Data-driven Modality Fusion: An AI-enabled Framework for Large-Scale Sensor Network Management

2025-02-07

AI Summary

To address energy efficiency and scalability bottlenecks in large-scale IoT sensor networks for smart cities, this paper proposes a Data-driven Modality Fusion (DMF) paradigm. DMF innovatively leverages cross-modal temporal correlations to compress the number of physical sensors—exploiting intrinsic dependencies among heterogeneous, multi-source sensing data—within an edge-light sensing and cloud-coordinated modeling architecture. Evaluated on a real-world IoT deployment in Madrid, DMF achieves high-fidelity reconstruction of traffic, environmental, and pollution metrics using only 30% of the original sensors, reducing bandwidth and energy consumption by over 65% while significantly improving fault tolerance. Key contributions include: (1) an unsupervised, cross-modal temporal correlation–driven sensor compression mechanism; (2) a cloud-edge collaborative computation migration strategy; and (3) an end-to-end lightweight framework with implicit privacy preservation.

Technology Category

• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The development and operation of smart cities relyheavily on large-scale Internet-of-Things (IoT) networks and sensor infrastructures that continuously monitor various aspects of urban environments. These networks generate vast amounts of data, posing challenges related to bandwidth usage, energy consumption, and system scalability. This paper introduces a novel sensing paradigm called Data-driven Modality Fusion (DMF), designed to enhance the efficiency of smart city IoT network management. By leveraging correlations between timeseries data from different sensing modalities, the proposed DMF approach reduces the number of physical sensors required for monitoring, thereby minimizing energy expenditure, communication bandwidth, and overall deployment costs. The framework relocates computational complexity from the edge devices to the core, ensuring that resource-constrained IoT devices are not burdened with intensive processing tasks. DMF is validated using data from a real-world IoT deployment in Madrid, demonstrating the effectiveness of the proposed system in accurately estimating traffic, environmental, and pollution metrics from a reduced set of sensors. The proposed solution offers a scalable, efficient mechanism for managing urban IoT networks, while addressing issues of sensor failure and privacy concerns.

Problem

• Enhances smart city IoT network efficiency
• Reduces physical sensors and energy usage
• Manages sensor failure and privacy concerns

Innovation

• Data-driven Modality Fusion enhances efficiency
• Reduces physical sensors via timeseries correlations
• Relocates computational complexity to core systems