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.

Electroencephalography (EEG) signals have emerged as a novel biometric feature in identity authentication. However, in highly sensitive scenarios such as remote access control and sensitive operation confirmation, identity authentication alone is insufficient to ensure system security. This paper proposes EEGAuth, an EEG-based secure and lightweight authentication system with cryptographic key generation, addressing the demand for integrated systems that enhance both security and user convenience by combining identity authentication and key generation into a unified solution. The proposed system employs a genetic algorithm for optimal channel selection, integrates a discrete wavelet transform with an autoencoder-based feature extraction framework, and implements a CNN-based architecture for robust identity authentication. In addition, the system discretizes feature vectors to generate unique and repeatable seeds, which are used as inputs to a secure hash function to produce keys. The evaluation results show that our model achieves a classification accuracy of 99.38% with only 15 channels, significantly outperforming state-of-the-art methods and baseline models. The generated cryptographic keys demonstrate robust security properties, as evidenced by their successful passage through NIST statistical test suite for randomness verification, scale index analysis for aperiodicity assessment, and autocorrelation testing for bit-sequence independence, collectively confirming their resistance to cryptographic attacks and compliance with security standards.

In mobile augmented reality applications, how to build a secure device connection between two previously unassociated devices without prior set-up is challenging, which also refers to the problem of device pairing. Most existing approaches to device pairing either have certain limits to be utilized in the environment of augmented reality or lack considerations of security issues. In this article, we design WVC, an intuitive, user-friendly, and secure device paring system for mobile augmented reality. It enables users to connect to a neighboring device by waving a finger to click towards it in the air. The system uses critical features from finger tracking trajectories to understand which device a user wants to interact with. Then, it designs key generation and correction algorithms to enhance security in device communication. In addition, we present solutions to defend against malicious attacks. We implement and test the system with 10 volunteers. The experimental results demonstrate the feasibility and effectiveness of the system under varied scenarios in which devices are close to each other at a small angle or located at different heights within 2 m .

Recent works have shown that inaudible signals (at ultrasound frequencies) can become audible to the microphone by exploiting the nonlinear effects. With a well-designed inaudible signal, an adversary can control Amazon Echo and Google Homelike devices in people’s rooms silently and remotely. A voice command like “Alexa, open the door“ can be a serious treat. Although recent works design various methods against such inaudible attacks, one important issue remains open: there is no clear solution to locate the attack source accurately. Obviously, the only way to completely eliminate such inaudible threats is to locate and remove the attack source. This paper is an attempt to close this gap. We propose Arrow, an effective method to help users locate the ultrasound attack source in 3D space indoors. Arrow establishes the relationship between inaudible signals and the recorded sounds of the microphone, and then explores the architecture of the embedded microphone array on smart speaker for extracting a 3D direction-specific signature. By learning such directional signature, Arrow can accurately estimate the spatial orientation of the inaudible attack source and help users to locate and remove it. We implement a prototype of Arrow and conduct comprehensive experiments to validate its performance. The results show Arrow can achieve 2.5° and 7° error in DoA(Direction of Arrival) estimation for horizontal and vertical angles, respectively.

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%.

Summary Recently, in a variety of Internet of Things (IoT) application scenarios, fingerprint image as a common biological information has been widely collected. For example, in safe city projects, civil fingerprints representing unique identification are vastly assembled, stored, and transmitted wirelessly. Under such situations, data privacy protection becomes increasingly valued. In our study, this fundamental problem was centered. In our work, the method to remain secure in such an universal cloud based IoT system and the approach to hide important information in biologically collected data such are fully discussed. A new lightweight algorithm in protecting the privacy and integrity of data in a cloud based IoT system is badly needed. We believe that it is a way to hide the core information in the surrounding environment just like “Chameleon” to protect the data security. Thus, in this paper, we propose “CHAMELEON” by using least significant bits (LSB) to hide important information in fingerprint images and chaotic sequence encryption (CSE) to securely transmit useful data. The experimental results of this algorithm show that Chameleon can resist static attacks and protect the privacy of users regarding to the information security and integrity.

Compared with traditional radio frequency identification (RFID), computational RFID (CRFID) tag has more powerful computing capability, but it shows poor performance when it follows EPC Class‐1 Generation‐2 protocol especially transmitting large amounts of data. In fact, the operation of the CRFID tag entirely depends on the state of energy. For this point, this study proposes an optimised protocol called LILAC. LILAC allows tags to select the communication time slot according to their current voltage value measured by using an analogue‐to‐digital converter rather than randomly selecting, the authors proposed a more reasonable time slot mapping algorithm for LILAC that increases the success rate of tag responding. In addition, they design a data transmission format that can be retransmitted to improve the uplink throughput. Finally, they implemented LILAC on a CRFID platform and practically measured several parameters to compare with the existing well‐known protocol. The results of experiments show that LILAC increases the maximum communication distance by more than half in the access phase and doubles the goodput of the backscatter link in both the inventory and access phase.

Summary The main challenge of access control system is how to allow authorized users to access quickly and accurately. In reality, most access control system cannot run smoothly with the adverse impact of a security problem, it is because the access control card is easy to duplicate. To improve system security, recent efforts introduced biological technologies, such as fingerprint identification, facial recognition, and iris recognition. However, these methods are costly for installation and later maintenance. In this paper, we introduce a cloud access control system that employs the ability of sensing acceleration of Wireless Identification and Sensing Platform (WISP) tags combined with customized motions. Users are allowed to pass the access control system only if they operate user‐defined motions correctly. Authorized users can define the authentication motions by themselves, which not only facilitates the daily use but also improves the security of the cloud access control system. We conduct intensive experimental measurements. The experimental results verify the effectiveness of our proposed system.