We consider a communication cell comprised of Internet-of-Things (IoT) nodes transmitting to a common Access Point (AP). The nodes in the cell are assumed to generate data samples periodically, which are to be transmitted to the AP. The AP hosts a machine learning model, such as a neural network, which is trained on the received data samples to make accurate inferences. We address the following tradeoff: The more often the IoT nodes transmit, the higher the accuracy of the inference made by the AP, but also the higher the energy expenditure at the IoT nodes. We propose distributed importance filtering, a scheme employed by the IoT nodes to filter out the redundant data and reduce the number of irrelevant transmissions. The IoT nodes do not have large on-device machine learning models and the data filtering scheme operates under periodic instructions from the model placed at the AP. The proposed scheme is evaluated using neural networks on a benchmark machine vision dataset, as well as in two practical scenarios: leakage detection in water distribution networks and air-pollution detection in urban areas. The results show that the proposed scheme offers significant benefits in terms of network longevity as it preserves the devices’ resources, whilst maintaining high inference accuracy. Our approach reduces the computational complexity for training the model and obviates the need for data pre-processing, which makes it highly applicable in practical IoT scenarios.

中文翻译：

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通过重要性过滤更新对无线物联网链路进行推理


我们考虑由物联网 (IoT) 节点组成的通信单元，这些节点向公共接入点 (AP) 进行传输。假定小区中的节点定期生成数据样本，并将其发送到 AP。 AP 托管一个机器学习模型，例如神经网络，它根据接收到的数据样本进行训练以做出准确的推理。我们解决以下权衡问题：物联网节点传输的频率越高，AP 推断的准确性越高，但物联网节点的能源消耗也越高。我们提出了分布式重要性过滤，这是物联网节点用来过滤冗余数据并减少不相关传输数量的方案。物联网节点没有大型设备上机器学习模型，数据过滤方案在来自接入点的模型的定期指令下运行。使用神经网络在基准机器视觉数据集以及两个实际场景中对所提出的方案进行评估：配水网络中的泄漏检测和城市地区的空气污染检测。结果表明，所提出的方案在网络寿命方面具有显着的优势，因为它保留了设备资源，同时保持了高推理精度。我们的方法降低了训练模型的计算复杂性，并消除了数据预处理的需要，这使得它非常适用于实际的物联网场景。