With the demand for economical and high-speed wireless services, Optical Wireless Communication (OWC) has attracted increasing attention in both research and the market. In the past decades, numerous optical-related techniques (e.g., LEDs, displays, cameras) and systems (e.g., VLC, LiFi, LiDAR) have been invented. OWC techniques, which are considered as a competitive mechanics in next-generation networks as an alternative to RF approaches, offer 10,000 times more bandwidth than conventional radio frequency (RF)-based wireless techniques (e.g., WiFi, LoRa, Bluetooth, LTE), as well as tremendous spatial reuse potential with even less interference. Because optical communications have a limited wavelength and travel in the line of sight (LoS) manner, the OWC is commonly thought as a secure wireless approach to confine light transmissions within physical bounds. However, in the real world, it is completely untrue. The privacy leakages and security risks broadly exist in the optical-related wireless applications including OWC networks. These threats and weaknesses have recently been the subject of several initial studies. However, they lack systematic analysis and are isolated. This survey first presents a general workflow of OWC systems, which consists of three stages: before signal emission (BSE), during signal propagation (DSP), and after signal receiving (ASV). For each stage, related risks are reviewed. Then, we summarize existing attacks in optical-related wireless applications and corresponding counter-attack solutions. Finally, we outline the future trends for improving OWC security.

Smart homes, medical devices, and education systems, among other emerging cyber-physical systems, hold immense promise for sensing-based user interfaces, especially for using fingers and hand gestures as system input. However, vision approaches compatible with time-consuming image processing adopt low 60 Hz location sampling rate (frame rate) for real-time hand gesture recognition. Furthermore, they are not suitable for low-light environment and long detection range. In this paper, we propose RoFin, which first exploits 6 temporal-spatial 2D rolling fingertips for real-time 3D reconstructing of 20-joint hand pose. RoFin designs active optical labeling for finger identification and enhances inside-frame 3D location tracking via high rolling shutter rate (5--8 KHz). These features enable great potentials for enhanced multi-user HCI, virtual writing for Parkinson suffers, etc. We implement RoFin prototypes with wearable gloves attached with low-power single-colored LED nodes and commercial cameras. The experiment results show that (1) In flexible sensing distances up to 2.5 m, RoFin achieves an average labeling parsing accuracy of 85%. (2) In comparison to vision-based techniques, RoFin improves the tracking grain with 4× more sampled points each frame. (3) RoFin reconstructs a hand pose in real time with 16 mm mean deviation error compared with Leap Motion under flexible distance.

Recent advancements in deep learning have shown that multimodal inference can be particularly useful in tasks like autonomous driving, human health, and production line monitoring. However, deploying state-of-the-art multimodal models in distributed IoT systems poses unique challenges since the sensor data from low-cost edge devices can get corrupted, lost, or delayed before reaching the cloud. These problems are magnified in the presence of asymmetric data generation rates from different sensor modalities, wireless network dynamics, or unpredictable sensor behavior, leading to either increased latency or degradation in inference accuracy, which could affect the normal operation of the system with severe consequences like human injury or car accident. In this paper, we propose PATCH, a framework of speculative inference to adapt to these complex scenarios. PATCH serves as a plug-in module in the existing multimodal models, and it enables speculative inference of these off-the-shelf deep learning models. PATCH consists of 1) a Masked-AutoEncoder-based cross-modality imputation module to impute missing data using partially-available sensor data, 2) a lightweight feature pair ranking module that effectively limits the searching space for the optimal imputation configuration with low computation overhead, and 3) a data alignment module that aligns multimodal heterogeneous data streams without using accurate timestamp or external synchronization mechanisms. We implement PATCH in nine popular multimodal models using five public datasets and one self-collected dataset. The experimental results show that PATCH achieves up to 13% mean accuracy improvement over the state-of-art method while only using 10% of training data and reducing the training overhead by 73% compared to the original cost of retraining the model.

Smart Agriculture has gained significant attention in recent years due to its benefits for both humans and the environment. However, the high costs associated with commercial devices have prevented some agricultural lands from reaping the advantages of technological advancements. Traditional methods, such as reflectance spectroscopy, offer reliable and repeatable solutions for soil property sensing, but the high costs and redundancy of preprocessing steps limit their on-site applications in real-world scenarios. Recently, RF-based soil sensing systems have opened a new dimension in soil property analysis using IoT-based systems. These systems are not only portable, but also significantly cheaper than traditional methods. In this paper, we carry out a comprehensive review of state-of-the-art soil property sensing, divided into four areas. First, we delve into the fundamental knowledge and studies of reflectance-spectroscopy-based soil sensing, also known as traditional methods. Secondly, we introduce some RF-based IoT soil sensing systems employing a variety of signal types. In the third segment, we introduce the details of sample pretreatment, inference methods, and evaluation metrics. Finally, after analyzing the strengths and weaknesses of the current work, we discuss potential future aspects of soil property sensing.

In recent years, there is a rapid increase in the number of Android based malware. To protect users from malware attacks, different malware detection methods are proposed. In this paper, a novel static method is proposed to detect malware. We use the static analysis technique to analyze the Android applications and obtain their static behaviors. Two kinds of behaviors are extracted to represent malware. One kind of behaviors is the function call graph and the other kind is opcode sequences. To automatically learn behavioral features, we convert the function call graphs and opcode sequences into two dimensional data, and use deep learning method to build malware classifier. To further improve the performance of the malware classifier, a deep feature fusion model is proposed, which can combine different behavioral features for malware classification. The experimental results show the deep learning method is effective to detect malware and the proposed fusion model outperforms the single behavioral model.

Smart agriculture requires efficient and scalable sensing of key soil parameters. While recent wireless sensing systems provide low-cost alternatives, active designs depend on powered underground components with potential instability and contamination risks, whereas passive battery-free approaches suffer from limited accuracy and are typically restricted to moisture detection. This work presents SoilNutri, a fully passive and battery-free metasurface capsule that enables simultaneous, high-precision monitoring of soil moisture and nitrogen concentration. The system employs a complementary split-ring (CSR) metasurface integrated within a sandwich-structured capsule that provides electromagnetic isolation and stable coupling with the surrounding soil. A lightweight two-stage soil-meta-VAE framework reconstructs soil-distorted reflection spectra and jointly predicts moisture and nitrogen from a shared latent representation. Extensive in-lab and field experiments demonstrate high-precision moisture estimation with 0.64% MAE and ppm-level nitrogen prediction with 5.14 ppm MAE across diverse soil types, depths, and seasonal conditions. With a per-unit cost below $1 and a reusable encapsulated design, SoilNutri offers a scalable, sustainable, and physically interpretable solution for multi-nutrient, in-situ soil monitoring for next-generation smart agriculture.