With the proliferation of Internet of Things (IoT) devices in the consumer market, the unprecedented sensing capability of IoT devices makes it possible to develop advanced sensing and complex inference tasks by leveraging heterogeneous sensors embedded in IoT devices. However, the limited power supply and the restricted computation capability make it challenging to conduct seamless sensing and continuous inference tasks on resource-constrained devices. How to conduct energy-efficient sensing and perform rich-sensor inference tasks on IoT devices is crucial for the success of IoT applications. Therefore, we propose a novel energy-efficient collaborative sensing framework to optimize the energy consumption of IoT devices. Specifically, we explore the latent correlations among heterogeneous sensors via an attention mechanism in temporal convolutional network to quantify the dependency among sensors, and characterize the heterogeneous sensors in terms of energy consumption to categorize them into low-power sensors and energy-intensive sensors . Finally, to decrease the sampling frequency of energy-intensive sensors , we propose a multi-task learning strategy to predict the statuses of energy-intensive sensors based on the low-power sensors . To evaluate the performance of the proposed collaborative sensing framework, we develop a mobile application to collect concurrent heterogeneous data streams from all sensors embedded in Huawei Mate 8. The experimental results show that latent correlation learning is greatly helpful to understand the latent correlations among heterogeneous streams, and it is feasible to predict the statuses of energy-intensive sensors by low-power sensors with high accuracy and fast convergence. In terms of energy consumption, the proposed collaborative sensing framework is able to preserve the energy consumption of IoT devices by nearly 50% for continuous data acquisition tasks.

中文翻译：

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节能协同传感：学习异构传感器的潜在相关性

随着物联网 (IoT) 设备在消费市场中的普及，物联网设备前所未有的传感能力使得通过利用嵌入在物联网设备中的异构传感器来开发高级传感和复杂推理任务成为可能。然而，有限的电源和有限的计算能力使得在资源受限的设备上进行无缝传感和连续推理任务具有挑战性。如何在物联网设备上进行节能传感和执行丰富的传感器推理任务对于物联网应用的成功至关重要。因此，我们提出了一种新的节能协同传感框架来优化物联网设备的能耗。具体来说，低功耗传感器和能量密集型传感器. 最后，降低采样频率能量密集型传感器，我们提出了一种多任务学习策略来预测能量密集型传感器基于低功耗传感器. 为了评估所提出的协同感知框架的性能，我们开发了一个移动应用程序来收集来自华为 Mate 8 中嵌入的所有传感器的并发异构数据流。实验结果表明，潜在相关性学习对于理解异构流之间的潜在相关性有很大帮助, 预测状态是可行的能量密集型传感器经过低功耗传感器精度高，收敛速度快。在能耗方面，所提出的协同传感框架能够将物联网设备的能耗降低近 50%，用于连续数据采集任务。