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Energy-Efficient-Enabled Edge-AI-IoT Integrated Traffic Incident Analysis and Avoidance of Secondary Incidents

IEEE Internet of Things Journal 2025年

作者： Chavhan, Suresh Deepika, Illa Sai Gupta, Deepak Rodrigues, Joel J P C Indian Institute of Science Education and Research Thiruvananthapuram School of Data Sciences India Indian Institute of Management Mumbai India Maharaja Agrasen Institute of Technology Dept. of Computer Science and Engineering Delhi India Chitkara University Chitkara University Institute of Engineering and Technology Punjab India Amazonas State University AM Manaus Brazil

Intelligent transportation systems (ITS) use information communication and technologies to provide road safety, traffic control, traffic congestion, accident avoidance, etc. Traffic accidents cause huge disruption of vehicle movements, road blockages, traffic jams, etc., known as secondary traffic incidents. These incidents in turn lead to huge CO2 emissions and fuel consumption, which directly impact the environment, reduce vehicle mileage, and unnecessary fuel waste. To reduce or avoid secondary traffic incidents, in this paper, we propose an Edge-AI-IoT integrated energy efficient system to detect, analyze, and predict the primary and secondary incidents. The proposed system uses the existing sensor technology like accelerometer, tilt, etc., to detect the accident and severity levels using Edge-AI-IoT, locally it analyzes and predicts the secondary incidents. The proposed system has been exhaustively simulated using real-time scenarios in OMENT++, Veins, and SUMO, and it was tested using the NVIDIA Jetson AGX Xavier edge device integrated with the ThingSpeak cloud platform. The proposed system is tested with performance parameters such as clearance time, accident detection, density of vehicles, speed of vehicles, CO2 emissions, and fuel consumption at different times of day. The simulation and real-time tested results show its real-time deployment. © 2025 IEEE.

关键词： Traffic congestion

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A channel-independent network based on wavelet enhancement for long-term time series forecasting

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engineering Applications of Artificial Intelligence 2025年 154卷

作者： Huang, Zhigen Zhang, Fan Liu, Yepeng School of Computer Science and Technology Shandong Technology and Business University Yantai264005 China Shandong Future Intelligent Financial Engineering Laboratory Yantai264005 China

Long-term time series forecasting is critical for applications like financial market analysis, energy consumption prediction, and industrial optimization. However, this task is challenging due to the long prediction horizon, high data volatility, and complex multi-variable dependencies. While Transformer-based models have shown promising performance by focusing on encoder–decoder structures and attention mechanisms, they primarily capture correlations within individual samples, neglecting inter-sample dependencies. Additionally, these models mainly rely on attention mechanisms and seldom explore alternative approaches. To address these limitations, we propose a novel single-encoder framework that adopts a channel-independent, patch-based processing mode. Specifically, we apply multi-level discrete Wavelet transforms to each channel's sequence and introduce a Wavelet external attention mechanism to capture the correlation of sequences between multiple samples in the Wavelet domain. Additionally, we incorporate patch graph convolution network and Mamba to model local spatial dependencies within patches and long-term dependencies between patches. Extensive experiments on multiple real-world datasets demonstrate that our proposed approach outperforms recent and popular approaches, including Transformer-based, convolutional neural networks-based, multilayer perceptron-based, and cross-domain learning models. Our approach achieves improvements of 5.7%, 16.8%, 17.1%, and 13.2%, respectively, demonstrating its effectiveness in improving forecasting accuracy and capturing broader temporal dependencies. This offers a robust and scalable solution for critical applications in finance, energy, healthcare, and industrial automation. © 2025 Elsevier Ltd

关键词： Convolutional neural networks

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Sea ice type identification method based on MEMS sensor network combined with PCA-BPMIV-GRU

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Yang, Le Quan, Xiyu Zhang, Jiankui Liu, Yunjing Zhao, Shiqi Fu, Changzeng Zhan, Zhikun Li, Lianliang Lv, Xiaoyong Zhao, Yuliang Northeastern University at Qinhuangdao Hebei Key Laboratory of MicroNano Precision Optical Sensing and Measurement Technology The School of Control Engineering Qinhuangdao066004 China Northeastern University at Qinhuangdao College of Control Science and Engineering Qinhuangdao066099 China Northeastern University at Qinhuangdao College of Computer Science and Engineering Qinhuangdao066004 China

Sea ice poses a significant hazard to vessels navigating in high-latitude maritime regions. To effectively prevent the collision between ships and large sea ice (such as icebergs) and other similar accidents, it is crucial to identify sea ice and its types in a timely and accurate manner. However, traditional sea ice identification techniques, such as optical and radar remote sensing, are often limited by visibility conditions, suffering from poor accuracy, high measurement costs, and low identification efficiency. This paper proposes a sea ice identification method based on a vibration sensor network, combined with the PCA-BPMIV-GRU strategy, which achieves low cost, high efficiency, and high accuracy in sea ice type detection. Firstly, a cost-effective MEMS vibration sensor network is deployed to capture vibration information of sea ice. The data collected from six sensors at different locations at the same time are treated as independent samples to construct a dataset, which contains time-frequency domain characteristic data of three sea-ice types: broken ice, flat ice, and open water. Secondly, the dimension reduction and feature optimization of vibration data were carried out by combining Principal Component Analysis (PCA) and the improved Mean Influence Value Optimization algorithm (BPMIV). This integration of unsupervised and supervised algorithms effectively eliminated unnecessary feature information, significantly improving model identification efficiency. Finally, seven commonly used classification algorithms are compared for the identification of three types of sea ice, evaluating their identification accuracy and efficiency under different dimensionality reduction methods. The results confirm that the Gated Recurrent Unit (GRU) model achieves the best comprehensive performance. Experimental data shows that the proposed method reduces the number of features to 9, resulting in a 24.2 % decrease in parameters compared to the GRU model alone, while achieving

关键词： Frequency domain analysis

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A Framework for Evaluating the Effectiveness of Explainability Methods in Deep Learning

A Framework for Evaluating the Effectiveness of Explainabili...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Bani Ahmad, Ahmad Y. A. Sarkar, Prithu Goswami, Brijesh Patil, Priyanka Rajesh Al-Said, Khaleel Al Said, Nidal Middle East University Faculty of Business Department of Accounting and Finance Science Amman11831 Jordan Amity University Amity School of Communication Kolkata India Gla University Institute of Business Management Mathura India Prof Ram Meghe College of Engineering & Management Department of Computer Science and Engineering Badnera India Middle East University Faculty of Arts and Educational Sciences Department of Educational Technology Amman Jordan Ajman University College of Mass Communication Ajman United Arab Emirates

ISBN: (纸本)9798331508685

The Internet of Things (IoT) is a new framework that is transforming societies across the globe into smart towns. Nevertheless, because of IoT systems' ongoing connection and data exchange, their rise has been followed by several cybersecurity problems. In response to this issue, several intrusion detection structures (IDSs) have been developed as safety precautions. These methods have shown effectiveness in thwarting several IoT-based assaults, particularly when deep learning (DL) techniques are employed. While there is a lot to discuss on the topic of eXplainable Artificial Intelligence (XAI) nowadays, more research is still required to completely comprehend how effective XAI is at identifying assault areas and routes when implemented in cyber safety uses. This research provides an innovative, explainable IDS for IoT systems. This study designed an IDS utilizing a Short-Term Long Memory (LSTM) framework to detect intrusions and analyze the system's choices. For training and assessing the LSTM algorithm, an innovative collection of source characteristics is gathered using a new SPIP (S- Shapley Addition ExPlanations, P- Permuting Characteristic Significance, I- Individual Conditioned Expectations, P- Partial Dependent Graph) architecture. When contrasted with existing competitor approaches, the SPIP architecture outperformed them in terms of identification accuracy, processing duration, and comprehension of data characteristics and models. The suggested method could help managers and decision-makers comprehend complicated assault activity. © 2025 IEEE.

关键词： Cybersecurity

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A hybrid flow shop scheduling optimization using puma optimizer: Addressing energy efficiency and workforce restrictions

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Alexandria engineering Journal 2025年 128卷 340-365页

作者： Sang-Woong Lee Jawad Tanveer Amir Masoud Rahmani Farhad Soleimanian Gharehchopogh Benyamin Abdollahzadeh Hamid Rokny Thantrira Porntaveetus Jan Lansky Mehdi Hosseinzadeh Pattern Recognition and Machine Learning Lab School of Computing Gachon University Seongnam 13120 Republic of Korea Department of Computer Science and Engineering Sejong University Seoul 05006 Republic of Korea Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Engineering Ur. C. Islamic Azad University Urmia Iran Center of Excellence in Precision Medicine and Digital Health Department of Physiology Faculty of Dentistry Chulalongkorn University Bangkok 10330 Thailand Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic School of Computer Science Duy Tan University Da Nang Vietnam Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India

The Hybrid Flow Shop Scheduling Problem (HFSP) is a complex challenge in production and manufacturing, requiring efficient scheduling solutions that optimize productivity while considering real-world constraints. This study proposes an advanced optimization framework utilizing the Puma Optimizer, a metaheuristic algorithm enhanced with an adaptive control mechanism for intelligently selecting phases and operators. This feature makes it well-suited for discrete optimization problems such as HFSP. Our approach integrates heuristic decoding and adaptive local search, balancing exploration and exploitation to generate high-quality schedules. The proposed mathematical model optimizes makespan and total tardiness while energy consumption is incorporated as a constraint to enhance sustainability. Extensive computational experiments demonstrate the superior performance of the proposed algorithm compared to traditional methods, achieving shorter makespan and lower tardiness while ensuring workforce feasibility. These results highlight the Puma Optimizer’s adaptability and potential to set new benchmarks for HFSP, offering a scalable and effective solution for modern production systems.

关键词： Hybrid Flow Shop Scheduling Problem (HFSP) Puma optimizer Energy efficiency Worker constraints Metaheuristic optimization

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Research on the Online Update Method for Retrieval-Augmented Generation (RAG) Model with Incremental Learning

arXiv

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arXiv 2025年

作者： Fan, Yuxin Wang, Yuxiang Liu, Lipeng Tang, Xirui Sun, Na Yu, Zidong School of Engineering and Applied Science University of Pennsylvania Toronto Canada School of Business Stevens Institute of Technology New York United States College of Engineering Peking University Beijing China College of Computer Sciences Northeastern University Boston United States Newark United States Syracuse University College of Engineering and Computer Science San Jose United States

In the contemporary context of rapid advancements in information technology and the exponential growth of data volume, language models are confronted with significant challenges in effectively navigating the dynamic and ever-evolving information landscape to update and adapt to novel knowledge in real time. In this work, an online update method is proposed, which is based on the existing Retrieval Enhanced Generation (RAG) model with multiple innovation mechanisms. Firstly, the dynamic memory is used to capture the emerging data samples, and then gradually integrate them into the core model through a tunable knowledge distillation strategy. At the same time, hierarchical indexing and multi-layer gating mechanism are introduced into the retrieval module to ensure that the retrieved content is more targeted and accurate. Finally, a multi-stage network structure is established for different types of inputs in the generation stage, and cross-attention matching and screening are carried out on the intermediate representations of each stage to ensure the effective integration and iterative update of new and old knowledge. Experimental results show that the proposed method is better than the existing mainstream comparison models in terms of knowledge retention and inference accuracy. © 2025, CC BY.

关键词： Contrastive Learning

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Advanced Maximal Biclique Enumeration on GPUs Using Bitmaps

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IEEE Transactions on computers 2025年

作者： Pan, Zhe He, Shuibing Li, Xu Zhang, Xuechen Wang, Rui Yin, Yanlong Chen, Gang Zhejiang University State Key Laboratory of Blockchain and Data Security Hangzhou310027 China Zhejiang University College of Computer Science and Technology Hangzhou310027 China Hangzhou310051 China Washington State University Vancouver School of Engineering and Computer Science VancouverWA98686 United States

Maximal biclique enumeration (MBE) in bipartite graphs is an important problem in data mining with many real-world applications. Parallel MBE algorithms for GPUs are needed for MBE acceleration leveraging its many computing cores. However, enumerating maximal bicliques using GPUs has three main challenges including large memory requirement, thread divergence, and load imbalance. In this paper, we propose GMBE+, an advanced GPU solution for the MBE problem. To overcome the challenges, we design (1) a node-reuse approach to reduce GPU memory usage with advanced node pruning, (2) a bitmap-based set intersection approach to minimize thread divergence, and (3) a load-aware task scheduling framework to achieve load balance among threads within GPU warps, facilitated by a novel set union approach. Our experiments reveal that GMBE+ is 1.2× faster than the latest GPU-based MBE algorithm GMBE on average when running on the same NVIDIA A100 GPU. © 1968-2012 IEEE.

关键词： Memory architecture

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Multi-agent Reinforcement Learning Based Delay and Power Optimization for UAV-WMN Substation Inspection

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IEEE Transactions on Network and Service Management 2025年

作者： Tang, Qingwei Sun, Wei Liu, Zhi Xiao, Yang Li, Qiyue Yuan, Xiaohui Zhang, Qian Hefei University of Technology School of Electrical and Automation Engineering Anhui Hefei230009 China Anhui Engineering Technology Research Center of Industrial Automation 193 Tunxi Rd Hefei230009 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo182-8585 Japan The University of Alabama Department of Computer Science TuscaloosaAL35487 United States University of North Texas Department of Science and Engineering DentonTX76207 United States

Unmanned aerial vehicles (UAV), due to their flexibility and extensive coverage, have gradually become essential for substation inspections. Wireless mesh networks (WMN) provide a scalable and resilient network environment for UAVs, where each node can serve as either an access point or a relay point, thereby enhancing the network’s fault tolerance and overall resilience. However, the UAV-WMN combined system is complex and dynamic, facing the challenge of dynamically adjusting node transmission power to minimize end-to-end (E2E) delay while ensuring channel utilization efficiency. Real-time topology changes, high-dimensional state spaces, and large solution spaces make it difficult for traditional algorithms to guarantee convergence and stability. Generic reinforcement learning (RL) methods also struggle with stable convergence. This paper introduces a new Lyapunov function-based proof to address these issues and provide a stable condition for dynamic control strategies. Then, we developed a specialized neural network power controller and combined it with the MATD3 algorithm, effectively enhancing the system’s convergence and E2E performance. Simulation experiments validate the effectiveness of this method and demonstrate its superior performance in complex scenarios compared to other algorithms. © 2004-2012 IEEE.

关键词： Lyapunov functions

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Progressive Human Motion Generation Based on Text and Few Motion Frames

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Zeng, Ling-An Wu, Gaojie Wu, Ancong Hu, Jian-Fang Zheng, Wei-Shi Sun Yat-sen University School of Artificial Intelligence Guangdong Zhuhai519082 China Sun Yat-sen University School of Computer Science and Engineering Guangdong Guangzhou510275 China Sun Yat-sen University School of Computer Science and Engineering China Guangdong Key Laboratory of Information Security Technology China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing China Peng Cheng Laboratory China

Although existing text-to-motion (T2M) methods can produce realistic human motion from text description, it is still difficult to align the generated motion with the desired postures since using text alone is insufficient for precisely describing diverse postures. To achieve more controllable generation, an intuitive way is to allow the user to input a few motion frames describing precise desired postures. Thus, we explore a new Text-Frame-to-Motion (TF2M) generation task that aims to generate motions from text and very few given frames. Intuitively, the closer a frame is to a given frame, the lower the uncertainty of this frame is when conditioned on this given frame. Hence, we propose a novel Progressive Motion Generation (PMG) method to progressively generate a motion from the frames with low uncertainty to those with high uncertainty in multiple stages. During each stage, new frames are generated by a Text-Frame Guided Generator conditioned on frame-aware semantics of the text, given frames, and frames generated in previous stages. Additionally, to alleviate the train-test gap caused by multi-stage accumulation of incorrectly generated frames during testing, we propose a Pseudo-frame Replacement Strategy for training. Experimental results show that our PMG outperforms existing T2M generation methods by a large margin with even one given frame, validating the effectiveness of our PMG. Code is available here. © 1991-2012 IEEE.

关键词： Controllable Motion Generation Human Motion Text2Motion Generation

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A Learning-Based Stochastic Game for Energy Efficient Optimization of UAV Trajectory and Task Offloading in Space/Aerial Edge Computing

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IEEE Transactions on Vehicular technology 2025年

作者： Li, Jialiuyuan Shi, You Dai, Chen Yi, Changyan Yang, Yuxiao Zhai, Xiangping Zhu, Kun Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China Nanjing Tech University College of Electrical Engineering and Control Science Nanjing211816 China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Nanjing University of Aeronautics and Astronautics College of Astronautics Nanjing211106 China

In this paper, we study the energy-efficient unmanned aerial vehicle (UAV) and low earth orbital (LEO) satellite assisted mobile edge computing (MEC) in space-air-ground integrated networks (SAGINs). The key challenge involves how to efficiently execute tasks offloaded from IoT devices, given limited on-board computation capabilities of UAVs and delay constraints of tasks. To address it, UAVs are first allowed to adjust their trajectories to accommodate as many tasks as possible from IoT devices. Subsequently, UAVs compute a portion of these tasks while delegating the rest to LEO satellites for further execution. Accordingly, we formulate a joint optimization problem including UAV trajectory planning, task offloading, and bandwidth allocations, aiming to maximize the long-term energy efficiency of UAVs and LEO satellites (i.e., the total data size in computation offloading divided by energy consumption of UAVs and LEO satellites). Considering the interactions among intelligent UAVs, this problem is reformulated as a set of interconnected multi-agent stochastic games, and the existence of corresponding Nash Equilibrium (NE) is theoretically proved. To obtain the NE, a multi-agent reinforcement learning-based algorithm, namely UTPTR, is developed concerning system dynamics and uncertainties. Simulations evaluate the performance of the UTPTR and demonstrate its superiority compared to existing approaches. © 1967-2012 IEEE.

关键词： Stochastic systems

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