Efficient and lightweight intrusion detection models are critical for real-time deployment on resource-constrained edge Internet of Things devices. This paper presents NINMix-KD, a lightweight knowledge distillation framework for tokenized network traffic classification. The framework is built upon a compact sequence encoder that replaces self-attention with efficient token mixing and channel mixing operations, enabling linear-time processing of variable-length traffic sequences. On this basis, a moment matching knowledge distillation strategy is introduced to transfer latent representations from the teacher to a lightweight student model by aligning first-order and second-order statistics of their embeddings. This design avoids reliance on output distribution alignment and eliminates the need for computationally expensive intermediate-layer distillation. Extensive experiments are conducted on four public benchmark edge network datasets ToNIoT, CICIoV2024, Edge-IIotset and X-IIoTID, covering IoT, IoV and IIoT scenarios. The results demonstrate that the proposed approach achieves strong performance across multiple evaluation metrics. In particular, the distilled student model attains performance comparable to the teacher while reducing model size by over 90%, highlighting the effectiveness of moment-based distillation for efficient intrusion detection. These results indicate that NINMix-KD provides a practical and scalable solution for intrusion detection in resource-constrained network environments.

In the current study, a novel network model is proposed for text classification based on Graph Attention Networks (GATs) and sentence-transformer embeddings. Most existing methods with a pretraining model as an input layer still treat words as the minimum processing unit. However, word embedding is not an efficient and appropriate solution when dealing with long texts containing many professional words. This study aims to design a model capable of handling text classification tasks at multilevel semantic segmentation. The main contribution of this study is that a novel GAT variant is designed using global nodes and Squeeze-and-Excitation Networks (SENet) to capture semantic information. Moreover, a novel unidirectional attention mechanism is introduced for our model to avoid the message passing of irrelevant noisy information within global nodes. The numerical results show that according to the characteristics of datasets, specific semantic information combinations can effectively improve the accuracy and performance of text classification. Without fine-tuning of the pretrained encoder, the new state-of-the-art performance is achieved on three benchmark datasets. In addition, a comprehensive analysis of the graph attention mechanism in the model for specific cases suggests that the unidirectional attention mechanism and the use of global nodes are key contributing factors to multilevel semantic fusion.

As for the information theory of Energy Internet, image signal processing and deep learning theory are important theories to support the information communication system of Energy Internet. The application of advanced remote sensing technology, such as radar, makes the theoretical system of Energy Internet improved. Power line safety is an important factor to be considered in the power transmission corridor, and radar technology can be used to effectively monitor it. This study proposes an interferometric inversion method of wide-area vegetation elevation range information, using radar satellite polarization data, by the simulation data of polarization interferometric Synthetic Aperture Radar (PolInSAR), firstly. There are three kinds of algorithms compared and analyzed, that is, Digital Elevation Model (DEM) difference algorithms, coherent amplitude inversion algorithm, and coherent amplitude and phase joint inversion algorithm. The actual ALOS 1-PALSAR telemetry data in Hechi has inverted the height of vegetation with coherent amplitude and phase joint inversion algorithm. The result is 16m of the average vegetation height, which is in line with the height grade of local pines and firs species. Then, the distance of ground object points and power line points range between poles and towers is measured by Light Detection and Ranging (Lidar) technology, to determine the distance between components in the power transmission corridor and external objects. This can identify the vegetation invasion. In conclusion, this study has guiding significance for the threat of vegetation in distant and close range. The methods can be used as an effective means to monitor the safety and stability of power lines. It can provide the basis for the operation optimization strategy of power transmission corridors.

In order to prolong the lifetime of wireless sensor networks (WSNs), a low-duty-cycle mode is widely used to save the energy for sensor nodes. Under this mode, sensor nodes switch between active and dormant states, which incurs a high latency for traditional routing algorithms. To mitigate this, in this paper, the data collection problem in low-duty-cycle WSNs is formulated as a delay optimization problem of traffic flow with consideration of both congestion and collision, which is solved by a distributed algorithm based on network utility maximization. Our proposed distributed routing algorithm achieves a better tradeoff between latency and energy conservation than existing schemes, and our schemes can find a nearly global-optimal-path to achieve almost minimum average end-to-end (E2E) delay with less energy consumption. The computation complexity and energy consumption of the distributed algorithm are analyzed and evaluated in detail. The simulation results show that the proposed algorithm can achieve almost the same average E2E delay performance as the global optimal algorithm with less energy, and reduce the average E2E delay by about 30% than the shortest path algorithm when the data generation rate is high.

Trimble R7 and Topcon NET-G3A are two common GPS receivers and both deployed with choke antenna. But two matching antennas are different capacity to suppress multipath effect. The research results that for two types of receivers, the ability of mitigate multipath effect based on P code with low satellite elevation cutoff angle is more effective than C/A code. Comparing with Topcon NET-G3A receiver, the multipath mitigation ability of Trimble receivers is better with low satellite elevation cutoff angle.

This paper addresses the data persistence problem in wireless sensor networks (WSNs) where static sinks are not present and the sensed data have to be temporarily but resiliently stored in the network. Based on the observation that sensor readings are correlated, we propose compressive data persistence (CDP) scheme that makes use of the compressive sensing (CS) theory. Each sensor node independently computes and stores a random projection of the sensed data, such that a mobile sink can recover the data with high probability after visiting a small and random portion of the network. As a prerequisite of distributed CS encoding, sensor readings from all nodes are disseminated within the network through random walk. Therefore, the CS measurement matrix depends heavily on how the random walk is performed. In this paper, we present an in-depth analysis on the interplay between random walk parameters and sensing data characteristics, and derive the conditions in successful CS data recovery. In addition, we discover that there is a trade-off between the number of random walk instances and steps in order to achieve the required data persistence performance. Experiments using real sensor data verify that the proposed CDP scheme achieves much lower decoding ratio than the state-of-the-art Fountain code based schemes or the decentralized erasure codes based schemes, and demonstrate that there exist energy-optimized random walk parameters for CDP.

Stream flow series analysis is of great importance for watershed management such as water conservation, water quality control etc. How to describe the characteristic of stream flow, particularly quantify stream flow variability, has been the subject of numerous studies. Due to the fact that stream flow process usually suffers from strong natural and anthropogenic disturbances, it is hardly to measure the chaotic characteristic by pure statistic method. This paper applied fractal concept for steam flow series analysis. The fractal dimension of the daily stream flow of Malian Basin was estimated using box-counting method. The result showed that the fractal dimension of the main stream and downstream gauging stations is higher than that of the tributary, headwater gauging stations. From the correlative and regress analyze of stream flow fractal dimension and soil erosion we found that the two has distinct relativity, which can bring forward a new method for the study of watershed management and so on. This could come to a conclusion stream flow series fractal dimension analysis could be a potential approach for watershed management.

We present CarSafe, a new driver safety app for Android phones that detects and alerts drivers to dangerous driving conditions and behavior. It uses computer vision and machine learning algorithms on the phone to monitor and detect whether the driver is tired or distracted using the front-facing camera while at the same time tracking road conditions using the rear-facing camera. Today's smartphones do not, however, have the capability to process video streams from both the front and rear cameras simultaneously. In response, CarSafe uses acontext-aware algorithm that switches between the two cameras while processing the data in real-time with the goal of minimizing missed events inside (e.g., drowsy driving) and outside of the car (e.g., tailgating). Camera switching means that CarSafe technically has a "blind spot" in the front or rear at any given time. To address this, CarSafe uses other embedded sensors on the phone (i.e., inertial sensors) to generate soft hints regarding potential blind spot dangers. We present the design and implementation of CarSafe and discuss its evaluation using results from a 12-driver field trial. Results from the CarSafe deployment are promising -- CarSafe can infer a common set of dangerous driving behaviors and road conditions with an overall precision and recall of 83% and 75%, respectively. CarSafe is the first dual-camera sensing app for smartphones and represents a new disruptive technology because it provides similar advanced safety features otherwise only found in expensive top-end cars.

We present CarSafe, the first driver safety application that uses dual cameras on smartphones to detect and alert drivers to dangerous driving conditions. CarSafe fuses events detected from cameras and readings from embedded sensors on the phone -- such as the GPS, accelerometer and gyroscope -- to detect and alert the driver of dangerous driving behavior in and outside of the car. Results from a 12-driver field trial show CarSafe can infer five of the most commonly occurring dangerous driving conditions with an overall precision and recall of 83% and 75%, respectively.

This paper considers a large-scale wireless sensor network where sensor readings are occasionally collected by a mobile sink, and sensor nodes are responsible for temporarily storing their own readings in an energy-efficient and storage-efficient way. Existing data persistence schemes based on erasure codes do not utilize the correlation between sensor data, and their decoding ratio is always larger than one. Motivated by the emerging compressive sensing theory, we propose compressive data persistence which simultaneously achieves data compression and data persistence. In the development of compressive data persistence scheme, we design a distributed compressive sensing encoding approach based on Metropolis-Hastings random walk. When the maximum step of random walk is 400, our proposed scheme can achieve a decoding ratio of 0.36 for 10%-sparse data. We also compare our scheme with a state-of-the-art Fountain code based scheme. Simulation shows that our scheme can significantly reduce the decoding ratio by up to 63%.