通过融合多源传感数据，减少城市物联网传感器数量与能耗。
Data-driven Modality Fusion: An AI-enabled Framework for Large-Scale Sensor Network Management
- 基于多模态时序数据相关性，动态融合传感器信息
- 实测数据表明可仅用部分传感器精准估算交通与污染指标
- 适合城市级物联网管理、低功耗设备部署场景
智慧城市的发展依赖于大规模物联网（IoT）网络和传感器基础设施，持续监测城市环境的多个方面。这些网络产生海量数据，带来带宽占用、能耗高和系统扩展性差等挑战。本文提出一种新型感知范式——数据驱动模态融合（Data-driven Modality Fusion, DMF），通过利用不同传感模态间的时间序列数据相关性，减少所需物理传感器数量，从而降低能耗、通信带宽和整体部署成本。该框架将计算复杂度从边缘设备转移到核心端，避免资源受限的物联网设备承担繁重处理任务。在马德里真实物联网部署数据上验证了该系统有效性，证明其能以较少传感器准确估计交通、环境及污染指标。该方案提供了一种可扩展、高效的都市物联网管理机制，并缓解传感器故障与隐私问题。
原文摘要 · Abstract (English)
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.
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