Physical-layer key generation holds significant potential in establishing cryptographic key pairs for emerging LoRa networks. Nevertheless, current key generation solutions may underperform due to critically impaired channel reciprocity, attributed to the low data rate and long range inherent in LoRa networks. In this study, we presentChirpKey, a novel key generation scheme for LoRa networks. We pinpoint the key hurdles as the coarse-grained channel measurement, inefficient quantization methods, and out-of-range device constraints. To capture fine-grained channel information, we introduce a unique, LoRa-specific channel measurement method that focuses on analyzing chirp-level variations in LoRa packets. We also propose a LoRa channel state estimation algorithm to neutralize asynchronous channel sampling. Instead of the traditional quantization approach, we propose an innovative key delivery method based on perturbed compressed sensing, offering enhanced robustness and security. For LoRa devices beyond each other's communication reach, we integrate relay nodes to ensure reliable key generation. To foster secure group communication, we formulate two protocols that facilitate collaborative key generation across both star and chain configurations. Evaluation across diverse real-world scenarios reveals thatChirpKeyenhances the key matching rate by 11.03–26.58% and increases the key generation rate by 27–49× in comparison to existing leading systems. Our security analysis shows thatChirpKeycan effectively withstand a variety of prevalent attacks. Furthermore, we implement aChirpKeyprototype, demonstrating its capability to operate within 0.2 s.

The recent proliferation of spacecraft in Earth's orbits has ushered in the rise of large-scale satellite constellations. However, this unprecedented growth of constellations has introduced a previously unforeseen challenge: ground track congestion. Specifically, the increasing density of orbital slots forces satellites to share similar orbit planes, causing their nadir-point projections on Earth's surface (i.e., ground tracks) to overlap or remain in close proximity within short time intervals. Such orbit-endowed ground track congestion can degrade constellation performance in remote sensing operations, specified by limited constellation coverage, redundant satellite count, and delayed data delivery.

Antimicrobial peptides (AMPs) are short peptides that play critical roles in diverse biological processes and exhibit functional activities against target organisms. While numerous methods have demonstrated the effectiveness of deep neural networks for AMP identification using sequence features; nevertheless, higher-level peptide characteristics-such as 3D structure and geometric surface features-have not been comprehensively explored. To address this gap, we introduce the SSFGM-Model (Sequence, Structure, Surface, Graph, and Geometric-based Model), a novel framework that integrates multiple feature types to enhance AMP identification. The model represents each peptide sequence as a graph, where nodes are characterized by amino acid features derived from ProteinBERT, ESM-2, and One-hot embeddings. Graph convolutional networks and an attention mechanism are employed to capture high-order structural and sequential relationships. Additionally, surface geometry and physicochemical properties are processed using a geometric neural network. Finally, a feature fusion strategy combines the outputs from these subnetworks to enable robust AMP identification. Extensive benchmarking experiments demonstrate that the SSFGM-Model outperforms current state-of-the-art methods. An ablation study further confirms the critical role of sequence, structural, and surface features in AMP identification. The key contribution of this work is the innovative integration of multiple levels of peptide characteristics and the combination of geometric and graph neural networks. This approach provides a more comprehensive understanding of the sequence-structure-function relationship of peptides, paving the way for more accurate AMP prediction. The SSFGM-Model has a significant potential for applications in the discovery and design of novel AMP-based therapeutics. The source code is publicly available at https://github.com/ggcameronnogg/SSFGM-Model.

Collaborative sensing has emerged as a novel sensing paradigm, entailing multi-sensor data sharing and multimodal modeling to collaboratively understand sensing behaviors. However, current solutions, i.e., data-level and decision-level fusion methods, fall short of generality, expert knowledge, and holistic/chronic perspective. In this paper, we proposeLLMCoSento revisit collaborative sensing with Large Language Models (LLMs). Specifically,LLM-CoSendesigns a semantic-level fusion approach for inference results for collaborative sensing. Such an approach is characterized by its generality, making it applicable to any heterogeneous devices, and its expert knowledge incorporation, which provides chronic, holistic, and insightful perspectives on the inference results. Regarding inference absence challenges, we propose a personalized model design method to constrain inference time, and a voting-based two-pass prompt engineering strategy for token completion. Regarding inference error challenges, we propose an accuracy restoration strategy for personalized models, and a two-level error estimator coupled with self-correction. Experimental results of human digital system use case on four corresponding benchmark datasets showLLM-CoSencan decrease inference absence by 72.83% and inference errors by 7.65% on average.

Multi-port chargers, designed to simultaneously charge multiple mobile devices such as smartphones, have gained significant popularity, with millions of units sold in recent years. However, this multi-device charging feature introduces security and privacy risks. If not properly designed and implemented, these chargers can enable communication between connected devices because they are inherently interconnected, which leads to crosstalk voltage leakages. Despite their widespread use, these risks have not been thoroughly investigated. We have identified novel attack surfaces in the circuit design of multi-port chargers that allow an adversary who shares the multi-port charger with the target victim in close proximity to exploit one port to (i) recognize fine-grained user activities of other devices being charged, (ii) eavesdrop on secret audio transmission from USB-C audio pins, and (iii) inject malicious audio commands into built-in voice assistants of charging devices (e.g., Siri, Google Assistant). In this paper, we design and implement XPORTHEFT, a novel system to analyze and demonstrate the uncovered security and privacy threats in multi-port chargers. Specifically, it leverages changes in voltage signals in one neighbor port to monitor voltage changes in the charging port induced by user activities in various user interfaces, such as recognizing running apps and detecting keystrokes. Moreover, XPORTHEFT can also achieve audio transmission eavesdropping and launch inaudible audio injection attacks from the neighbor port to the charging mobile device via the USB-C interface. We extensively evaluate the effectiveness of XPORTHEFT using five commercial multi-port chargers and five mobile devices. The evaluation results show its high effectiveness in recognizing the launch of 20 mobile apps (88.7%) and revealing unlocking passcodes (98.8%), as well as eavesdropping on the audios of numeric digits (97.1%) and alphabetic characters (98.0%). Furthermore, XPORTHEFT achieves 100% success rates in inaudible audio injection attacks on three commercial voice assistants. In addition, our study also shows that XPORTHEFT is resilient to various impact factors and presents the potential to attack multiple victims.

Federated learning has recently been applied to recommendation systems to protect user privacy. In federated learning settings, recommendation systems can train recommendation models by collecting the intermediate parameters instead of the real user data, which greatly enhances user privacy. In addition, federated recommendation systems (FedRSs) can cooperate with other data platforms to improve recommendation performance while meeting the regulation and privacy constraints. However, FedRSs face many new challenges such as privacy, security, heterogeneity, and communication costs. While significant research has been conducted in these areas, gaps in the surveying literature still exist. In this article, we: 1) summarize some common privacy mechanisms used in FedRSs and discuss the advantages and limitations of each mechanism; 2) review several novel attacks and defenses against security; 3) summarize some approaches to address heterogeneity and communication costs problems; 4) introduce some realistic applications and public benchmark datasets for FedRSs; and 5) present some prospective research directions in the future. This article can guide researchers and practitioners understand the research progress in these areas.

Protein pocket is a special region on the surface of a protein that commonly interacts with other molecules, especially small molecule compounds. Accurately identifying and understanding the structure character of protein pockets, that can accelerate the development of new drugs, as well improve existing drugs to reveal disease mechanisms. In this study, we propose a deep learning method named PocketLG, to identify the potential binding pockets integrating a Protein Language model and Graph Transformer. The method constructs the pocket identification task as a regression problem, which can more accurately identify the real pocket boundaries. In addition, We propose a data sampling strategy and construct a pipeline to support the data sampling effort. This strategy has the benefit of augmenting the data, allowing the model to better learn the intrinsic characteristics of the pocket structure, even for pockets with unknown boundaries This method can sample the unknown pocket by dividing different sample sizes and then determine the boundary of the unknown pocket by the regression value, it can be a direct help for downstream tasks. Experimental results show that our deep learning model can identify binding pockets on proteins with 91 percent success rate. This work provides a new technical route for protein binding pocket prediction research, which can greatly contribute to the development of the pharmaceutical industry. PocketLG source code, pre-trained models and data preprocessing code are available at https://github.com/NENUBioCompute/PocketLG.

Eggs and chickens serve as crucial animal-source proteins in our diets, making large-scale breeding egg incubation an essential undertaking. However, current solutions, i.e., vision-based and sensor-based methods, are primarily designed for egg fertility detection tasks under single-egg settings, which have not yet satisfied the goal of multi-target and multi-task sensing. In this paper, we propose RF-Egg, the first RF-based fine-grained multi-target and multi-task egg incubation sensing system with respect to sensing fertility, incubation status, and early mortality of chicken embryos. RF-Egg leverages the weak coupling effects of RFID tags when interacting with eggs, which induces different impedance changes of RFID tags with the incubation levels of eggs, thereby resulting in a variation of low-level phase readings of the backscatter signals. Regarding the challenge of multi-target profiling interference, we propose a multipath combating algorithm to extract the target-induced signal component based on the built signal model, and address non-uniformity issues across multiple tags. Moreover, we devise three unique feature maps tailored to each task, and then design an Multi-Task Triplet (MTT) network for multitasking. Our evaluation results based on 189 eggs show that RF-Egg achieves an accuracy of 94.4%, 96.1%, and 90.1% for the aforementioned three tasks when supporting 16 targets. Additionally, our extensive field study in a local egg hatchery suggests that RF-Egg presents the potential to be widely deployed in the modern poultry industry.

Diverse Wi-Fi-based wireless applications have been proposed, ranging from daily activity recognition to vital sign monitoring. Despite their remarkable sensing accuracy, the high energy consumption and the requirement for customized hardware modification hinder the wide deployment of the existing sensing solutions. In this paper, we propose REHSense, an energy-efficient wireless sensing solution based on Radio-Frequency (RF) energy harvesting. Instead of relying on a power-hungry Wi-Fi receiver, REHSense leverages an RF energy harvester as the sensor and utilizes the voltage signals harvested from the ambient Wi-Fi signals to enable simultaneous context sensing and energy harvesting. We design and implement REHSense using a commercial-off-the-shelf (COTS) RF energy harvester. Extensive evaluation of three fine-grained wireless sensing tasks (i.e., respiration monitoring, human activity recognition, and hand gesture recognition) shows that REHSense can achieve comparable sensing accuracy with conventional Wi-Fi-based solutions while adapting to different sensing environments, reducing the power consumption of sensing by 98.7% and harvesting up to 4.5 mW of power from RF energy.

In this paper, we proposed F2Key, the first earable physical security system based on commercial off-the-shelf headphones. F2Key enables impactful applications, such as enhancing voiceprint-based authentication systems, reliable voice assistants, audio deepfake defense, and the legal validity of artifacts. The key idea of F2Key is to establish a stable acoustic sensing field across the user's face and embed the user's facial structures and articulatory habits into a user-specific generative model that serves as a private key. The private key can decrypt the Channel Impulse Response (CIR) profiles provided by the acoustic sensing field into an inferred spectrogram that can match the real one calculated from the corresponding speech, provided that the user's CIR-spectrogram mapping relationship is consistent with the one embedded in the generative model. Extensive experiments demonstrate that F2Key resists 99.9%, 96.4%, and 95.3% of speech replay attacks, mimicry attacks, and hybrid attacks, respectively. We discussed and evaluated F2Key from different perspectives, such as the health consideration and identical twins study, to show the practicality and reliability.

The widespread deployment of unattended LoRa networks poses a growing need to perform Firmware Updates Over-The-Air (FUOTA). However, the FUOTA specifications dedicated by LoRa Alliance fall short of several deficiencies with respect to energy efficiency, transmission reliability, multicast fairness, and security. This article proposes FLoRa+ , energy-efficient, reliable, beamforming-assisted, and secure FUOTA for LoRa networks, which is featured with several techniques, including delta scripting, channel coding, beamforming, and securing mechanisms. Specifically, we first propose a joint differencing and compression algorithm to generate the delta script for processing gain, which unlocks the potential of incremental FUOTA in LoRa networks. Then, we design a concatenated channel coding scheme with outer rateless code and inner error detection to enable reliable transmission for coding gain. Afterward, we develop a beamforming strategy to avoid biased multicast and compromised throughput for power gain. Finally, we present a securing mechanism incorporating progressive hash chain and packet arrival time pattern verification to countermeasure firmware integrity and availability attacks for security gain. Experimental results on a 20-node testbed demonstrate that FLoRa+ improves transmission reliability and energy efficiency by up to 1.51× and 2.65× compared with LoRaWAN. Additionally, FLoRa+ can defend against 100% and 85.4% of spoofing and Denial-of-Service (DoS) attacks.

The study of transmembrane proteins (TMPs) and their binding activities holds significant importance in the pharmaceutical industry. Due to their physicochemical properties, known binding information regarding TMPs remains comparatively scarce and prevented researchers to dig information from known samples. However, research into general binding structure basis can circumvent this barrier and provide more mechanism insights, which we previously demonstrated its existence and named it as TMPs binding Domain. In this study, we try to discover the TMPs binding domain more precisely. Through atomic-level heterogenous graph convolutions, we significantly improved the classification performance of binding domains. This lays the algorithmic groundwork for utilizing binding domains in the study of TMPs binding activities and further boost the drug target research or new drug development.

Physical-layer key generation is promising in establishing a pair of cryptographic keys for emerging LoRa networks. However, existing key generation systems may perform poorly since the channel reciprocity is critically impaired due to low data rate and long range in LoRa networks. To bridge this gap, this paper proposes a novel key generation system for LoRa networks, named ChirpKey. We reveal that the underlying limitations are coarse-grained channel measurement and inefficient quantization process. To enable fine-grained channel information, we propose a novel LoRa-specific channel measurement method that essentially analyzes the chirp-level changes in LoRa packets. Additionally, we propose a LoRa channel state estimation algorithm to eliminate the effect of asynchronous channel sampling. Instead of using quantization process, we propose a novel perturbed compressed sensing based key delivery method to achieve a high level of robustness and security. Evaluation in different real-world environments shows that ChirpKey improves the key matching rate by 11.03–26.58% and key generation rate by 27–49× compared with the state-of-the-arts. Security analysis demonstrates that ChirpKey is secure against several common attacks. Moreover, we implement a ChirpKey prototype and demonstrate that it can be executed in 0.2 s.

LoRa has emerged as one of the promising long-range and low-power wireless communication technologies for Internet of Things (IoT). With the massive deployment of LoRa networks, the ability to perform Firmware Update Over-The-Air (FUOTA) is becoming a necessity for unattended LoRa devices. LoRa Alliance has recently dedicated the specification for FUOTA, but the existing solution has several drawbacks, such as low energy efficiency, poor transmission reliability, and biased multicast grouping. In this paper, we propose a novel energy-efficient, reliable, and beamforming-assisted FUOTA system for LoRa networks named FLoRa, which is featured with several techniques, including delta scripting, channel coding, and beamforming. In particular, we first propose a novel joint differencing and compression algorithm to generate the delta script for processing gain, which unlocks the potential of incremental FUOTA in LoRa networks. Afterward, we design a concatenated channel coding scheme to enable reliable transmission against dynamic link quality. The proposed scheme uses a rateless code as outer code and an error detection code as inner code to achieve coding gain. Finally, we design a beamforming strategy to avoid biased multicast and compromised throughput for power gain. Experimental results on a 20-node testbed demonstrate that FLoRa improves network transmission reliability by up to 1.51 × and energy efficiency by up to 2.65 × compared with the existing solution in LoRaWAN.

LoRa has emerged as a novel Internet of Things (IoT) communication paradigm, featuring with long-range and low-power transmission capabilities. With the widespread deployment of LoRa networks, the demand to perform Firmware Update Over-The-Air (FUOTA) tasks has become increasingly critical for unattended LoRa devices. However, in practice, three fundamental problems that hinder the performance of FUOTA tasks are revealed, including low energy efficiency, poor transmission reliability, and biased multicast grouping. In this demo, we present a novel FUOTA system, the first work that offers an effective and sustainable solution to achieve energy-efficient and reliable over-the-air firmware updates in LoRa networks. In particular, this system incorporates threefold key modules: delta scripting, channel coding, and beamforming. The delta scripting algorithm unlocks the capability of incremental update, the channel coding scheme ensures the reliability and robustness of large-scale firmware image distribution, and the beamforming strategy as an optional module can further serve the unicast user. Thus, this demo presents a working example of functionality customization to show the efficacy and feasibility of our FUOTA system in LoRa networks.

Radio Frequency (RF)-based gait recognition has emerged as a promising technology to authenticate individuals in a pervasive and unobtrusive way. However, a fundamental challenge remains in collecting extensive data of the same user in the same environment. To address this challenge, this paper introduces XGait, a cross-modal gait recognition framework that does not require the prior deployment of RF devices or explicit data collection. The key idea is to leverage the signals of the Inertial Measurement Unit (IMU), which is widely available in modern mobile devices, to simulate the RF signals that would be generated if the same person walked near RF devices. Despite the straightforward idea, several technical challenges need to be addressed due to the diversity of RF devices, the intrinsic difference between IMU signals and RF signals, and the complexity of gait. First, we propose an RF spectrogram generation method to consistently extract essential RF gait data features across different RF signals. Secondly, we propose a generative network-enabled IMU-to-RF translation approach that accurately converts IMU data to RF data. Finally, we design an RF gait spectrogram-specific transformer model to further improve the recognition performance. We conduct a comprehensive evaluation of XGait, involving thirty subjects in three different environments, utilizing three RF devices and seven mobile devices. Experimental results show that XGait consistently achieves over 99% Top-3 accuracy in various scenarios.

Human Action Recognition (HAR) aims to understand human behavior and assign a label to each action. It has a wide range of applications, and therefore has been attracting increasing attention in the field of computer vision. Human actions can be represented using various data modalities, such as RGB, skeleton, depth, infrared, point cloud, event stream, audio, acceleration, radar, and WiFi signal, which encode different sources of useful yet distinct information and have various advantages depending on the application scenarios. Consequently, lots of existing works have attempted to investigate different types of approaches for HAR using various modalities. In this article, we present a comprehensive survey of recent progress in deep learning methods for HAR based on the type of input data modality. Specifically, we review the current mainstream deep learning methods for single data modalities and multiple data modalities, including the fusion-based and the co-learning-based frameworks. We also present comparative results on several benchmark datasets for HAR, together with insightful observations and inspiring future research directions.

The vast demand for diverse applications raises new networking challenges, which have encouraged the development of a new paradigm of Internet of Things (IoT), e.g., LoRa. LoRa is a proprietary spread spectrum modulation technique that provides a solution for long-range and ultra-low power-consumption transmission. Due to promising prospects of LoRa, significant effort has been made on this compelling technology since its emergence. In this article, we provide a comprehensive survey of LoRa from a systematic perspective: LoRa analysis, communication, security, and its enabled applications. First, we summarize works focusing on analyzing the performance of LoRa networks. Then, we review studies enhancing the performance of LoRa networks in communication. Afterward, we analyze the security vulnerabilities and countermeasures. Finally, we survey the various LoRa-enabled applications. We also present comparisons of existing methods, together with insightful observations and inspiring future research directions.