MACeIP: A Multimodal Ambient Context-enriched Intelligence Platform in Smart Cities
Truong Thanh Hung Nguyen, Phuc Truong Loc Nguyen, Monica Wachowicz, Hung Cao
TL;DR
The paper addresses the challenge of delivering integrated, citizen-centric smart-city operations by unifying IoT sensing, edge computing, cloud analytics, and multimodal AI. It proposes MACeIP, a platform comprising Interactive Hubs, an IoT sensor network, pedestrian monitoring, public asset management, a City Planning Portal, and a Cloud Computing System, all empowered by Multimodal AI including time-series and vision models, Large Language Models, and Explainable AI to support transparent decision-making. A Fredericton, Canada deployment demonstrates practical data flows, secure data practices, AR/VR-enabled citizen interfaces, and end-to-end interactions between components. The work advances scalable, efficient urban intelligence that can enhance city management, citizen services, and engagement across modern municipalities.
Abstract
This paper presents a Multimodal Ambient Context-enriched Intelligence Platform (MACeIP) for Smart Cities, a comprehensive system designed to enhance urban management and citizen engagement. Our platform integrates advanced technologies, including Internet of Things (IoT) sensors, edge and cloud computing, and Multimodal AI, to create a responsive and intelligent urban ecosystem. Key components include Interactive Hubs for citizen interaction, an extensive IoT sensor network, intelligent public asset management, a pedestrian monitoring system, a City Planning Portal, and a Cloud Computing System. We demonstrate the prototype of MACeIP in several cities, focusing on Fredericton, New Brunswick. This work contributes to innovative city development by offering a scalable, efficient, and user-centric approach to urban intelligence and management.