Owing to its ultra-low power consumption and long-range communication capabilities, LoRa, a prominent Low-Power Wide Area Network (LPWAN) technology, has seen widespread deployment across academic and industrial domains. However, its operation in unlicensed spectrum and reliance on ALOHA-based Medium Access Control (MAC) make it highly susceptible to packet collisions, undermining network reliability and performance. While existing efforts primarily address intra-channel collisions, the effects of cross-channel interference—caused by overlapping transmissions using different bandwidths or spreading factors—remain insufficiently studied, posing challenges to communication quality, scalability, and spectrum efficiency. To bridge this gap, we propose AntScale, a novel collision resolution framework based on antithetical - stationary scaling. Specifically, AntScale introduces two key techniques: (1) it transforms minor time-domain signal distortions into robust frequency-domain features through dual antithetical scaling, enabling effective collision suppression; and (2) it employs a patching algorithm that leverages wide-bandwidth signal characteristics to identify and isolate dominant interference components for targeted decoding. Implementation on a USRP N210 platform demonstrates that AntScale achieves a 2.3× throughput improvement over state-of-the-art methods.

Vibration is a normal reaction that occurs during the operation of machinery and is very common in industrial systems. How to turn fine-grained vibration perception into visualization, and further predict mechanical failures and reduce property losses based on visual vibration information, which has aroused our thinking. In this article, the phase information generated by the tag is processed and analyzed, and MFD is proposed, a real-time vibration monitoring and fault-sensing discrimination system. MFD extracts phase information from the original RF signal and converts it into a Markov transition map by introducing White Gaussian Noise and a low-pass filter for denoising. To accurately predict the failure of machinery, a deep and machine learning model is introduced to calculate the accuracy of failure analysis, realizing real-time monitoring and fault judgment. The test results show that the average recognition accuracy of vibration can reach 96.07%, and the average recognition accuracy of forward rotation, reverse rotation, oil spill, and screw loosening of motor equipment during long-term operation can reach 98.53%, 99.44%, 97.87%, and 99.91%, respectively, with high robustness.

With the rapid development of industry, vibration equipment has become one of the most widely used components for industrial systems. Utilizing vibration sensing and recognition is an effective way to diagnose and understand the working condition of these systems. However, the performance of traditional video/laser-based vibration sensing and recognition solutions varies significantly under different lighting conditions, while the invasive approaches need to directly mounting dedicated sensors to the target, which might pose a threat to its operating safety. To tackle this issue, we propose RF-Vsensing, an RFID-based contactless vibration sensing and recognition method without attaching anything to the target device. Unlike existing methods, RF-Vsensing can realize highly accurate non-contact vibration sensing and recognition using commercial off-the-shelf RFID devices. The evaluation results show that the average accuracy of vibration can reach 96.07% and the average recognition accuracy of clockwise and anticlockwise can reach 99.44%.