Conference Proceedings  

The Objective of this research is an AI-powered IoT framework designed for real-time environmental monitoring in smart cities, aiming to enhance urban sustainability and inform data-driven decision-making. It explores the integration of various sensors and AI algorithms to address environmental challenges, ultimately contributing to improved urban living conditions and resource management.

The methodology includes a systematic literature review, the design of an AI-based IoT framework, and the analysis of environmental monitoring techniques. It encompasses data collection, preprocessing, and the application of machine learning and deep learning algorithms for predictive analytics.

Smart cities have to monitor environmental parameters such as air and water quality, which can be done in real time using an IoT framework powered by AI technology. 

Applies ML/DL methods to assess environmental data, project risks, and provide technological backing for proactive management of urban resources. 

To present a scalable framework combined with secure communication protocols and privacy-preserving mechanisms for data integrity and its adaptability. 

We see applications in city planning, including solving problems like traffic and pollution, to make more intelligent and sustainable ecosystems within cities.

This research explores the integration of solar energy technologies in urban settings, assessing their feasibility and benefits for buildings, roads, and public infrastructure. It highlights advancements in solar technology, evaluates economic and environmental impacts, and discusses challenges to widespread adoption, ultimately advocating for sustainable urban development through solar integration.

The methodology employed in this research includes a literature review of existing solar technologies, case studies of successful solar integration projects, and an analysis of technical and economic assessments related to solar energy applications in urban environments.

Investigate the introduction of solar energy into architecture and advance the infrastructure of roads and public structures to contribute towards sustainable urban development. 

Focuses on innovations such as BIPV, solar roadways, and energy storage systems to give utility to implementors and facilitate adoption. 

This paper discusses some of the technical, financial, and policy barriers that need to be addressed to harness solar technologies for a sustainable urban future.

In this research, We explored and developed a theoretical framework for integrating AI-based predictive modeling and computational design into the retrofitting process, focusing on enhancing the performance and sustainability of existing buildings.

The methodology involves developing a theoretical framework that combines AI simulation and optimization techniques. It includes creating prototypes to assess energy efficiency and environmental performance in the retrofitting process for aging buildings.

Using off-grid, floating solar arrays that use flood-resistant materials for both durability and adaptability in areas vulnerable to flooding along coasts with many socioeconomic groups but little access to cost-effective sources of sustainable energy. 

Developing a scalable model for combining technical resilience with socioenvironmental aspects; seeking reliable energy provision, grid stability, and long-endurance during climate-induced disasters in coastal regions of Bangladesh.

This research distinguishes itself by focusing specifically on the integration of floating solar technologies with disaster resilience measures in Bangladesh's coastal regions. My contribution includes a detailed analysis of the socio-environmental impacts and the development of a scalable model for community-based energy solutions.

The methodology includes a literature review, prototype design development, simulation and analysis using computational tools (like Grasshopper and Ladybug), and performance metrics evaluation. The research employs both qualitative and quantitative analyses to assess the feasibility of the proposed floating solar framework.

A framework for retrofitting existing buildings through AI simulation and optimization to improve energy distribution, responsiveness, electric grid, and environmental performance towards cost-effective net-zero emissions in urban areas. 

We are developing adaptive strategies for urban planners, policymakers, and architects to upgrade obsolete infrastructures into energy-efficient and sustainable buildings to respond not only to contemporary challenges of energy but also integrate within the evolving smart grid.