The primary objective of this project is to develop a spatially intelligent smart green building platform that enhances indoor air quality (IAQ) monitoring and management through AI-driven analytics and IoT devices. This platform aims to revolutionize smart building management by leveraging real-time sensor data, AI-based anomaly detection, and multi-modal retrieval-augmented generation (RAG) to improve air quality and environmental conditions in commercial and residential buildings. Key focus areas include:

• Integration with External Data Sources: Connect with public air quality datasets, IoT sensor networks, and corporate environmental reports to enhance IAQ analytics.
• Localized LLMs with Multi-Modal RAG: Use retrieval-augmented generation (RAG) techniques for analyzing sensor data and generating actionable insights on air quality trends.
• AI-Agent Solutions for Abnormality Detection: Deploy AI-driven agents to identify air quality anomalies and provide automated handling solutions.
• Spatial Intelligence for IAQ Optimization: Utilize location-based data to optimize ventilation, HVAC control, and pollutant mitigation strategies.
• Automation of IAQ Monitoring and Reporting: Implement AI-driven analytics to replace manual assessments with real-time, data-driven decision-making.
• Enhanced User Interface & Experience: Develop an intelligent dashboard that provides interactive visualizations and alerts for indoor air quality monitoring.
• Advanced AI-Driven Report Generation: Utilize IoT sensor data and generative AI to create detailed reports on air quality trends, pollutant sources, and mitigation strategies.

Tasks for Students

To achieve these objectives, students will complete the following tasks:

1. Conduct Background Research

• Investigate the challenges of IAQ monitoring and the impact of air pollutants on health and building efficiency.
• Explore best practices in spatial intelligence and IoT-based environmental monitoring.
• Identify potential external air quality datasets and public sources relevant to IAQ analysis.

2. Analyze the Current Dataset and External Data Sources

• Examine the IoT sensor data, air quality metrics, and environmental parameters provided by the company.
• Connect and preprocess external air quality datasets, including government air quality indexes, real-time pollution monitoring networks, and weather reports.
• Ensure data quality and consistency for multi-source IAQ analytics.

3. Research Latest AI/ML Techniques

• Investigate state-of-the-art AI/ML models for air quality forecasting, pollutant source detection, and anomaly detection.
• Evaluate Multi-Modal RAG models for real-time IAQ data retrieval, analysis, and automated reporting.
• Explore AI-agent solutions for anomaly detection and automated handling of IAQ fluctuations.

4. Develop AI/ML Models

• Implement AI-driven models tailored for IAQ monitoring and anomaly detection, such as:
• Predictive Analytics: Forecast air quality fluctuations based on historical sensor data and environmental conditions.
• AI-Agent Abnormality Detection: Deploy AI agents to monitor sudden spikes in pollutants and trigger mitigation actions.
• Classification Models: Categorize indoor spaces based on air quality risks and HVAC efficiency.
• Multi-Modal RAG for IAQ Insights: Implement localized LLMs to generate real-time air quality insights and recommend corrective actions.
• Train and validate models to ensure high accuracy and responsiveness.

5. Generate IAQ Reports Using AI

• Integrate AI models to automatically generate IAQ reports for smart buildings.
• Ensure compliance with industry IAQ standards and building sustainability regulations.
• Incorporate spatial intelligence and multi-modal RAG insights to provide dynamic, location-based IAQ assessments.
• Develop AI-driven automated alerts for pollutant spikes and HVAC inefficiencies.

6. Enhance User Interface for IAQ Monitoring

• Develop a sophisticated, interactive dashboard for IAQ visualization and decision-making.
• Implement dynamic visualizations such as heat maps, pollutant trend analysis, and spatial air quality mapping.
• Enable real-time interaction with IAQ data using AI-powered alerts and recommendations.
• Ensure an intuitive design for enhanced user experience and accessibility.

7. Documentation and Reporting

• Provide a comprehensive technical report detailing data analysis, model development, and IAQ report generation.
• Deliver documentation outlining system architecture, AI-agent workflows, and model deployment guidelines.

Final Deliverables

Program Codes

• Python scripts for data processing, AI model training, anomaly detection, and IAQ report generation.
• JavaScript-based interactive UI for IAQ report visualization.
• README with setup instructions and deployment guidelines.

Developed Models

• Trained models with documentation on architecture, hyperparameters, and performance metrics.
• Serialized models for easy integration and deployment.
• AI-agents for automated anomaly detection and IAQ monitoring.

Final Report

• Detailed dataset analysis, including spatial intelligence applications in IAQ monitoring.
• Integration of external datasets and public sources for enhanced IAQ insights.
• Step-by-step setup guide for AI/ML models and dashboard integration.
• Summary of AI-driven insights for building ventilation strategies and air quality management.
• Impact assessment of AI-agent abnormality detection and automated handling strategies.

Mentorship Plan

To ensure students successfully generate AI-driven IAQ reports and optimize the user experience, the project will include dedicated mentorship and support.

1. Expert Mentorship

• Assign mentors with expertise in spatial intelligence, AI/ML, IoT sensor integration, and air quality management.
• Conduct regular check-ins and technical guidance sessions.

2. Technical Resources

• Provide documentation on IoT air quality sensors, public datasets, and AI-powered data analysis tools.
• Share annotated datasets, APIs, and integration guidelines.

3. AI/ML Support

• Provide curated learning materials on AI techniques for IAQ forecasting and pollutant detection.
• Offer debugging support and optimization strategies for AI-agent-driven anomaly detection.

4. UI/UX Enhancements

• Guide students on developing sophisticated dashboards with real-time AI insights and anomaly alerts.
• Ensure user-centric design principles for an enhanced IAQ monitoring experience.

5. Evaluation and Feedback

• Conduct iterative reviews on model accuracy, anomaly detection quality, and UI effectiveness.
• Organize final presentations for student deliverables with expert feedback.