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Blockchain-Empowered Asynchronous Federated Reinforcement Learning for IoT-Based Traffic Trajectory Prediction

IEEE Internet of Things Journal 2025年第11期12卷 17095-17109页

作者： Wang, Bin Tian, Zhao Tang, Fengxiao Pan, Heng She, Wei Liu, Wei Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450002 China Zhengzhou University School of Cyber Science and Engineering Zhengzhou450002 China Central South University School of Computer Science and Engineering Changsha410083 China Zhongyuan University of Technology School of Cyber Space Security Zhengzhou450007 China Zhengzhou University School of Cyber Science and Engineering Zhengzhou University The Zhengzhou Key Laboratory of Blockchain and Data Intelligence Zhengzhou450002 China

Vehicle trajectory prediction plays a crucial role in IoT-based intelligent transportation systems, which can effectively address key issues such as driving safety and multi-vehicle collaboration. However, the sensitivity of trajectory data and the reluctance of data holders to share it constrain the prediction model's ability to capture vehicle behavior patterns in different scenarios. To address the above problems, we propose a blockchain-enabled asynchronous federated proximal policy optimization framework (BE-AFPPO) for the trajectory prediction of self-driving vehicles. Firstly, we propose a Curiosity Proximal Policy Optimisation (C-PPO) algorithm. The method utilizes a driven exploration strategy to actively motivate the intelligent agent to explore the unknown state space. The avoidance policy model reaches a local optimum when processing trajectory data. In addition, we design historical GRU and future GRU as input layers. The target's historical motion features and future trajectory features are extracted, respectively. Then, various data is received through asynchronous federated learning. This model can fully learn the vehicle's behavior patterns in different scenarios, which improves prediction accuracy. Based on this, we develop a blockchain-based Dynamic Group Practical Byzantine Fault Tolerance (DG-PBFT) consensus algorithm. This enhances the credibility and integrity of the data while enriching the sources of trajectory data. Finally, we perform the experiments on the publicly available dataset nuScenes. The results demonstrate that the proposed method improves the robustness and accuracy of trajectory prediction. © 2014 IEEE.

关键词： Federated learning

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HASLB: Huge Page Allocation Strategy Optimized for Load-Balance in Parallel Computing Programs 20th

HASLB: Huge Page Allocation Strategy Optimized for Load-Bal...

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20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： Yue, Lei Wu, Tao Shen, Yang Zhang, Jin Zhang, Wenzhe School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China College of Computer Science and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819628292

Equitable resource allocation is currently considered as a key enabler for achieving complete parallelism in parallel computing. Optimized memory management mechanisms are crucial for load balance in parallel tasks. In some cases, the performance of Linux’s huge page management mechanism deteriorates when handling parallel tasks due to the nonhomogeneous allocation of memory resources among parallel processes. This research introduces HASLB, a huge page allocation strategy optimized for parallel programs. HASLB evaluates the status of parallel processes based on the count of huge pages acquired by each process and the number of page faults it encountered. Following the results of the analysis, huge pages are allocated using a method conducive to load balance. The problems in the Linux transparent huge page mechanism are optimized through this strategy, which limits the greedy allocation mechanism in some situations. HASLB achieves a reasonable allocation of memory resources among parallel processes with low overhead, benefiting the load balance of parallel computing programs. In addition, contiguous memory would be protected because of limiting the greedy allocation of huge pages. The parallel benchmark experiments show a maximum performance improvement of 6.4% compared to Linux in the BFS benchmark. In the IS benchmark program, huge page consumption exhibits a 9% reduction. © IFIP International Federation for Information Processing 2025.

关键词： Memory management

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Efficient stochastic human motion prediction via consistency model 4

Efficient stochastic human motion prediction via consistency...

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4th International Conference on computer Vision, Application, and Algorithm, CVAA 2024

作者： Pan, Renhao School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing210094 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Diffusion models have recently proven successful in stochastic human motion prediction. Despite their excellent generative performance, they are difficult to predict in real-time because the multi-step sampling mechanism involves tens or hundreds of iterations of repetitive function evaluation. To adress this issue, we introduce the Motion Consistency Model (MotionCM) to alleviate the computational and time consumption of the iterative inference process. It applies a diffusion pipelline to the low-frequency domain processed by discrete cosine transformation(DCT), alleviating the computational burden for each function evaluation. The predictive efficiency of the motion diffusion model in stochastic human motion is further improved by employing a one-step (or several-step) inference via maitaining consistency of the outputs over the trajectory of PF-ODE. Extensive experiments on two benchmark datasets show that the proposed model achieves state-of-the-art performance at less than 10% of the time cost. © 2025 SPIE.

关键词： Cosine transforms

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FPMT: Enhanced Semi-supervised Model for Traffic Incident Detection 27th

FPMT: Enhanced Semi-supervised Model for Traffic Incident De...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Lu, Xinying Xiao, Jianli School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9783031781063

For traffic incident detection, the acquisition of data and labels is notably resource-intensive, rendering semi-supervised traffic incident detection both a formidable and consequential challenge. Thus, this paper focuses on traffic incident detection with a semi-supervised learning way. It proposes a semi-supervised learning model named FPMT within the framework of MixText. The data augmentation module introduces Generative Adversarial Networks to balance and expand the dataset. During the mix-up process in the hidden space, it employs a probabilistic pseudo-mixing mechanism to enhance regularization and elevate model precision. In terms of training strategy, it initiates with unsupervised training on all data, followed by supervised fine-tuning on a subset of labeled data, and ultimately completing the goal of semi-supervised training. Through empirical validation on four authentic datasets, our FPMT model exhibits outstanding performance across various metrics. Particularly noteworthy is its robust performance even in scenarios with low label rates. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Enhanced Ransomware Detection using a Hybrid Quantum-Classical Convolutional Neural Network with Boosted Sooty Tern Optimization 4

Enhanced Ransomware Detection using a Hybrid Quantum-Classic...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Chaithanya, B.N. Patil, Renuka R. Gitam School of Technology Gitam University Department of Computer Science and Engineering Nagadenahalli Bengaluru India

ISBN: (纸本)9798331523923

Ransomware attacks threaten organizations by encrypting files or locking systems and keeping them inaccessible unless a ransom is paid. Early detection of ransomware attacks helps organizations avoid financial losses, theft of data, and harm to their reputations. This early detection will also reduce the risks related to malicious activities. Detection by signature-based or heuristic approaches tends to be traditional and fails when evolvement in ransomware attacks is considered. Thus, a need for innovative techniques of deep learning and optimization algorithms can be observed. The method proposed in this study focuses on developing a novel multi-stage framework for detecting ransomware attacks. Firstly, correlation coefficient-based min-max normalization (CCMMN) for data preprocessing is used since it makes the data elements more uniform and makes the detection system more efficient. Feature selection is then carried out and optimized using the hybrid Osprey optimization algorithm with an enhancing sparrow search algorithm (OOA-ESSA) to arrive at the most relevant feature for classification. Last, this paper applies a hybrid quantum-classical convolutional neural network with boosted sooty tern optimization (HQCCNN-BSTO) in ransomware detection. The classic model is replaced by a quantum and hybrid classical neural network that may be complemented by an optimization algorithm to increase the speed and efficiency of the detection. The method that has been proposed here offers a flexible and efficient approach to the problems associated with the detection of ransomware threats in the practical environment. Thus, high effectiveness is proved by the 99% accuracy rate of the suggested method. This exceptional outcome proves the robustness of the approach and its ability to adapt to various patterns of dependence and data changes. © 2025 IEEE.

关键词： Convolutional neural networks

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Smart Agricultural Greenhouse System: A Context-Aware Application

Smart Agricultural Greenhouse System: A Context-Aware Applic...

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International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2023

作者： Swain, Sujata Niyogi, Rajdeep School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Department of Computer Science IIT Roorkee Roorkee India

ISBN: (纸本)9783031513374

Smart applications are getting more powerful and cheaper cost due to the advancement in sensor technology. In this chapter, we have considered a smart greenhouse application. The important parameters of the smart greenhouse are to control light, temperature, humidity, and water. Greenhouse can provide a favourable growth environment for plants. This smart greenhouse system is a context-aware application. When the context changes in the environment, the application automatically adapts it and does the changes seamlessly without user intervention. In this chapter, we propose a model for the smart greenhouse system. The proposed model can adapt to context changes such as temperature rises, and it automatically turns on the AC seamlessly. The proposed model is based on the machine learning approach. The model maintains the greenhouse temperature, humidity, and sunlight duration in the suitable ranges of the plant. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Context Greenhouse Machine learning NLP Sensors Web service

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A Robust Protocol Design to Perform Data Communication over Wireless Sensor Networks with Enhanced Privacy Preserving Principles 4

A Robust Protocol Design to Perform Data Communication over ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Sathyavani, Bandela Kumar Yadav, Rakesh John Shiny, J. Revathi, R. Bulakh, Ashish Kumar Jain, Shilpa School of Engineering Sr University Department of Eee Telangana Warangal India Amity School of Engineering and Technology Amity University Bihar Patna India Velammal College of Engineering and Technology Madurai India Psgr Krishnammal College for Women Department of Computer Science-Information Technology Coimbatore India Cdgi Department of Computer Science and Engineering Indore India Ies College of Technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798331523923

the growth of wireless sensor networks has transformed all automation and industrial systems. Nonetheless, guaranteeing safe and privacy-preserving connectivity in sensor networks continues to be a difficult task. Current authentication and routing techniques frequently jeopardize privacy, entail significant design intricacy, also vulnerable to malware assaults. To mitigate these restrictions, we present an innovative secure protocol, the Asymmetric Cryptography-based Modified Cuckoo Search Optimization Protocol (MCuSO) for wireless sensor networks. Our proposed technique impacts asymmetric credentials with belief supervision for the privacy preservation of WSN data. This method attains an equilibrium in the middle of security and efficiency. Through comprehensive simulations utilizing the MATLAB platform, we illustrate that MCuSO attains an average network throughput of 94% and a network residual energy of 89%, in comparison to leading other algorithms. Moreover, the protocol efficiently reduces malware attacks. These findings underscore MCuSO as a viable outcome of resource limited WSN devices, providing an advantageous equilibrium of information security, data integrity and data efficacy. © 2025 IEEE.

关键词： Authentication Protocol

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AN 8-WAY RF FEED NETWORK FOR 5.3 GHZ C-BAND SAR: DESIGN, SIMULATION, AND ANALYSIS USING STANDARD METHODS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 21-43页

作者： Kothapudi, Venkata Kishore Thokala, Niharika Vallabhaneni, Ramyasri Sanaka, Poojitha Kumar, Vijay Pappula, Lakshman Gottapu, Srinivasa Kiran Bhattacharyya, Arka Guntur District Andhra Pradesh Vadlamudi522213 India Department of Communication Engineering School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Electrical Electronics and Communication Engineering GITAM School of Technology GITAM University Andhra Pradesh Visakhapatnam530045 India School of Science and Engineering Engineering and Computer Science Department Benedict College 1600 Harden St ColumbiaSC29204 United States

This study proposes the design and analysis of an eight-way power divider for unequal division at 5.3 GHz for C-band frequency. Many transmission line pieces make up the current power divider. These transmission lines were printed on a substrate material (RT/duroid 5880, 1.6 mm thick, permittivity 2.2, and loss tangent 0.0002). The eight-way power divider’s unequal amplitudes are constructed using Taylor’s 1-parameter method to achieve the side projection levels –17 dB down as from the main beam. The proposed power divider consists of using two transmission lines with a 3/4 frequency connected to branched microstriplines, providing excellent isolation and matching between the power divider’s two output ports. This power divider is composed of three stages. A 1:2 power divider is used in the first stage, a 1:4 power divider is used in the second stage, and a 1:8 power divider is used in the third stage. According to the simulation results, 1:2 delivers half of the input power, 1:4 produces one-fourth of the power, and 1:8 delivers one-eighth of the input power. All of these assessments were done with the advanced design system (ADS) simulation program. According to both simulated and measured data, the return loss exceeds –35 dB, and the isolation across the output terminals exceeds –40 dB. The proposed circuit operates effectively at C-band frequencies in terms of return loss and isolation (4 GHz and 8 GHz). © 2025 by Begell House, Inc.

关键词： Integrated circuit design

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A comparative review on machine learning based medical diagnostic systems for Chikungunya and Zika virus 4th

A comparative review on machine learning based medical diagn...

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4th International Conference on Computational Methods in Science and technology, ICCMST 2024

作者： Gupta, Nisha Singh, Harjeet Singla, Jimmy Chitkara Institute of Engineering and Technology Chitkara University India School of Computer Science and Engineering Lovely Professional University India

ISBN: (纸本)9781032911571

The Chikungunya arbovirus and zika virus have recently experienced widespread outbreaks, which have allowed the virus to spread rapidly worldwide. These illnesses initially manifest as an acute stage of fever and severe joint inflammation. However, this stage gradually turns into a chronic stage, and during this time, a person experiences debilitating articular pain that is persistent and long-lasting. Additionally, the traditional methodologies used to identify it are not very effective and time-consuming. However, in the current years, the advancement in technology assists in monitoring, diagnosing, and treating individuals suffering from this virus remotely. For healthcare systems, Machine Learning (ML) is a godsend since it helps experts identify, diagnose, treat, and control dangerous diseases. In order to detect Zika and Chikungunya virus, this review study evaluates and compares the performance parameters of various ML approaches utilized in existing studies. This paper discusses the top techniques for diagnosing the selected viruses and offers a thorough evaluation of medical expert systems. © 2025 the Author(s).

关键词： Expert systems

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CO-LEACH: Cooperative Data Collection Protocol for Data-Heterogeneous WSNs 18th

CO-LEACH: Cooperative Data Collection Protocol for Data-Het...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zhang, Xinyu Wu, Gaojie Fu, Luwei Mao, Wenliang Zhao, Zhiwei School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9783031714696

Wireless Sensor Networks (WSNs) are indispensable for providing crucial sensing and data collection services to IoT systems. The diversity of IoT applications leads to data heterogeneity, which has become a prevalent characteristic of WSNs. Most existing work can only cope with data-homogeneous WSNs, where the same policy for clustering and data processing is applied to all network nodes. However, in practical IoT systems, different policies are desired to support the diverse types of IoT data. The neglect of data heterogeneity may result in poor data collection performance or even non-functionality. In this paper, we propose a cooperative data collection protocol (CO-LEACH) for data-heterogeneous WSNs. Specifically, 1) To mitigate the impact of data heterogeneity, CO-LEACH introduces an efficient cooperative clustering algorithm, in which heterogeneous nodes jointly form clusters, nodes with a higher proportion of intra-cluster type are more likely to be elected as cluster heads, thereby improving data collection efficiency. 2) To cope with the heterogeneous data, CO-LEACH presents an adaptive reprogramming strategy, making cluster heads capable of processing heterogeneous data. Simulation experiments show that CO-LEACH makes a better energy balance of all devices than state-of-the-art protocols, and prolongs the network lifetime by 3–5 times in data-heterogeneous WSNs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Data collection

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