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An Intelligent Privacy Protection Scheme for Efficient Edge Computation Offloading in IoV

Chinese Journal of Electronics 2024年第4期33卷 910-919页

作者： Liang YAO Xiaolong XU Wanchun DOU Muhammad Bilal School of Software Nanjing University of Information Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Department of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies

As a pivotal enabler of intelligent transportation system(ITS), Internet of vehicles(Io V) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive,and privacy-aware vehicular applications in Io V result in the transformation from cloud computing to edge computing,which enables tasks to be offloaded to edge nodes(ENs) closer to vehicles for efficient execution. In ITS environment,however, due to dynamic and stochastic computation offloading requests, it is challenging to efficiently orchestrate offloading decisions for application requirements. How to accomplish complex computation offloading of vehicles while ensuring data privacy remains challenging. In this paper, we propose an intelligent computation offloading with privacy protection scheme, named COPP. In particular, an Advanced Encryption Standard-based encryption method is utilized to implement privacy protection. Furthermore, an online offloading scheme is proposed to find optimal offloading policies. Finally, experimental results demonstrate that COPP significantly outperforms benchmark schemes in the performance of both delay and energy consumption.

关键词： Industries Privacy Energy consumption Transportation Computational efficiency Encryption Protection

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A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1412-1434页

作者： Shoaib Hassan Qianmu Li Khursheed Aurangzeb Affan Yasin Javed Ali Khan Muhammad Shahid Anwar School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingJiangsuChina Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia School of Software Northwestern Polytechnical UniversityXianShaanxiChina Department of Computer Science School of PhysicsEngineering&Computer ScienceUniversity of HertfordshireHatfieldUK Department of AI and Software Gachon University Seongnam-siSeongnamSouth Korea

Over the past few years,the application and usage of Machine Learning(ML)techniques have increased exponentially due to continuously increasing the size of data and computing *** the popularity of ML techniques,only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement engineering(RE)activities to solve the problems that occur in RE *** authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–*** authors aim to investigate the research trends,main RE activities,ML algorithms,and data sources that were studied during this ***-five research studies were selected based on our exclusion and inclusion *** results show that the scientific community used 57 *** those algorithms,researchers mostly used the five following ML algorithms in RE activities:Decision Tree,Support Vector Machine,Naïve Bayes,K-nearest neighbour Classifier,and Random *** results show that researchers used these algorithms in eight major RE *** activities are requirements analysis,failure prediction,effort estimation,quality,traceability,business rules identification,content classification,and detection of problems in requirements written in natural *** selected research studies used 32 private and 41 public data *** most popular data sources that were detected in selected studies are the Metric Data Programme from NASA,Predictor Models in Software engineering,and iTrust Electronic Health Care System.

关键词： data sources machine learning requirement engineering supervised learning algorithms

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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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ReQ-tank: Fine-grained Distributed Machine Learning Flow Scheduling Approach 29

ReQ-tank: Fine-grained Distributed Machine Learning Flow Sch...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Xu, Quanyi Yan, Kedong Yin, Dan Huang, Chanying Xiao, Shan Yang, Yuxin School of Cyber Science and Engineering Nanjing University of Science and Technology Jiangyin China School of Computer Science and Engineering Nanjing Univ. of Science and Technology Nanjing China School of Computer Science and Engineering Beijing Univ. of Civil Engineering and Architecture Beijing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Fiberhome Communication Tech. Co. Wuhan China

ISBN: (纸本)9798350330717

The swift advancement of distributed computing has enhanced the support for big data and massive-scale models. Yet, delivering superior services to manage large and intricate network flows in data center networks remains a formidable challenge. In this paper, we present ReQ-tank, an intricate flow scheduling approach based on a multi-level feedback queue (MLFQ) devised to achieve flow prioritization and efficient flow scheduling. ReQ-tank employs a two-tier scheduling strategy: On the flow scheduling layer, priority queues are segmented into two categories, and the flows within high-priority queues follow a strict priority scheduling, while those in low-priority queues adhere to differential weighted Round-robin scheduling;On the packet scheduling layer, ReQ-tank modifies the priority of initially high-priority re-transmitted packets to facilitate fine-grained data packet scheduling. We carry out simulation experiments on web search workloads and data mining workloads. Experimental results demonstrate that ReQ-tank can curtail packet wait time in the network, significantly truncate the flow completion time (FCT) of delay-sensitive flows and counteract the issue of flow starvation in traditional strict priority queues. Consequently, ReQ-tank is deemed more suitable for complex distributed network applications. © 2023 IEEE.

关键词： Data Center Networking Flow scheduling packet scheduling SDN

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SAM-Aware Graph Prompt Reasoning Network for Cross-Domain Few-Shot Segmentation 39

SAM-Aware Graph Prompt Reasoning Network for Cross-Domain Fe...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Peng, Shi-Feng Sun, Guolei Li, Yong Wang, Hongsong Xie, Guo-Sen School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Computer Vision Laboratory ETH Zurich Switzerland School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)157735897X

The primary challenge of cross-domain few-shot segmentation (CD-FSS) is the domain disparity between the training and inference phases, which can exist in either the input data or the target classes. Previous models struggle to learn feature representations that generalize to various unknown domains from limited training domain samples. In contrast, the large-scale visual model SAM, pre-trained on tens of millions of images from various domains and classes, possesses excellent generalizability. In this work, we propose a SAM-aware graph prompt reasoning network (GPRN) that fully leverages SAM to guide CD-FSS feature representation learning and improve prediction accuracy. Specifically, we propose a SAM-aware prompt initialization module (SPI) to transform the masks generated by SAM into visual prompts enriched with high-level semantic information. Since SAM tends to divide an object into many sub-regions, this may lead to visual prompts representing the same semantic object having inconsistent or fragmented features. We further propose a graph prompt reasoning (GPR) module that constructs a graph among visual prompts to reason about their interrelationships and enable each visual prompt to aggregate information from similar prompts, thus achieving global semantic consistency. Subsequently, each visual prompt embeds its semantic information into the corresponding mask region to assist in feature representation learning. To refine the segmentation mask during testing, we also design a non-parameter adaptive point selection module (APS) to select representative point prompts from query predictions and feed them back to SAM to refine inaccurate segmentation results. Experiments on four standard CD-FSS datasets demonstrate that our method establishes new state-of-the-art results. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semantic Segmentation

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RASTMTrack: Robust and Adaptive Space-Time Memory Networks for Visual Tracking 14

RASTMTrack: Robust and Adaptive Space-Time Memory Networks f...

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14th IEEE International Conference on Cyber technology in Automation, Control, and Intelligent Systems, CYBER 2024

作者： Zheng, Yu Liu, Yong Che, Xun School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Xiaolingwei Nanjing210094 China School of Computer Science and Engineering Nanjing University of Science and Technology Xiaolingwei Jiangsu Nanjing210094 China

ISBN: (纸本)9798331506056

These trackers based on the space-time memory network locate the target object in the search image employing contextual information from multiple memory frames and their corresponding foreground-background features. It is conceivable that these trackers are susceptible to the memory frame quality as well as the accuracy of the corresponding foreground labels. In the previous work (STMTrack), a fixed and experienced extraction strategy for memory frames was adopted, which hinders the improvement of performance and generalization. To address these limitations, we propose a dynamic and adaptive extraction strategy for memory frames to ensure that the most representative historical frames are selected into the set of memory frames to improve the accuracy of localization and reduce failures due to error accumulation. Specifically, we propose an extraction network to evaluate historical frames, where historical frames with the highest score are labeled as the memory frame and conversely dropped. Qualitative and quantitative analyses were implemented on multiple datasets (LaSOT and GOT-10K), and our method achieves significant performance gains (LaSOT: 60.6→61.4, GOT-10K: 64.2→65.2) over STMTrack especially for challenging scenarios (e.g., occlusion, vanishing, and reappearance, etc.). while bringing only a negligible inference speed degradation (Fps: 37 → 34), otherwise, the proposed method obtains competitive results compared to other state-of-the-art (SOTA) methods (CNN-based). © 2024 IEEE.

关键词： Space time adaptive processing

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Multi-Dimensional Training Optimization for Efficient Federated Synergy Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6243-6258页

作者： Fu, Shucun Dong, Fang Chen, Runze Shen, Dian Zhang, Jinghui He, Qiang Southeast University School of Computer Science and Engineering Nanjing211189 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China

Edge learning (EL) is an end-to-edge collaborative learning paradigm enabling devices to participate in model training and data analysis, opening countless opportunities for edge intelligence. As a promising EL framework, federated synergy learning (FSyL) mitigates the computation and communication overhead on resource-constrained devices by offloading partial model layers to the edge server for synergistic training. Nevertheless, due to the system and statistical heterogeneity, naively using existing FSyL methods is significantly time-consuming and causes accuracy degradation. Motivated by this issue, this paper introduces a novel FSyL framework that integrates multi-dimensional training optimization and formulates the edge learning cost minimization (ELCM) problem. To tackle the ELCM efficiently, we design OL-MG, an OnLine Model Splitting and Resource Provisioning Game. Specifically, we first reformulate and decompose the original ELCM based on data quality evaluation. Then, given a model splitting decision, we determine the optimal resource provisioning in Sub-problem1, based on which optimal model splitting in Sub-problem2 is modeled as a potential game. Subsequently, we introduce a decentralized algorithm to find a Nash equilibrium (NE) solution. Furthermore, we further extend OL-MG to support a budget-aware multi-edge scenario. Extensive experiments demonstrate that the proposed mechanism significantly outperforms state-of-the-art methods in cost-saving and accuracy improvement. © 2002-2012 IEEE.

关键词： Federated learning

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On Designs of Decentralized Reputation Management for Permissioned Blockchain Networks

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computer Modeling in engineering & sciences 2024年第5期139卷 1755-1773页

作者： Jinyu Chen Long Shi Qisheng Huang Taotao Wang Daojing He School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing210094China School of Mechanical Engineering and Automation Harbin Institute of TechnologyShenzhen518055China College of Electronics and Information Engineering Shenzhen UniversityShenzhen518060China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

In permissioned blockchain networks,the Proof of Authority(PoA)consensus,which uses the election of authorized nodes to validate transactions and blocks,has beenwidely advocated thanks to its high transaction throughput and fault ***,PoA suffers from the drawback of centralization dominated by a limited number of authorized nodes and the lack of anonymity due to the round-robin block proposal *** a result,traditional PoA is vulnerable to a single point of failure that compromises the security of the blockchain *** address these issues,we propose a novel decentralized reputation management mechanism for permissioned blockchain networks to enhance security,promote liveness,and mitigate centralization while retaining the same throughput as traditional *** paper aims to design an off-chain reputation evaluation and an on-chain reputation-aided ***,we evaluate the nodes’reputation in the context of the blockchain networks and make the reputation globally verifiable through smart ***,building upon traditional PoA,we propose a reputation-aided PoA(rPoA)consensus to enhance securitywithout sacrificing *** particular,rPoA can incentivize nodes to autonomously form committees based on reputation authority,which prevents block generation from being tracked through the randomness of reputation ***,we develop a reputation-aided fork-choice rule for rPoA to promote the network’s ***,experimental results show that the proposed rPoA achieves higher security performance while retaining transaction throughput compared to traditional PoA.

关键词： Blockchain reputation management PoA throughput security decentralization

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Mutual A Priori based Completion for Low Overlapping Point Clouds Registration

Mutual A Priori based Completion for Low Overlapping Point C...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Liu, Zhiyong Liu, Yazhou School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9781665410205

This work presents a new registration method that especially designed for low-overlapping partial point clouds. Based on the assumption that the partial point clouds to be registered belong to the same target, the proposed Mutual a Priori based Completion (MPC) method uses the partial point clouds to be registered as the a priori for each other to expend each individual point clouds. The main challenge of this mutual reference approach is that the partial clouds without spatial alignment cannot provide reliable completion reference for each. Based on the mutual information maximization, a progressive completion structure is developed to make the alignment in the spatial, feature and completion space respectively. Experiments on public datasets show encouraging results. Especially for the ultra-low overlap cases, compared with the state-of-the-art (SOTA) models, the size of overlapping regions can be increased by about 14.0%, and the rotation and translation error can be reduced by 30.8% and 57.7% respectively. © 2024 IEEE.

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Joint Optimization of Service Caching and Task Scheduling in Energy-Efficient Mobile Edge Computing 4

Joint Optimization of Service Caching and Task Scheduling in...

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4th International Conference on Intelligent technology and Embedded Systems, ICITES 2024

作者： Chen, Bin Huang, Wenxuan Wang, Linhui Zhang, Yi Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

ISBN: (纸本)9798331506292

This paper studies an energy-efficient task scheduling problem that takes into account the cooperation among service caching-enabled mobile edge computing (MEC) servers. We consider a MEC system consisting of multiple sub-networks of MEC nodes and formulate the studied problem as a constrained optimization model that makes decisions on energy-efficient assignment of task requests. To cope with the formulated optimization problem, we propose a metaheuristic approach that is built upon the searching mechanism of the squirrel search algorithm (SSA) but involves improved operators and name the metaheuristic approach ISSA. By using an offloading sequence (i.e., a permutation of all computation tasks) to represent the scheduling solution, ISSA uses a cooperative task assignment heuristic for evaluating the fitness value of each candidate solution. This heuristic is responsible for dispatching each task in the sequence to a certain MEC node within the sub-network or outsourcing the task to the cloud in case of insufficient resources, such that the total energy consumption can be calculated. In addition, when updating the squirrel positions to facilitate the metaheuristic search, we employ a new updating rule to enhance ISSA's capability in exploring the solution space. We perform extensive simulations to demonstrate the advantage of our proposed ISSA over other metaheuristic approaches in reducing the total energy consumption. © 2024 IEEE.

关键词： Constrained optimization

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