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检索条件"任意字段=Proceedings of the 22nd International Conference on Distributed Computing and Networking"

共 1646 条记录，以下是31-40 订阅

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Personalized Privacy-Preserving Semi-Centralized Recommendation System in a Trust-Based Agent Network 22

Personalized Privacy-Preserving Semi-Centralized Recommendat...

引用

IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Wen, Qi Leung, Carson K. Pazdor, Adam G. M. Univ Manitoba Winnipeg MB Canada

ISBN: (纸本)9798350381993;9798350382006

In the current big data era, recommendation systems play an important role in our daily life to help us make faster and better decisions from massive numbers of choices. Personalized recommendation has gained its popularity as it provides recommendations according to the user profile, preferences and/or interests. Many existing systems make recommendation by centralizing data. However, the exposure of sensitive data raises a privacy concern as research has shown that it is possible to de-identify anonymous users. Examples include inferring sensitive information (e.g., political views, sexual orientations) from nonsensitive data (e.g., movie ratings). In this paper, we present a personalized privacy-preserving recommendation system called Trust-based Agent Network (TAN). It tackles the privacy issue by semi-decentralizing data and treating each node in the network as an agent. As such, data are distributed to each agent within each trusted network, and the recommendation service provider collects only obfuscated data from agents by adopting the differential-privacy mechanism. Consequently, data in our TAN are either protected inside local trusted networks or obfuscated outside of trusted networks. Final recommendation can then be made by aggregating the local suggestions from the trusted network and obfuscated global suggestions from the service provider. Personalized recommendations can be made by putting more emphasize on local suggestions. Evaluation results show that our TAN leads to high accuracy and highly personalized recommendations while protecting privacy.

关键词： Recommendation system Personalization Collaborative filtering Semi-centralized Trusted network Privacy preservation Ubiquitous computing

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VMR-Tree: Efficient and Verifiable Location-based kNN Queries on Blockchain 22

VMR-Tree: Efficient and Verifiable Location-based kNN Querie...

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IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Teng, Yiping Liu, Lei Qi, Jiawei Pan, Haochun Fan, Chunlong Shenyang Aerosp Univ Sch Comp Large Scale Distributed Syst Lab Liaoning Shenyang Peoples R China

ISBN: (纸本)9798350381993;9798350382006

In recent years, blockchain technology has received extensive attention and applied in various fields including healthcare, IoT and database systems. Utilizing the decentralization and anti-tampering properties, the blockchain provides potential solutions to achieve verification of data queries, without the assumption of the trusted third parties in traditional data verification studies. However, for kNN queries, a common query type in practical location-based scenarios, few existing solutions can directly support the location-based kNN query processing and result authentication based on blockchain. To address this problem, in this paper, we propose a blockchain-based verifiable kNN query processing method. In this method, we first design a novel authenticated data structure called VMR-Tree, which stores the data objects and their neighboring objects in leaf nodes and stores the hash values used for data verification in non-leaf nodes. To verify the results with the minimum size of verification objects (VOs), we design a query result verification method based on the blockchain, in which the client can verify the query results by processing the VOs generated based on the proposed VMR-Tree index and the blockchain. Besides, we further propose an optimization algorithm to reduce the size of VOs. We theoretically analyze the computational complexity and security guarantees of the proposed approaches. We also conducted extensive experiments on real and synthetic datasets to evaluate the efficiency of the proposed method on the result verification of location-based kNN queries.

关键词： Blockchain kNN Query Query Authentication

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2024 IEEE international conference on Pervasive computing and Communications, PerCom 2024

2024 IEEE International Conference on Pervasive Computing an...

引用

22nd IEEE international conference on Pervasive computing and Communications, PerCom 2024

ISBN: (纸本)9798350326031

The proceedings contain 23 papers. The topics discussed include: decentralized landslide disaster prediction for imbalanced and distributed data;DiTMoS: Delving into diverse tiny-model selection on microcontrollers;BLEAR: practical wireless earphone tracking under BLE protocol;UltraWrite: a lightweight continuous gesture input system with ultrasonic Signals on COTS devices;Affective-NLI: towards accurate and interpretable personality recognition in conversation;iMove: exploring bio-impedance sensing for fitness activity recognition;practical latency-aware scheduling for low-latency elephant VR flows in Wi-Fi networks;and semi-automated framework for digitalizing multi-product warehouses with large scale camera arrays.

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A Crowdsensing Service Pricing Method in Vehicular Edge computing 22

A Crowdsensing Service Pricing Method in Vehicular Edge Comp...

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22nd IEEE international Symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： Li, Zheng Tang, Sizhe Tian, Hao Xiang, Haolong Xu, Xiaolong Dou, Wanchun Nanjing University State Key Laboratory of Novel Software Technology Nanjing China Nanjing University of Information Science and Technology School of Software Nanjing China

ISBN: (纸本)9798331509712

The rapid advancements in vehicular networking technology have enabled onboard users to access a variety of emerging services like precise navigation and real-time hazard avoidance. However, as the diversity and volume of required data expand and demands intensify, most of vehicular services are unable to satisfy user expectations. Although crowdsensing architectures based on edge computing have been proposed in vehicular networks, how to optimally allocate the sensing service time of roadside units and set appropriate prices for services to maximize user benefits still remain significant challenges. To solve the above issue, in this paper, a crowdsensing service pricing method is proposed in vehicular edge computing. Specifically, a vehicular networking crowdsensing framework based on the edge computing is designed. Then, the optimization problem of perception time pricing during the crowdsensing process is modeled into a Stackelberg game and further the multi-agent deep deterministic policy gradient-based algorithm is employed to obtain the optimal pricing strategy with user profits maximization. Finally, simulation results demonstrate that the proposed method significantly increases the payoff for all participants during the crowdsensing process. © 2024 IEEE.

关键词： Reinforcement learning

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Enabling 6D Pose Tracking on Your Acoustic Devices 24

Enabling 6D Pose Tracking on Your Acoustic Devices

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22nd Annual international conference on Mobile Systems, Applications and Services (MOBISYS)

作者： Wu, Yuanyuan Chen, Sheng Meng, Xuanqi XinyuTong Liu, Xiulong Xie, Xin Qu, Wenyu Tianjin Key Lab Adv Networking TANK Tianjin Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China

ISBN: (纸本)9798400705816

The ubiquity of acoustic devices and the fine-grained sensing of acoustic signals have made acoustic device tracking a popular option. We propose to expand the use of commercial devices with microphones as an extension of the audio system to support intelligent applications, such as VR/AR. This paper introduces a novel 6D acoustic pose estimation system. To realize device-based pose estimation, most existing systems deploy multiple speakers. However, due to limited inaudible bandwidth, concurrent transmissions with multiple speakers pose challenges in balancing resolution and frame rate. To address this problem, we design 2xTrack, a band multiplexing signal model that doubles the availability of limited bandwidth by utilizing a unique encoding strategy for concurrent transmissions. We also propose solutions to enhance signal feature estimation and implement a 6DoF pose tracking scheme tailored for distributed systems. The prototype is deployed on a typical circular microphone array, and experimental results show that 2xTrack achieves a median position and orientation error of 7.6mm and 4.1 degrees, respectively, in a 4-speaker setup. Our extended applications on commercial devices also showcase the versatility of our system, particularly in face orientation detection, air mouse and drone tracking.

关键词： Acoustic signal FMCW pose tracking 6DoF band multiplexing

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Task Scheduling Policies in a Cloud computing Environment 22

Task Scheduling Policies in a Cloud Computing Environment

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22nd RoEduNet conference: networking in Education and Research, RoEduNet 2023

作者： Mancas, Catalina Dumitrascu, Eugen Ganea, Eugen Enescu, Nicolae Nedianu, Gabriel-Catalin University of Craiova Faculty of Automation Computers and Electronics Craiova Romania Software Development Department NetDania SRL Craiova Romania

ISBN: (纸本)9798350393651

Virtualization solutions used within Cloud computing have proven over time to address the problem of inefficient use of physical computing resources. Virtualization can provide high-level availability to critical applications with, thus streamlining the operation of IT infrastructure and responding quickly to changes. The paper proposes a mathematical model based on Queuing Theory aimed at ensuring a certain level of Quality of Service (QoS). © 2023 IEEE.

关键词： Cloud computing

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Federated Learning-Based In-Network Traffic Analysis on IoT Edge 22

Federated Learning-Based In-Network Traffic Analysis on IoT ...

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22nd international-Federation-for-Information-Processing (IFIP) networking conference (IFIP networking)

作者： Zang, Mingyuan Zheng, Changgang Koziak, Tomasz Zilberman, Noa Dittmann, Lars Tech Univ Denmark Lyngby Denmark Netlight Slough England Univ Oxford Oxford England

ISBN: (纸本)9783903176577

The rise of IoT-connected devices has led to an increase in collected data for service and traffic analysis, but also to emerging threats and attacks. In-network machine learning-based attack detection has proven effective in fast response, but scaling to distributed IoT edge devices risks increasing communication overheads and raising data privacy concerns. To address these concerns, we present FLIP4, a distributed in-network attack detection framework based on federated tree models. FLIP4 maintains data privacy by enabling distributed machine learning training while keeping data local on IoT edge, and provides in-network inference within the programmable data plane on edge gateway for timely attack labeling and mitigation. Evaluation results show that FLIP4 can accurately detect attacks while maintaining source data privacy and enabling lightweight deployment on IoT edge.

关键词： In-Network computing Federated Learning Security Internet of Things P4

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Experimental and Theoretical Analysis of Deep Residual Time-Delay Reservoir computing Based on Clipping Algorithm 22

Experimental and Theoretical Analysis of Deep Residual Time-...

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22nd international conference on Optical Communications and Networks, ICOCN 2024

作者： Zhou, Changdi Li, Nianqiang School of Optoelectronic Science and Engineering Collaborative Innovation Center of Suzhou Nano Science and Technology Soochow University Suzhou215006 China Soochow University Key Lab of Advanced Optical Manufacturing Technologies of Jiangsu Province Key Lab of Modern Optical Technologies of Education Ministry of China Suzhou215006 China

ISBN: (纸本)9798350367652

Photonic reservoir computing (RC), as a crucial variant of recurrent neural networks, has boasted substantial potential owing to its straightforward training processes and facile hardware integration. In this work, we introduce and validate both experimentally and numerically a novel post-processing technique, referred to as the clipping algorithm (CA), utilized on the high-performance deep residual time-delay RC (DR-TDRC). The proof-of-concept model, i.e., a four-layer DR-TDRC, comprises 960 interrelated neurons (240 neurons per layer) based on four injection-locked distributed feedback lasers. Our results demonstrate that the implementation of the CA can effectively mitigate issues associated with the redundant layers and improve the performance of deep architectures. © 2024 IEEE.

关键词： distributed feedback lasers

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Game Theoretic Resource Planning and Request Scheduling in Mobile Edge computing Networks 22

Game Theoretic Resource Planning and Request Scheduling in M...

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22nd international-Federation-for-Information-Processing (IFIP) networking conference (IFIP networking)

作者： Xiang, Bin Elias, Jocelyne Martignon, Fabio Di Nitto, Elisabetta Niyato, Dusit CNRS CREATE Singapore Italy Univ Bologna Bologna Italy Univ Bergamo Bergamo Italy Politecn Milan Milan Italy Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9783903176577

Mobile Edge computing (MEC) networks offer an increasing computing power through the collaboration among MEC nodes. This opens a large computing market and brings challenges for efficient resource management. In this paper, we study the joint optimization problem of planning cost-efficient edge networks, allocating link and computation resources, as well as scheduling and routing user requests in edge computing networks with arbitrary topologies and multiple ingress nodes. We formulate this problem as a Stackelberg game where the network operator, as the leader, aims at maximizing its profit, and the edge nodes, as the followers, minimize their users' costs and latency. Then, we prove the existence of the generalized Nash equilibrium for the follower subgame, and the Stackelberg equilibrium for the leader-follower game. We further propose a distributed best-response algorithm for the follower game and an alternating leader-follower optimization algorithm for the full game to compute the equilibrium and prove its convergence. A centralized optimization incorporating both profit and network latency targets is formulated and solved, which serves as benchmark for the game solution. Extensive numerical results demonstrate the effectiveness of the proposed game, achieving near-optimal planning and scheduling solutions in a very short time even for large-scale edge networks.

关键词： Edge computing network planning request scheduling and routing Stackelberg game

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Network Anomaly Detection in distributed Edge computing Infrastructure 22

Network Anomaly Detection in Distributed Edge Computing Infr...

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22nd IEEE Consumer Communications and networking conference, CCNC 2025

作者： Marfo, William Rico, Enrique A. Tosh, Deepak K. Moore, Shirley V. University of Texas at El Paso Department of Computer Science El Paso United States

ISBN: (纸本)9798331508050

As networks continue to grow in complexity and scale, detecting anomalies has become increasingly challenging, particularly in diverse and geographically dispersed environments. Traditional approaches often struggle with managing the computational burden associated with analyzing large-scale network traffic to identify anomalies. This paper introduces a distributed edge computing framework that integrates federated learning with Apache Spark and Kubernetes to address these challenges. We hypothesize that our approach, which enables collaborative model training across distributed nodes, significantly enhances the detection accuracy of network anomalies across different network types. We show that by leveraging distributed computing and containerization technologies, our framework not only improves scalability and fault tolerance but also achieves superior detection performance compared to state-of-the-art methods. Extensive experiments on the UNSW-NB15 and ROAD datasets validate the effectiveness of our approach, demonstrating statistically significant improvements in detection accuracy and training efficiency over baseline models, as confirmed by MannWhitney U and Kolmogorov-Smirnov tests (p © 2025 IEEE.

关键词： Edge computing

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