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QSroute : A QoS Aware Routing Scheme for Software Defined networking

QSroute : A QoS Aware Routing Scheme for Software Defined Ne...

引用

international conference on Software Engineering and Computer Systems (ICSECS)

作者： Imran Edzereiq Kamarudin Mohamed Ariff Ameedeen Muhamad Idaham Umar Ong Azlee Zabidi Department of Computer Systems and Networking Faculty of Computing Universiti Malaysia Pahang AL-Sultan Abdullah Pekan Malaysia Department of Software Engineering Faculty of Computing Universiti Malaysia Pahang AL-Sultan Abdullah Pekan Malaysia

the increasing demand for bandwidth-intensive network applications, such as video streaming, multimedia, and Internet of things (IoT) applications, necessitates improved resource management to protect the network without compromising Quality of Service (QoS). Meeting these challenges requires a centralized view of all available network resources. Software Defined networking (SDN), an emerging technology, provides a centralized view and control of network resources. this feature enables administrators to programmatically define and manage network behavior, including routing, making it more flexible and adaptable. this study proposes a QoS-aware routing scheme for SDN that considers available bandwidth, packet delay, and packet loss to determine the optimal routing path. Optimal paths are selected based on meeting predefined threshold criteria. the study concludes by discussing potential directions for future research in this field.

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Enhancing Intellectual Property Protection in Deep Neural Network with Confidential computing 19

Enhancing Intellectual Property Protection in Deep Neural Ne...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Tian, Wensheng Xia, Ruiyan Yan, Zhichao Tang, Panpan Tu, Yonggang Zhang, Lei Nanhu Lab Jiaxing China ShanghaiTech University Shanghai China Office of Government Services and Data Resource Management Jiaxing China

ISBN: (纸本)9798350346558

Deep neural network(DNN) models consume a lot of computational resources in the training process, and DNN models have become an important intellectual property. the methods for DNN models protection are currently mainly based on digital watermarking. the protection technique based on digital watermarking can only passively verify deep neural networks, and cannot restrict the illegal distribution and use of deep learning models. If the deep neural network model is deployed on a public cloud platform, the watermarking-based approach does not prevent malicious administrators from stealing the model. We propose a protection method based on the confidential computing, which divides the deep neural network model into a data preprocessing model and a inference model, we use a confidential computing environment to protect the data preprocessing model, and the inference model can be distributed to authorized users for deployment, even if malicious users get the inference model or train student models through knowledge distillation, they cannot obtain the highly accurate results from their models. It is also experimentally demonstrated that the method is also able to resist knowledge migration from the model using fine-tuning training. © 2022 IEEE.

关键词： Neural network models

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Research on the Application of "Cloud + Edge"Meteorological Blockchain distributed Storage and Network Technology

Research on the Application of "Cloud + Edge"Meteorological ...

引用

IEEE Information Technology and Mechatronics Engineering conference (ITOEC)

作者： Changliang Zhang Cong Guo Yinhai Xie Sichuan Meteorological Observation and Data Centre Chengdu China Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province Chengdu China National Meteorological Information Center Beijing China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

this paper studies a blockchain-based solution for the distributed storage and networking of meteorological data, aiming to address issues in traditional meteorological data storage and transmission, such as single points of failure, poor scalability, inadequate security, and difficulties in data sharing. First, by analyzing the characteristics of meteorological data and the limitations of existing technologies, the feasibility and advantages of applying blockchain and distributed storage technology in the field of meteorological data are explored. the author designs an overall architecture consisting of a meteorological blockchain data collection layer, blockchain network layer, distributed storage layer, and application service layer. the functions and implementation methods of core components, such as the data collection module, blockchain module, distributed storage module, and data access module, are elaborated in detail. A blockchain-based distributed storage and networking system for meteorological data is constructed. through experiments and result analysis, the proposed solution is shown to improve data processing efficiency by over 30%, while ensuring data security and access speed, greatly enhancing the meteorological digital infrastructure's support capability for forecasting and disaster prevention and mitigation services.

关键词： Sharding Prevention and mitigation Data security Scalability Disasters Memory Blockchains Peer-to-peer computing Maintenance Data communication

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Parallel Computation of Random Walks on distributed Graphs

Parallel Computation of Random Walks on Distributed Graphs

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Big Data, Cloud computing, Data Science & Engineering (BCD)

作者： Yao Hu Research Institute for Digital Media and Content Hiyoshi Campus Keio University Yokohama Japan

A random walk is a process in which a random walker takes consecutive steps in space at equal intervals of time, with the length and direction of each step determined independently. Models related to random walks have been applied in diverse areas such as prediction, recommendation algorithms, and more recently, supervised learning and network analysis. Notably, there is a shortage of parallel computing models for random walks on distributed graphs across various scenarios. therefore, the objective of this work is to offer a versatile model for random walks on distributed graphs, covering network principles and algorithmic models, followed by their parallel implementations. Evaluation results demonstrate the remarkable efficiency of our network model and highlight the future prospects of parallel random walks.

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Robust Spatio-Temporal Trajectory Modeling Based on Auto-Gated Recurrent Unit 19

Robust Spatio-Temporal Trajectory Modeling Based on Auto-Gat...

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作者： Jia, Jia Li, Xiaoyong Li, Ximing Li, Linghui Yuan, Jie Wang, Hongmiao Gao, Yali Qiu, Pengfei Tang, Jialu Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing China

ISBN: (纸本)9798350346558

With the huge amount of crowd mobility data generated by the explosion of mobile devices, deep neural networks (DNNs) are applied to trajectory data mining and modeling, which make great progresses in those scenarios. However, recent studies have demonstrated that DNNs are highly vulnerable to adversarial examples which are crafted by adding subtle, imperceptible noise to normal examples, and leading to the wrong prediction with high confidence. To improve the robustness of modeling spatiotemporal trajectories via DNNs, we propose a collaborative learning model named 'Auto-GRU', which consists of an autoencoder-based self-representation network (SRN) for robust trajectory feature learning and gated recurrent unit (GRU)-based classification network which shares information with SRN for collaborative learning and strictly defending adversarial examples. Our proposed method performs well in defending both white and black box attacks, especially in black-box attacks, where the performance outperforms state-of-the-art methods. Moreover, extensive experiments on Geolife and Beijing taxi traces datasets demonstrate that the proposed model can improve the robustness against adversarial examples without a significant performance penalty on clean examples. © 2022 IEEE.

关键词： Deep neural networks

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LSTM-based distributed Photovoltaic Output Interval Prediction Method for Source-load Collaboration

LSTM-based Distributed Photovoltaic Output Interval Predicti...

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6th international conference on Intelligent computing and Signal Processing (ICSP)

作者： Bo Li Zhan Shi Zhentian Wu Zhihua Yang Communication Management Department Guangdong Power Grid Co. Ltd. Power Dispatching Control Center Guangdong China Science Innovation Department Guangdong Electric Power Communication Technology Co. Ltd. Guangdong China

With the continuous development of distributed energy, photovoltaic (PV) power emerges as a significant construction to tradeoff the balance of power supply and demand. Since the prediction of PV output is unavoidable, interval prediction becomes a powerful method to describe the output curve accurately. To this end, this paper proposes a long short term memory (LSTM)-based distributed PV output interval prediction method for source-load collaboration. It can correct the prediction error according to the spatiotemporal characteristic of PV power stations, and then calculates the interval prediction results under different confidence levels. Simulation results show that the proposed algorithm has the superior performance in the average width of the forecast interval, and the coverage width.

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Trusted computing and Privacy Protection of Computer Blockchain IoT Nodes Based on Fuzzy Set theory Analysis

Trusted Computing and Privacy Protection of Computer Blockch...

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international conference on Information Systems Engineering (ICISE)

作者： Fan Zhang School of Computer Science Wuhan Dong hu University Wuhan China

By utilizing trusted computing and blockchain, a secure and dependable operating environment and management mechanism can be ensured in the Power Distribution IoT. the paper develops a distributed decision-making and collaborative autonomy model for the power distribution IoT based on fuzzy mathematics set theory's comprehensive evaluation algorithm and blockchain. this model enables the power distribution IoT to be controlled and managed, leading to a comprehensive improvement in the network's information security defense level.

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AIC-based distributed Photovoltaic Data Compression Sensing Method

AIC-based Distributed Photovoltaic Data Compression Sensing ...

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6th international conference on Intelligent computing and Signal Processing (ICSP)

作者： Zhan Shi Bo Li Zhihua Yang Xiuzhu Wang Communication Management Department Guangdong Power Grid Co. Ltd. Power Dispatching Control Center China Science Innovation Department Guangdong Electric Power Communication Technology Co. Ltd. Guangdong China

In view of the high-speed data transmission requirements of the new generation of power line carrier communication in distributed photovoltaic information access, data compression sensing is required to improve. In this paper, we study an AIC-based distributed photovoltaic data compression sensing method. Based on the adaptive characteristics of K-singular value decomposition (K-SVD) algorithm, the data sparsity is dynamically estimated to realize the adaptive dynamic adjustment of data acquisition. AIC algorithm is used to reconstruct data, which greatly improves the accuracy of data reconstruction. Information aggregation based on compressed sensing is constructed to achieve high efficiency data aggregation under the condition of low sampling rate. Simulation results show the superior performance of the proposed algorithm in the data transmission volume and energy consumption.

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Greedy networking in Cryptocurrency Blockchain 37th

Greedy Networking in Cryptocurrency Blockchain

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37th IFIP TC 11 international conference on ICT Systems Security and Privacy Protection (IFIP SEC)

作者： Wuthier, Simeon Chandramouli, Pranav Zhou, Xiaobo Chang, Sang-Yoon Univ Colorado Dept Comp Sci Colorado Springs CO 80907 USA

ISBN: (纸本)9783031069758;9783031069741

Proof of work (PoW) is a widely adopted distributed consensus protocol which enables cryptocurrency transaction processing without a trusted third party. the miners are financially incentivized to participate in the PoW consensus protocol, and PoWrelies on the underlying peer-to-peer (P2P) networking for receiving and transmitting the transactions and the up-to-date blocks (which are the inputs for the PoW consensus protocol). We study the rational miner strategy but control an orthogonal parameter from those in the previous blockchain research, which has studied the control of the mining power or the timing of the block submissions (e.g., selfish mining or block withholding). More specifically, we study greedy networking, in which a miner node increases its connectivity beyond the default protocol to expedite the deliveries of blocks and transactions for an unfair mining advantage. While greedy networking has been actively studied in the general P2P networking, it has not been systematically studied in cryptocurrency and blockchain despite the alleged real-world instances by the Bitcoin community. We build an analytical framework for greedy networking and study how the networking control impacts the cryptocurrency application to quantify the benefits and costs of the networking control. To demonstrate the use of our framework, we implement a greedy networking prototype based on an active Bitcoin node connected to the Mainnet while simulating different miner capabilities by varying the computing backend of the miner. In contrast to the previous belief in blockchain and cryptocurrency, we discover that the optimal number of connections is bounded (limiting the greedy behavior) and depends on the hash computing capability of the miner.

关键词： Blockchain Bitcoin Peer-to-peer networking Proof of Work (pow) Mining Rational strategy Greedy networking

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Managed ELK deployments at the Edge with OpenStack and IoTronic: an italian Smart City case study 8

Managed ELK deployments at the Edge with OpenStack and IoTro...

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8th IEEE international conference on Smart computing (SMARTCOMP)

作者： Benomar, Zakaria D'Agati, Luca Longo, Francesco Merlino, Giovanni Puliafito, Antonio Univ Messina Dept Engn Messina Italy CINI Natl Interuniv Consortium Informat Rome Italy

ISBN: (纸本)9781665481526

the huge growth in terms of the number of distributed devices connected to the Internet, the so-called Internet of things (IoT), has led to the expansion of their domain of applicability by enabling advanced and futuristic environments such as the Smart City-like scenarios. In such a use case, a pressing need emerges to cope with the complexity and heterogeneity of the infrastructure by reusing as much tooling as possible without resorting to vertical ad-hoc solutions. Furthermore, having good decision support based on the colossal amount of data (i.e., Big Data) generated by these environments is absolutely critical to make them smart. In fact, the impediment to the smart city concept is to provide efficient aiding decisions whilst using a malleable solutions to meet the information technology evolution. In this context, we introduce in this paper our system for IoT infrastructure management and Big Data processing. In particular, our IoT infrastructure manager middleware Stack4things (S4T) is integrated with the Elastic Stack (ELK) data management solution. this work thus describes the rationale, efforts, and results so far achieved, for an integration of the IoT paradigm with a Cloud-oriented environment focusing on a Smart City scenario, and featuring data collection and visualization as example use cases of such integration.

关键词： Smart Cities Cloud computing Edge computing Internet of things Elastic Stack

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