Trustworthy AI-IoT for Citizen-Centric Smart Cities: The IMTPS Framework for Intelligent Multimodal Crowd Sensing

Area of Science:

• Artificial Intelligence and the Internet of Things (AI-IoT)
• Smart City Governance
• Data Science

Background:

• Smart cities generate vast, heterogeneous data streams from sources like citizen interactions.
• Processing this complex data into actionable intelligence for urban governance is a significant challenge.
• Existing solutions often lack robustness and efficiency in handling multimodal sensing data.

Purpose of the Study:

• To introduce a novel AI-IoT system, the Intelligent Multimodal Ticket Processing System (IMTPS), for processing heterogeneous data streams.
• To develop a theoretically grounded architecture for reliable intelligence extraction from noisy urban data.
• To enhance sustainable urban governance through efficient and trustworthy AI-IoT operations.

Main Methods:

• Orchestration of Information Theory for semantic-preserving data compression, reducing storage and energy costs.
• Application of Game Theory for adversarial verification, ensuring high reliability in data extraction.
• Utilization of Causal Inference for robust multimodal data fusion, distinguishing true causality.
• Implementation of Meta-Learning for adaptive system response to evolving query patterns.

Main Results:

• The IMTPS system demonstrated state-of-the-art performance on a large-scale multimodal dataset.
• Achieved a 96.9% reduction in storage footprint through efficient data compression.
• Reduced critical data extraction errors by 47% using trustworthy verification methods.
• Validated robust multimodal fusion and adaptive learning capabilities.

Conclusions:

• The IMTPS provides a principled and effective solution for converting noisy, heterogeneous AI-IoT data into reliable intelligence for smart cities.
• The system offers significant improvements in efficiency, trustworthiness, and adaptability for urban governance.
• Open-sourcing the implementation aims to facilitate the development of next-generation citizen-centric AI-IoT systems.