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

Hrishikesh Dutta, Roberto Minerva, Maira Alvi, Noel Crespi

TL;DR

This paper addresses the challenge of managing large-scale smart city IoT networks by reducing sensing hardware and bandwidth usage through Data-driven Modality Fusion (DMF). DMF leverages cross-modality correlations to synthesize multiple modalities from pollutant-concentration sensors, with computation performed at the core to alleviate edge load. Validated on Madrid's 2023 IoT deployment, DMF achieves multi-modal reconstruction accuracy while substantially lowering sensor counts and communications, and it compares Isolated Target Regressors (ITR) with Unified Target Regressor (UTR) to reveal a performance-complexity trade-off. An eigen-space analysis further characterizes feasibility and guides sensor selection. Overall, DMF presents a scalable, privacy-friendly framework for efficient, robust urban IoT management with potential extensions to incorporate spatial correlations and formal feasibility bounds.

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.

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

TL;DR

This paper addresses the challenge of managing large-scale smart city IoT networks by reducing sensing hardware and bandwidth usage through Data-driven Modality Fusion (DMF). DMF leverages cross-modality correlations to synthesize multiple modalities from pollutant-concentration sensors, with computation performed at the core to alleviate edge load. Validated on Madrid's 2023 IoT deployment, DMF achieves multi-modal reconstruction accuracy while substantially lowering sensor counts and communications, and it compares Isolated Target Regressors (ITR) with Unified Target Regressor (UTR) to reveal a performance-complexity trade-off. An eigen-space analysis further characterizes feasibility and guides sensor selection. Overall, DMF presents a scalable, privacy-friendly framework for efficient, robust urban IoT management with potential extensions to incorporate spatial correlations and formal feasibility bounds.

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.

Paper Structure

This paper contains 8 sections, 7 equations, 9 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Model
  4. Correlation among Sensing Modalities
  5. Data-driven Modality Fusion
  6. Performance Evaluation
  7. DMF Feasibility and Limits: Eigen Space Representation
  8. Conclusions

Figures (9)

  • Figure 1: System model representing smart city IoT data collection, processing and analysis
  • Figure 2: Inter-dependency among pollutant concentrations and meteorological sensing modalities
  • Figure 3: Data-driven Modality Fusion Framework.
  • Figure 4: Comparison between the original and estimated time-series for different sensing modalities
  • Figure 5: DMF Performance with ITR for different model complexity
  • ...and 4 more figures