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检索条件"丛书名=Computer Communications and Networks"

共 4934 条记录，以下是201-210 订阅

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Context-Aware Online Offloading Strategy with Mobility Prediction for Mobile Edge Computing 30

Context-Aware Online Offloading Strategy with Mobility Predi...

引用

30th International Conference on computer communications and networks (ICCCN)

作者： Chen, Shuyu Chen, Haopeng Ruan, Jinteng Wang, Ziming Shanghai Jiao Tong Univ Sch Software Shanghai Peoples R China

ISBN: (纸本)9780738113302

With the development of 5G technology and the proliferation of various mobile applications, mobile edge computing (MEC) provides services near the user side to meet the quality constraints of different tasks. Most current works focus on the offloading decision and resource allocation issues in MEC. However, few works focus on user mobility and the personalized preferences of different applications. In this paper, we study these issues and propose a deep reinforcement learning (DRL) based context-aware online offloading strategy. To further reduce the overhead caused by user mobility for task offloading and migration, we consider the user's future movement trajectory and calculate the potential migration cost. Considering the dynamic network environment and the incompleteness of the observed system state information, we formulate the offloading decision problem as a partially observable Markov decision process (POMDP) problem, and then devise an efficient DRL algorithm to speed it up. We use EdgeCloudSim tool and Geolife trajectory to simulate the task offloading decision problem. The simulation results show that the proposed strategy is superior to other baseline strategies in terms of the total cost, delay, energy consumption, migration cost, and can be well adapted to different preferences and the dynamic network environment.

关键词： Mobile edge computing dynamic task offloading deep reinforcement learning mobility

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P4 Programmability at the Network Edge: the BRAINE Approach [Invited] 30

P4 Programmability at the Network Edge: the BRAINE Approach ...

引用

30th International Conference on computer communications and networks (ICCCN)

作者： Cugini, Filippo Scano, Davide Giorgetti, Alessio Sgambelluri, Andrea Castoldi, Piero Paolucci, Francesco CNIT Pisa Italy Scuola Super Sant Anna Pisa Italy CNR IEIIT Pisa Italy

ISBN: (纸本)9780738113302

Network programmability based on the P4 language is gaining consensus in multiple scenarios, including edge computing. In this work, we present the P4-based edge networking solutions adopted in the framework of the EU-funded BRAINE Project. The project targets the design and development of a powerful edge micro data center (EMDC) aiming at boosting artificial intelligence (AI) at the network edge. The EMDC will encompass an embedded programmable P4 ASIC supporting unprecedented intra- and inter-edge/fog interconnection. In this paper, a selection of the solutions to be supported by the P4 switch embedded within the BRAINE EMDC is presented. They include decentralized traffic engineering solutions driven in-band telemetry (INT), quality of service enforcement and verification, 5G network function virtualization, and decentralized cyber-security at the edge.

关键词： P4 security programmability programmable data plane 5G GTP INT in-band telemetry

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Realization of an Intrusion Detection use-case in ONAP with Acumos 30

Realization of an Intrusion Detection use-case in ONAP with ...

引用

30th International Conference on computer communications and networks (ICCCN)

作者： Sultana, Shabnam Dooze, Philippe Kumar, Vijay Venkatesh Highst Technol GmbH Berlin Germany Orange Labs Lannion France AT&T Serv Inc Deptford NJ USA Tech Univ Chemnitz Chair Commun Networks Chemnitz Germany

ISBN: (纸本)9780738113302

With Software-Defined Networking and Machine Learning/Artificial Intelligence (ML/AI) reaching new paradigms in their corresponding fields, both academia and industry have exhibited interests in discovering unique aspects of intelligent and autonomous communication networks. Transforming such intentions and interests to reality involves software development and deployment, which has its own story of significant evolution. There has been a notable shift in the strategies and approaches to software development. Today, the divergence of tools and technologies as per demand is so substantial that adapting a software application from one environment to another could involve tedious redesign and redevelopment. This implies enormous effort in migrating existing applications and research works to a modern industrial setup. Additionally, the struggles with sustainability maintenance of such applications could be painful. Concerning ML/AI, the capabilities to train, deploy, retrain, and re-deploy AI models as quickly as possible will be crucial for AI-driven network systems. An end-to-end workflow using unified open-source frameworks is the need of the hour to facilitate the integration of ML/AI models into the modern software-driven virtualized communication networks. Hence, in our paper, we present such a prototype by demonstrating the journey of a sample SVM classifier from being a python script to be deployed as a micro-service using ONAP and Acumos. While illustrating various features of Acumos and ONAP, this paper intends to make readers familiar with an end-to-end workflow taking advantage of the integration of both open-source platforms.

关键词： ONAP-DCAE Acumos Acumos-DCAE adapter

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A Blockchain and Self-Sovereign Identity Empowered Digital Identity Platform 30

A Blockchain and Self-Sovereign Identity Empowered Digital I...

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30th International Conference on computer communications and networks (ICCCN)

作者： Bandara, Eranga Liang, Xueping Foytik, Peter Shetty, Sachin De Zoysa, Kasun Old Dominion Univ Norfolk VA 23529 USA Univ North Carolina Greensboro Greensboro NC USA Univ Colombo Sch Comp Colombo Sri Lanka

ISBN: (纸本)9780738113302

Most of the existing identity systems are built on top of centralized storage systems. Storing identity data on these types of centralized storage platforms(e.g cloud storage, central servers) becomes a major privacy concern since various types of attacks and data breaches can happen. With this research, we are proposing blockchain and self-sovereign identity based digital identity (KYC - Know Your Customer) platform "Casper" to address the issues on centralized identity systems. "Casper " is an Android/iOS based mobile identity wallet application that combines the integration of blockchain and a self-sovereign identity-based approach. Unlike centralized identity systems, the actual identities of the customer/users are stored in the customers' mobile wallet application. The proof of these identities is stored in the blockchain-based decentralized storage as a self-sovereign identity proof. Casper platforms' Self-Sovereign Identity(SSI)-based system provides a Zero Knowledge Proof(ZKP) mechanism to verify the identity information. Casper platform can be adopted in various domains such as healthcare, banking, government organization etc. As a use case, we have discussed building a digital identity wallet for banking customers with the Casper platform. Casper provides a secure, decentralized and ZKP verifiable identity by using blockchain and SSI based approach. It addresses the common issues in centralized/cloud-based identity systems platforms such as the lack of data immutability, lack of traceability, centralized control etc.

关键词： Blockchain Self-Sovereign Identity Zero-Knowledge Proof Cloud Computing

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Massive MIMO-NOMA Systems Secrecy in the Presence of Active Eavesdroppers 30

Massive MIMO-NOMA Systems Secrecy in the Presence of Active ...

引用

30th International Conference on computer communications and networks (ICCCN)

作者： Soltani, Marziyeh Mirmohseni, Mahtab Papadimitratos, Panos Sharif Univ Technol Dept Elect Engn Tehran Iran KTH Royal Inst Technol Networked Syst Secur Grp Stockholm Sweden

ISBN: (纸本)9780738113302

Non-orthogonal multiple access (NOMA) and massive multiple-input multiple-output (MIMO) systems are highly efficient. Massive MIMO systems are inherently resistant to passive attackers (eavesdroppers), thanks to transmissions directed to the desired users. However, active attackers can transmit a combination of legitimate user pilot signals during the channel estimation phase. This way they can mislead the base station (BS) to rotate the transmission in their direction, and allow them to eavesdrop during the downlink data transmission phase. In this paper, we analyse this vulnerability in an improved system model and stronger adversary assumptions, and investigate how physical layer security can mitigate such attacks and ensure secure (confidential) communication. We derive the secrecy outage probability (SOP) and a lower bound on the ergodic secrecy capacity, using stochastic geometry tools when the number of antennas in the BSs tends to infinity. We adapt the result to evaluate the secrecy performance in massive orthogonal multiple access (OMA). We find that appropriate power allocation allows NOMA to outperform OMA in terms of ergodic secrecy rate and SOP.

关键词： Massive MIMO NOMA Secrecy Stochastic Geometry

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Contrastive Self-Supervised Representation Learning for Sensing Signals from the Time-Frequency Perspective 30

Contrastive Self-Supervised Representation Learning for Sens...

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30th International Conference on computer communications and networks (ICCCN)

作者： Liu, Dongxin Wang, Tianshi Liu, Shengzhong Wang, Ruijie Yao, Shuochao Abdelzaher, Tarek Univ Illinois Dept Comp Sci Champaign IL 61820 USA George Mason Univ Dept Comp Sci Fairfax VA 22030 USA

ISBN: (纸本)9780738113302

This paper presents a contrastive self-supervised representation learning framework that is new in being designed specifically for deep learning from frequency domain data. Contrastive self-supervised representation learning trains neural networks using mostly unlabeled data. It is motivated by the need to reduce the labeling burden of deep learning. In this paper, we are specifically interested in applying this approach to physical sensing scenarios, such as those arising in Internet-of-Things (IoT) applications. Deep neural networks have been widely utilized in IoT applications, but the performance of such models largely depends on the availability of large labeled datasets, which in turn entails significant training costs. Motivated by the success of contrastive self-supervised representation learning at substantially reducing the need for labeled data (mostly in areas of computer vision and natural language processing), there is growing interest in customizing the contrastive learning framework to IoT applications. Most existing work in that space approaches the problem from a time-domain perspective. However, IoT applications often measure physical phenomena, where the underlying processes (such as acceleration, vibration, or wireless signal propagation) are fundamentally a function of signal frequencies and thus have sparser and more compact representations in the frequency domain. Recently, this observation motivated the development of Short-Time Fourier Neural networks (STFNets) that learn directly in the frequency domain, and were shown to offer large performance gains compared to Convolutional Neural networks (CNNs) when designing supervised learning models for IoT tasks. Hence, in this paper, we introduce an STFNet-based Contrastive Self-supervised representation Learning framework (STF-CSL). STF-CSL takes both time-domain and frequency-domain features into consideration. We build the encoder using STFNet as the fundamental building block. We also apply b

关键词： IoT Self-supervision Frequency Domain Representation Learning

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It's a Matter of Style: Detecting Social Bots through Writing Style Consistency 30

It's a Matter of Style: Detecting Social Bots through Writin...

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30th International Conference on computer communications and networks (ICCCN)

作者： Cardaioli, Matteo Conti, Mauro Di Sorbo, Andrea Fabrizio, Enrico Laudanna, Sonia Visaggio, Corrado A. Univ Padua Dept Math Padua Italy Univ Sannio Dept Engn Benevento Italy Open Reply Rome Italy GFT Italy Milan Italy

ISBN: (纸本)9780738113302

Social bots are computer algorithms able to produce content and interact with other users on social media autonomously, trying to emulate and possibly influence humans' behavior. Indeed, bots are largely employed for malicious purposes, like spreading disinformation and conditioning electoral campaigns. Nowadays, bots' capability of emulating human behaviors has become increasingly sophisticated, making their detection harder. In this paper, we aim at recognizing bot-driven accounts by evaluating the consistency of users' writing style over time. In particular, we leverage the intuition that while bots compose posts according to fairly deterministic processes, humans are influenced by subjective factors (e.g., emotions) that can alter their writing style. To verify this assumption, by using stylistic consistency indicators, we characterize the writing style of more than 12,000 among bot-driven and human-operated Twitter accounts and find that statistically significant differences can be observed between the different types of users. Thus, we evaluate the effectiveness of different machine learning (ML) algorithms based on stylistic consistency features in discerning between human-operated and bot-driven Twitter accounts and show that the experimented ML algorithms can achieve high performance (i.e., F-measure values up to 98%) in social bot detection tasks.

关键词： Social bot Stylometry Bot detection

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Community Detection and Analysis in Air Pollution Complex Network Based on LPA 30

Community Detection and Analysis in Air Pollution Complex Ne...

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30th International Conference on computer communications and networks (ICCCN)

作者： Lian, Jie Ding, Xin Pan, Jianguo Shanghai Normal Univ Shanghai Engn Res Ctr Intelligent Educ & Bigdata Shanghai 200234 Peoples R China

ISBN: (纸本)9780738113302

Air pollution has caused a bad effect on the natural environment and public health. With the rapid establishment of air monitoring stations and meteorological stations, the volume of air pollution data is increasing dramatically. The traditional methods to model the air pollution transmission process have shown a limitation to process a large amount of non-linear data. Machine learning methods have recently been proven to be extremely efficient and accurate to process data in complex structures. In this paper, a novel method based on a complex network is proposed to discover the air monitoring station communities and analyze the air pollutants diffusion. The method comprises several aspects, including a dimension reduction process, a complex network generation process, and a community detection process. The proposed method was verified based on a real-world air pollution dataset, and the results demonstrate that the air pollutants diffusion has a community structure, and some critical air monitoring stations have a greater influence on the transmission of pollutants, which should be paid more attention to the following air quality management.

关键词： air pollutants complex network community detection label propagation algorithm

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Co-governed Space-Terrestrial Integrated Network Architecture and Prototype Based on MIN 30

Co-governed Space-Terrestrial Integrated Network Architectur...

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30th International Conference on computer communications and networks (ICCCN)

作者： Wei, Guohua Li, Hui Bai, Yongjie Yang, Xin Zhang, Huayu Que, JianMing Li, Wenjun Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China Foshan Saisichan Technol Co Ltd Foshan Peoples R China Future Network Ctr Purple Mt Labs Nanjing Peoples R China Network Commun Ctr Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9780738113302

In this paper, we propose a Space-Terrestrial Integrated Multi-identifier Network (STI-MIN), which has the characteristic of efficient routing scheme, co-governed network space, and endogenous security. The fundamental theory is proposed to depict the overview of STI-MIN firstly. Then a multi-identifier management system based on hierarchical consortium blockchain and a scalable space-terrestrial integrated routing scheme are designed to build the efficient management plane and data plane of STI-MIN respectively. The endogenous security mechanism of STI-MIN is also proposed based on trusted computing and identity self-authentication. A testbed covering six provinces or regions of China and a high throughput satellite has been built. The experiment done in the testbed shows that STI-MIN has many merits while comparing it with the traditional network.

关键词： multi-identifier network space-terrestrial integrated network blockchain endogenous security prototype system

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Improvement of Bi-directional communications using Solar Powered Reconfigurable Intelligent Surfaces 30

Improvement of Bi-directional Communications using Solar Pow...

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30th International Conference on computer communications and networks (ICCCN)

作者： Almasoud, Abdullah Selim, Mohamed Y. Alsharoa, Ahmad Kamal, Ahmed E. Prince Sattam Bin Abdulaziz Univ Coll Engn Dept Elect Engn Al Kharj 16273 Saudi Arabia Iowa State Univ Ames IA USA Missouri Univ Sci & Technol Rolla MO 65409 USA

ISBN: (纸本)9780738113302

Recently, there has been a flurry of research on the use of Reconfigurable Intelligent Surfaces (RIS) in wireless networks to create dynamic radio environments. In this paper, we investigate the use of an RIS panel to improve bi-directional communications. Assuming that the RIS will be located on the facade of a building, we propose to connect it to a solar panel that harvests energy to he used to power the RIS panel's smart controller and reflecting elements. Therefore, we present a novel framework to optimally decide the transmit power of each user and the number of elements that will be used to reflect the signal of any two communicating pair in the system (user-user or base station-user). An optimization problem is formulated to jointly minimize a scalarized function of the energy of the communicating pair and the RIS panel and to find the optimal number of reflecting elements used by each user. Although the formulated problem is a mixed-integer nonlinear problem, the optimal solution is found by linearizing the non-linear constraints. Besides, a more efficient close to the optimal solution is found using Bender decomposition. Simulation results show that the proposed model is capable of delivering the minimum rate of each user even if line-of-sight communication is not achievable.

关键词： Reconligurable Intelligent Surfaces Two-way communications Optimization Bender Decomposition

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