automation intelligence, evolving cyber threats, high comm...

Low-Latency Urban Flood Forecasting and Early Warning System Based on Edge-Cloud Collaborative AI Architecture

Infrastructure and overall community safety and economic stability of fast urbanizing areas are in jeopardy due to urban flooding. A new Edge-Cloud Collaborative AI Architecture is proposed in this paper, which integrates Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN) and attention mechanis...

AI-Driven RIS-Assisted 5G Wireless Sensor Networks for Energy-Efficient Smart City Communications

In order to handle the growing energy demand, water scarcity, waste production, and environmental sustainability issues, smart cities need intelligent resource management. But current resource management frameworks generally optimize resource use in each infrastructure separately, reducing the ability of coordinated de...

Explainable Artificial Intelligence Framework for Urban Air Quality Prediction and Environmental Risk Assessment

Multi-Agent Reinforcement Learning for Energy-Efficient Smart Grid Optimization

Smart grid incorporates the use of renewable energy, distributed generation, energy storage, and demand response for enhancing efficiency and sustainability. However, smart grid operation is challenging because of uncertainty of renewable generation, fluctuating electricity prices, changing consumer demand, and the req...

A Multi-Objective AI Framework for Robust and Scalable Urban Flood Early Warning Under Climate Uncertainty

The problem of urban flooding has been increasing with the climate variability, rapid urbanization process, and the increased uncertainty in the rainfall patterns. Conventional flood prediction models are usually based on single-objective optimization, which restricts their capacity to optimise competing needs which in...

Cross-Modal Transformer Networks for Unified Intelligence in Multi-Sensor IoT Systems

The emergence of multi-sensor Internet of Things (IoT) deployments has led to an immediate demand to have unified intelligence models that can take advantage of heterogeneous data modalities, such as time-series sensor readings, visual data, audio streams as well as contextual metadata, to form incoherent understanding...

Continual Learning Framework for Drift-Resilient and Autonomous IoT Intelligence

The ubiquitous use of Internet of Things (IoT) system in dynamic and real-life contexts presents a high level of challenges because of the constant changes in the data distributions, otherwise known as concept drift. The conventional machine learning models that are implemented in IoT systems are usually trained in sta...

Sustainable Early Warning System for Urban Flood Risk Assessment using Hybrid CNN-LSTM and Remote Sensing Data

Urban flooding is a major issue to sustainable urban development and disaster risk mitigation. A sustainable AI-based early warning system is suggested to improve the predictive effectiveness and reactiveness and is based on a hybrid deep learning architecture to provide real-time flood hazard estimates. It combines Lo...

6G-Integrated Federated Learning for Trustworthy and Scalable Edge-IoT Collaboration

The intersection of sixth-generation (6G) communication networks, edge computing, and federated learning offers a novel occasion regarding empowering trustful and scalable collaboration throughout distributed Internet of Things (IoT) ecosystems. Conventional centralized machine learning technology is plagued by severe...