Recent years have witnessed the proliferation of Low-power Wide Area Networks (LPWANs) in the unlicensed band for various Internet-of-Things (IoT) applications. Due to the ultra-low transmission power and long transmission duration, LPWAN devices inevitably suffer from high power Cross Technology Interference (CTI), such as interference from Wi-Fi, coexisting in the same spectrum. To alleviate this issue, this paper introduces the Partial Symbol Recovery (PSR) scheme for improving the CTI resilience of LPWAN. We verify our idea on LoRa, a widely adopted LPWAN technique, as a proof of concept. At the PHY layer, although CTI has much higher power, its duration is relatively shorter compared with LoRa symbols, leaving part of a LoRa symbol uncorrupted. Moreover, due to its high redundancy, LoRa chips within a symbol are highly correlated. This opens the possibility of detecting a LoRa symbol with only part of the chips. By examining the unique frequency patterns in LoRa symbols with time-frequency analysis, our design effectively detects the clean LoRa chips that are free of CTI. This enables PSR to only rely on clean LoRa chips for successfully recovering from communication failures. We evaluate our PSR design with real-world testbeds, including SX1280 LoRa chips and USRP B210, under Wi-Fi interference in various scenarios. Extensive experiments demonstrate that our design offers reliable packet recovery performance, successfully boosting the LoRa packet reception ratio from 45.2% to 82.2% with a performance gain of 1.8 times.
As a representative technology of low power wide area network, LoRa has been widely adopted to many applications. A fundamental question in LoRa is how to improve its reception quality in ultra-low SNR scenarios. Different from existing studies that exploit either spatial or temporal correlation for LoRa reception recovery, this paper jointly leverages the fine-grained spatial-temporal correlation among multiple gateways. We exploit the spatial and temporal correlation in LoRa packets to jointly process received signals so that the fine-grained offsets including Central Frequency Offset (CFO), Sampling Time Offset (STO) and Sampling Frequency Offset (SFO) are well compensated, and signals from multiple gateways are combined coherently. Moreover, a deep learning based soft decoding scheme is developed to integrate the energy distribution of each symbol into the decoder to further enhance the coding gain in a LoRa packet. We evaluate our work with commodity LoRa devices (i.e., Semtech SX1278) and gateways (i.e., USRP-B210) in both indoor and outdoor environments. Extensive experiment results show that our work achieves 4.6dB higher signal-to-noise ratio (SNR) and 1.5× lower bit error rate (BER) compared with existing approaches.
It is our great pleasure to welcome you to Smart City 2022, the 20th IEEE International Conference on Smart City, held on December 18-21, 2022. For the IEEE SmartCity 2022, the financial sponsors are IEEE and IEEE Computer Society, the technical sponsors are IEEE Technical Committee on Scalable Computing, Task Force on Cyber- Physical-Social Systems of IEEE Smart World Technical Committee. The organizers of IEEE SmartCity 2022 are Sichuan University and Hainan University.
Recent researches have mitigated interference by utilizing cloud assistance or cloud-edge collaboration for Low-Power Wide-Area Networks. However, the issue of long interference recovery time prevents these methods from being well utilized in practical scenarios. In this paper, we propose a novel method, called FDR, for Edge-Cloud collaborative interference mitigation with Fuzzy Detection Recovery, which recovers errors in real-time. Our design (i) utilizes gateways and cloud servers and (ii) reduces data transmissions with fuzzy detection codes for real-time error recovery. In our design, each gateway detects and reports the fuzzy positions of errors to the cloud. Then the cloud restores packets with fuzzy detection results. FDR takes the advantage of both the computational ability of the cloud and the error detection benefit of each gateway. We design and implement FDR with commodity devices including LoRa SX1280 and the USRP-B210 platform. Experimental results show that FDR reduces recovery time by 78.53% compared with the state-of-art, and recovers interfered data packets accurately when the packet damage rate reaches 45.72%.
Compared with conventional delivery services, instant delivery usually provides a stricter constraint on delivery time (e.g., 30 minutes). To guarantee the quality of time constraint service, precisely predicting the courier's actual route plays an important role in order dispatching. Most of the existing studies on route prediction are based on single-source data-set such as GPS trajectories or order waybills information, and are not significant to accurately predict the courier's route. This paper focuses on fully leveraging multi-source data to improve the accuracy of route prediction, including the encounter data, active site report data and GPS trajectories. To achieve this, we propose a multi-source data fusion framework for route prediction. It consists of (i) a multi-source features extracting and fusion module to address the challenge of the heterogeneity of multisource data; (ii) a prediction module taking full advantage of features with different aspects of information containing noise. We evaluate our approach with real-world data collected from one of the largest instant delivery companies in China, i.e., Eleme. Experimental results show that the performance of our multisource data fusion-based prediction model outperforms other state-of-the-art baselines, and achieves a precision of 83.08% for route prediction.
Human identification is a key requirement for many applications in everyday life, such as personalized services, automatic surveillance, continuous authentication, and contact tracing during pandemics, etc. This work studies the problem of cross-modal human re-identification (ReID), in response to the regular human movements across camera-allowed regions (e.g., streets) and camera-restricted regions (e.g., offices) deployed with heterogeneous sensors. By leveraging the emerging low-cost RGB-D cameras and mmWave radars, we propose the first-of-its-kind vision-RF system for cross-modal multi-person ReID at the same time. Firstly, to address the fundamental inter-modality discrepancy, we propose a novel signature synthesis algorithm based on the observed specular reflection model of a human body. Secondly, an effective cross-modal deep metric learning model is introduced to deal with interference caused by unsynchronized data across radars and cameras. Through extensive experiments in both indoor and outdoor environments, we demonstrate that our proposed system is able to achieve ~ 92.5% top-1 accuracy and ~ 97.5% top-5 accuracy out of 56 volunteers. We also show that our proposed system is able to robustly reidentify subjects even when multiple subjects are present in the sensors' field of view.
This paper focuses on the rotor thermal design of the axial flux permanent magnet synchronous machine (AFPMSM) with housing water-cooling. By adapting the effect of disc rotor self-ventilation, an enhanced cooling method has been innovated to optimize the temperature distribution of the rotor in AFPMSM. Two scenarios will be created to compare their influence on cooling enhancement of rotor with the diverse types of fins, the annular fin and the rectangular fin, each will be installed separately to the internal surface of housing. There is a significant effect by using this method in all types of AFPMSM with housing cooling, especially for multi-rotor ones. The results of CFD show that the effectiveness of annular fins is essentially enhanced, with 25% more heat dissipation achieved.
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