This paper presents a Long Range (LoRa) data aggregation system (LoRaPDA) that aggregates data (e.g., sum, average, min, max) directly in the physical layer. In particular, after coordinating a few nodes to transmit their data simultaneously, the gateway leverages a new multi-packet reception (MPR) approach to compute aggregate data from the phase-asynchronous superimposed signal. Different from the analog approach which requires additional power synchronization and phase synchronization, our MRP-based digital approach is compatible with commercial LoRa nodes and is more reliable. Different from traditional MPR approaches that are designed for the collision decoding scenario, our new MPR approach allows simultaneous transmissions with small packet arrival time offsets, and addresses a new co-located peak problem through the following components: 1) an improved channel and offset estimation algorithm that enables accurate phase tracking in each symbol, 2) a new symbol demodulation algorithm that finds the maximum likelihood sequence of nodes’ data, and 3) a soft-decision packet decoding algorithm that utilizes the likelihoods of several sequences to improve decoding performance. Trace-driven simulation results show that the symbol demodulation algorithm outperforms the state-of-the-art MPR decoder by 5.3$\times$in terms of physical-layer throughput, and the soft decoder is more robust to unavoidable adverse phase misalignment and estimation error in practice. Moreover, LoRaPDA outperforms the state-of-the-art MPR scheme by at least 2.1$\times$for all SNRs in terms of network throughput, demonstrating quick and reliable data aggregation.

This paper presents a Long Range (LoRa) physical-layer data aggregation system (LoRaPDA) that aggregates data (e.g., sum, average) directly in the physical layer. In particular, after coordinating a few nodes to transmit their data simultaneously, the gateway leverages a new multi-packet reception (MPR) approach to compute aggregate data from the asynchronous superimposed signal. Different from traditional MPR approaches that are designed for the uncoordinated collision decoding scenario, our MPR approach allows simultaneous transmissions with small packet arrival time offsets, and addresses the new co-located peak problem through the following components: 1) an improved channel and offset estimation algorithm that enables accurate phase tracking in each symbol; 2) a new symbol demodulation algorithm that finds the maximum likelihood sequence of nodes' data; and 3) a soft-decision packet decoding algorithm that keeps the likelihoods of several sequences to improve decoding performance. Trace-driven simulation results show that the symbol demodulation algorithm outperforms a state-of-the-art MPR decoder by 5.4× in terms of physical-layer throughput, and the soft decoder is more robust to unavoidable adverse phase misalignment and estimation error in practice. Moreover, LoRaPDA outperforms a state-of-the-art MPR scheme by at least 2.1× for all SNRs in terms of network throughput, demonstrating quick and reliable data aggregation.

The majority of semantic segmentation networks generally employ cross-entropy as a loss function and intersection-over-union (IoU) as the evaluation metric for network performance. Employing IoU as a loss function can solve the mismatch issue between the loss function and the evaluation metric. We propose a Soft IoU training strategy based on mini-batch (mini-batch Soft IoU). Our work has two primary contributions: The first is to extend the IoU loss function to a multi-class segmentation network. The second is to collect various categories of the training samples in every mini-batch, which will ensure that the number of categories equals to the batch size at least. Our method breaks the randomness of the original mini-batch gradient descent (GD) strategy, advancing training samples in the mini-batch much more consistent with the distribution characteristics of the overall data. It solves the instability of IoU loss function. In addition, the experimental results on the PASCAL VOC2012 dataset reveal that our method effectively improves the segmentation accuracy of the network and attains significant improvements beyond state-of-the-art IoU loss function methods.