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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced computer science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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Online Flow Scheduling in Virtualized Time-Sensitive Networks: A Joint Admission control and VNF Embedding Approach

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IEEE Transactions on Mobile Computing 2025年第6期24卷 4651-4667页

作者： Zhang, Yajing Chen, Cailian Li, Mingyan You, Chaoqun Guan, Xinping Quek, Tony Q. S. Shanghai Satellite Network Research Institute Co. Ltd. Shanghai200120 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Chongqing University College of Computer Science Chongqing400044 China Fudan University School of Computer Science Shanghai200433 China Singapore University of Technology and Design 487372 Singapore

Time-sensitive networking (TSN) is proposed to satisfy the increasingly stringent demands of Industrial 4.0 for deterministic transmission. This is achieved by generating a series of centrally configured gate control lists to strictly restrict the forwarding time of arriving flows. However, such a centralized scheme requires prior information of all flows, severely impeding TSN from providing an online response to dynamic industrial applications. To solve this problem, we innovatively propose to use admission control (AC) to realize deterministic transmission in the virtualized TSN network. In this approach, AC is distributively executed on each node and link, whereby flows of applications are served by passing through a series of virtual network functions (VNFs). This distributed AC execution is regarded as a VNF embedding (VNE) process. Specifically, we propose a two-stage online framework, Smart Admission control (SmartAC), to cater to dynamic applications. The first stage, referred to as the static stage, obtains a deterministic VNE solution by synthesizing AC decisions of individual TSN nodes and links. The second stage, referred to as the dynamic stage, fine-tunes VNE solutions obtained from the static stage to adapt to the harsh environment with insufficient resources or limited VNF migration budgets. Simulation results demonstrate the effectiveness of SmartAC in improving response rate and resource utilization ratio. Notably, SmartAC reduces runtime by 90% compared to existing algorithms and exhibits robustness across different network topologies. © 2002-2012 IEEE.

关键词： Budget control

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Task Offloading in Internet of Vehicles: A DRL-based Approach with Representation Learning for DAG Scheduling

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5045-5060页

作者： Deng, Xiaoheng Yang, Haoyu Zhang, Jingjing Gui, Jinsong Lin, Siyu Wang, Xin Min, Geyong Central South University School of Electronic Information Changsha410075 China Central South University Shenzhen Research Institute China Beijing Jiaotong University School of Electronic and Information Engineering The State Key Laboratory of Rail Traffic Control and Safety Beijing100044 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

The rapid evolution of the Internet-of-Vehicles (IoV) has amplified the need for mobile computing resources, driving the shift toward offloading tasks to edge servers or vehicles with idle resources to optimize computational efficiency. To this end, an approach based on Deep Reinforcement Learning (DRL) is presented in this paper, termed DVTP, which integrates Variational Graph Attention Networks (VGAT) and Transformer models to optimize Directed Acyclic Graph (DAG) task scheduling in vehicular networks. DVTP effectively captures both the spatiotemporal information and task dependencies, enabling more accurate and efficient task offloading decisions. Extensive simulation experiments demonstrate that DVTP outperforms traditional methods in reducing task completion times across various multi-vehicle and multi-edge server scenarios, showcasing its potential for real-world IoV applications. © 2025 IEEE.

关键词： Deep reinforcement learning

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Optimization of BLE Broadcast Mode in Offline Finding Network

arXiv

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arXiv 2025年

作者： Zhang, Li Feng, Cheng Xia, Tian School of Computer and Control Engineering Northeast Forestry University China School of Measurement-control Technology and Communications Engineering Harbin University of Science and Technology China

In the Offline Finding Network(OFN), offline Bluetooth tags broadcast to the surrounding area, the finder devices receiving the broadcast signal and upload location information to the IoT(Internet of Things) cloud servers, thereby achieving offline finding of lost items. This process is essentially a Bluetooth low energy (BLE) neighbor discovery process(NDP). In the process, the variety of Bluetooth scan modes caused by the scan interval and scan window settings affects the discovery latency of finder devices finding the tag broadcast packets. To optimize the experience of searching for lost devices, we propose the CPBIS-mechanism, a certain proportion broadcast-intervals screening mechanism that calculates the most suitable two broadcast intervals and their proportion for offline tags. This reduces discovery latency in the BLE NDP, improves the discovery success rate, further enhances the user experience. To our knowledge, we are the first to propose a comprehensive solution for configuring the broadcast interval parameters of advertisers in BLE NDP, particularly for configurations involving two or more broadcast intervals. We evaluated the results obtained by CPBIS on the nRF52832 chip. The data shows that the CPBIS-mechanism achieves relatively low discovery latencies for multiple scan modes. © 2025, CC BY.

关键词： Bluetooth

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Joint Sensing and Computation Incentive Mechanism for Mobile Crowdsensing Networks: A Multiagent Reinforcement Learning Approach

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IEEE Internet of Things Journal 2025年第9期12卷 13033-13046页

作者： Zhao, Nan Sun, Yiling Pei, Yiyang Niyato, Dusit Hubei University of Technology Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Wuhan430061 China Singapore Institute of Technology Information and Communications Technology Cluster Tampines Singapore Nanyang Technological University School of Computer Science and Engineering Jurong West Singapore

Mobile crowdsensing (MCS) is a novel sensing paradigm by utilizing mobile users (MUs) to collect data from environment. Considering the finite sensing and computing resources of MUs, it is crucial to inspire MUs to take part in crowdsensing willingly. In this study, a multiagent-deep-reinforcement-learning (DRL)-based incentive mechanism is investigated to tackle the joint data sensing and computing issues. Specifically, due to the heterogeneity of sensing tasks, multiple MCS platforms (MCPs) motivate MUs to participate in different tasks. The interaction between MCPs and MUs is modeled as a multileader-multifollower Stackelberg game with Stackelberg equilibrium proved by derivation. Moreover, the Stackelberg game is transformed as a Markov decision process (MDP) to deal with a multiagent DRL method without any prior knowledge. Due to the continuous high-dimensional action space of multiple MCPs and MUs, a multiagent double actors deep deterministic policy gradient (MA-DADDPG) algorithm is proposed to obtain the optimal sensing data size, computing resource, and incentive payment policies. Extensive simulation results illustrate the effectiveness of the proposed crowdsensing incentive mechanism. © 2014 IEEE.

关键词： Deep reinforcement learning

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Tissue P Systems with Look-Ahead Mode

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Chinese Journal of Electronics 2025年第1期23卷 81-86页

作者： Yun Jiang Tao Song Zheng Zhang School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China Department of Control Science and Engineering Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan China

Tissue P systems are a class of distributed and parallel computing models inspired from inter-cellular communication and cooperation between cells. In this work, a variant of tissue P system, named tissue P system with look-ahead mode, is discussed for decreasing the inherent non-determinism of tissue P systems and helping implementing tissue P systems on computers. Such systems are proved to be universal by simulating register machine, and they are also proved to be able to efficiently solve computationally hard problems by means of a space-time tradeoff, which is illustrated with a polynomial solution to 3-coloring problem.

关键词： computers Computational modeling Medical services Parallel processing Polynomials Computational efficiency Registers Business

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H-STEP: Heuristic Stable Edge Service Entity Placement for Mobile Virtual Reality Systems

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IEEE Transactions on Mobile Computing 2025年

作者： Chi, Xuejian Chen, Honglong Ni, Zhichen Sun, Haiyang Sun, Peng Yu, Dongxiao Shandong Provincial Engineering Research Center of Intelligent Sensing and Measurement and Control Technology College of Control Science and Engineering Qingdao266580 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Shandong University School of Computer Science and Technology Qingdao266237 China

Virtual reality (VR) technology, as a latency-sensitive application, can achieve real-time response to enhance the user's quality of experience (QoE) on edge devices. However, edge servers, unlike internally managed cloud servers, are prone to hardware failures, software abnormalities, and network attacks. Most prior studies have focused on reducing service delay and improving user coverage through service entity (SE) placement, often neglecting the critical impact of edge server malfunctions on user QoE. In this work, we design a stable service entity placement framework that connects users on faulty servers to collaborative edge servers, ensuring seamless task completion. This framework presents two primary challenges: determining the grouping of collaborative edge services and the placement of SEs. To address these challenges, we introduce a heuristic stable service entity placement (H-STEP) scheme. This scheme first determines the grouping of collaborative edge servers using an iterative search algorithm and then places SEs on suitable edge servers via a fast non-dominated sorting genetic placement algorithm. This approach balances stability benefits with total cost, enhancing the system's economic benefits. We theoretically analyze the performance of H-STEP and derive the performance gap between H-STEP and the optimal scheme. Extensive real-data-driven simulations demonstrate that H-STEP's performance closely approximates that of the optimal scheme and surpasses existing schemes. © 2002-2012 IEEE.

关键词： Virtual environments

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Log-driven predictive analysis of remaining time for emergency response processes

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Expert Systems with Applications 2025年 283卷

作者： Cao, Rui Zeng, Qingtian Guo, Shuai Ni, Weijian Duan, Hua Guo, Wenyan School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266510 China

The cross-organizational, messaging and resource attributes of emergency response processes effectively improve the accuracy of remaining time prediction. For this purpose, a log-driven analysis method for remaining time prediction of emergency response processes is proposed. Firstly, the attributes such as cross-organization, messaging and resources of the emergency response process are encoded, and then the vector representation of the emergency response process is obtained. Secondly, the vector representations of the emergency response processes are fed into a deep neural network prediction model for learning. Finally, we experimented with an emergency response process log to demonstrate the proposed method. © 2025 Elsevier Ltd

关键词： Deep neural networks

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Crowdsourcing Task Recommendation Method Based on Heterogeneous Graph Feature Anomaly Detection 18th

Crowdsourcing Task Recommendation Method Based on Heterogen...

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18th China Conference on Wireless Sensor Networks, CWSN 2024

作者： Wei, Kai Pan, Qingxian Yu, Song Fan, Zequn Li, Jinru School of Computer and Control Engineering Yantai University Yantai264005 China School of Computer and Information Science Southwest University Chongqing400715 China

ISBN: (纸本)9789819621859

The crowdsourcing task recommendation method addresses worker selection overload by intelligently matching workers with tasks. However, existing research often overlooks the inherent characteristics of workers and tasks, the long-term behavioral preferences of workers, and abnormal information about workers and tasks, which impacts the accuracy of recommendations. To address these issues, we propose a novel crowdsourcing task recommendation method based on anomaly detection using multi-attribute heterogeneous graph features. This method first establishes the connection relationships between crowdsourcing workers and tasks to construct a worker-task heterogeneous graph. DeepWalk generates initial node features, and the message-passing mechanism in the graph neural network is employed to explore the feature information of workers and task nodes. During message transmission, the graph convolutional autoencoder drives the detection of worker and task feature information. Finally, Bi-GRU captures the long-term behavioral preferences of workers, and link prediction constructs links based on node similarity to complete the recommendation of sensing tasks. We evaluate nine real datasets, and the experimental results demonstrate that the proposed method outperforms the baseline method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph neural networks

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RGB-Based Category-Level Object Pose Estimation via Depth Recovery and Adaptive Refinement

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IEEE Robotics and Automation Letters 2025年第6期10卷 5377-5384页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Zeng, Zhiwen Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha410082 China Central South University School of Architecture and Art Changsha410082 China University of Western Australia Department of Computer Science and Software Engineering CrawleyWA6009 Australia

Category-level pose estimation methods have received widespread attention as they can be generalized to intra-class unseen objects. Although RGB-D-based category-level methods have made significant progress, reliance on depth image limits practical application. RGB-based methods offer a more practical and cost-effective solution. However, current RGB-based methods struggle with object geometry perception, leading to inaccurate pose estimation. We propose depth recovery and adaptive refinement for category-level object pose estimation from a single RGB image. We leverage DINOv2 to reconstruct the coarse scene-level depth from the input RGB image and propose an adaptive refinement network based on an encoder-decoder architecture to dynamically improve the predicted coarse depth and reduce its gap from the ground truth. We introduce a 2D–3D consistency loss to ensure correspondence between the point cloud obtained from depth projection and the objects in the 2D image. This consistency supervision enables the model to maintain alignment between the depth image and the point cloud. Finally, we extract features from the refined point cloud and feed them into two confidence-aware rotation regression branches and a translation and size prediction residual branch for end-to-end training. Decoupling the rotation matrix provides a more direct representation, which facilitates parameter optimization and gradient propagation. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superior performance of our method. Real-world estimation and robotic grasping experiments demonstrate our model robustness to occlusion, clutter environments, and low-textured objects. Our code and robotic grasping video are available at DA-Pose. © 2016 IEEE.

关键词： Depth perception

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