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RFClock: Timing, Phase and Frequency Synchronization for distributed Wireless Networks 21

RFClock: Timing, Phase and Frequency Synchronization for Dis...

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27th ACM Annual international conference on Mobile computing and networking (MobiCom)

作者： Alemdar, Kubra Varshney, Divashree Mohanti, Subhramoy Muncuk, Ufuk Chowdhury, Kaushik Northeastern Univ Inst Wireless Internet Things Boston MA 02115 USA

ISBN: (纸本)9781450383424

Emerging applications like distributed coordinated beamforming (DCB), intelligent reflector arrays, and networked robotic devices will transform wireless applications. However, for systems-centric work on these topics, the research community must first overcome the hurdle of implementing fine-grained, over-the-air timing synchronization, which is critical for any coordinated operation. To address this gap, this paper presents an open-source design and implementation of 'RFClock' that provides timing, frequency and phase synchronization for software defined radios (SDRs). It shows how RFClock can be used for a practical, 5-node DCB application without modifying existing physical/link layer protocols. By utilizing a leader-follower architecture, RFClock-leader allows follower clocks to synchronize with mean offset under 0.107Hz, and then corrects the time/phase alignment to be within a 5ns deviation. RFClock is designed to operate in generalized environments: as standalone unit, it generates a 10MHz/1PPS signal reference suitable for most commercial-off-the-shelf (COTS) SDRs today;it does not require custom protocol-specific headers or messaging;and it is robust to interference through a frequency-agile operation. Using RFClock for DCB, we verify significant increase in channel gain and low BER in a range of [ 0 - 10(-3)] for different modulation schemes. We also demonstrate performance that is similar to a popular wired solution and significant improvement over a GPS-based solution, while delivering this functionality at a fractional price/power point.

关键词： Synchronization

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Graph State Distribution: Integer Formulation 2

Graph State Distribution: Integer Formulation

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IEEE international conference on Quantum computing and Engineering (QCE) / Quantum Week

作者： Xie, Wenbo Dai, Wenhan Towsley, Don Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA MIT Quantum Photon Lab Cambridge MA 02139 USA Univ Massachusetts Coll Informat & Comp Sci Amherst MA 01003 USA

ISBN: (纸本)9781665416917

Graph states are a particular class of entangled quantum states, that play essential roles in distributed quantum computing and quantum communication. In both cases, graph states are usually used as resources for a specific set of quantum computers to share information. Therefore, distributing a graph state across a quantum network is an important problem. Currently, we do not have an efficient method for generating Einstein-Podolsky-Rosen(EPR) pairs on demand. Hence EPR pairs needed to distribute graph states are critical resources, and it is important to design distribution algorithms that reduce the need for such resources. Meignant et al. [1] presented the edge-decorated complete graph algorithm that uses n(n - 1)/2 number of EPR pairs to distribute the given graph state, where n is number of node in the network. Fischer et al [2] proposed the Graph State Transfer (GST) algorithm, which consumes fewer EPR pairs. However, GST requires an unbounded amount of memory at each node and preparation of the entire graph state in a single network node. In this poster, we present an integer programming formulation that removes these two assumptions. The model accounts for both the topology of the quantum network and memory constraints at each network node. Meanwhile, we propose a greedy algorithm to solve the problem.

关键词： Quantum Quantum networking Optimization

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A Review on Data Offloading Paradigms in Internet of Things (IoT)

A Review on Data Offloading Paradigms in Internet of Things ...

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Advances in computing, Communication Control and networking (ICAC3N), international conference on

作者： Nidhi Jitender Kumar Dept. of Computer Engineering DCRUST Murthal Sonepat India

With the advent of new technologies in modern era, Internet of Things (IoT) is one of the technologies that have gained tremendous popularity by establishing connection with smart devices and bringing them closer to users. But it is not a piece of cake that can be easily accessed by everybody. It has various challenges associated with it like traffic congestion, battery exploitation, loss of bandwidth, delay in latency and many others. Various studies have been conducted to overcome these challenges. One of the most common techniques is Data Offloading. It is the transfer of certain tasks to rich devices to perform execution of tasks with minimum delay. In addition to this, when server reaches the maximum capacity of tasks execution, extra tasks are distributed to sensor devices relatively called as ‘Load Balancing’.The paper provides systematic literature review (SLR) on various data offloading approaches conducted in recent years. Besides this, it also discusses criteria and infrastructure requirements for offloading of data.

关键词： Performance evaluation Systematics distributed databases Delays Internet of Things Servers Task analysis

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OMN-IBUS Type of networking System for the Communication System in 6G

OMN-IBUS Type of Networking System for the Communication Sys...

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Advance computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： A. Sangeetha Devi Deepanshu Bhatt Saloni Bansal A Keerthika T. Mohanraj S. Santhosh Department of Science and Humanities (Mathematics) Nehru Institute of Engineering and Technology T.M.Palayam Tamil Nadu India Department of Mechanical Engineering IES Institute of Technology and Management IES University Bhopal M.P India Department of Computer Engineering and Applications GLA University Mathura Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College TN Department of Computer Science Engineering (CSE) Karpagam Academy of Higher Education TN Department of Electronic and Communication Engineering Einstein College of Engineering Tamil nadu

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

The scientific community and industry have been working together in recent years to shift computational resources off centralized cloud systems to decentralized computing, or ‘edge’ computing. This change is a result of the cloud system’s inability to keep up with the growing demands of emerging relationships, which are marked by the spread of time-sensitive applications like sophisticated robotics, augmented reality/virtual the truth technologies, self-driving cars, and vital remote control of Internet of Things applications. Decentralized computing at the border is still in its infancy and lacks a full platform, despite the critical role that it will play in future distributed systems. This article presents OMNIBUS, a novel decentralized edge architecture. This approach uses moving vehicles as resources for processing and storage, enabling the continuous delivery of computational capability to end-devices across many locations. Scalability and flexibility are two characteristics that set OMNIBUS distinct from other edge computing solutions. The suggested method, which is based on mathematical models of prediction, determines computational capacity for particular locations by learning the direction and time of vehicle movements. It also presents a theoretical foundation for learning, improvement, and caching amid dynamic conditions caused by moving vehicles, specifically for sequential concurrent conversion. OMNIBUS’s scalability allows for almost infinite growth, which might result in a significant increase in network speed.

关键词： Cloud computing Scalability Transportation Computer architecture Traffic control Nonlinear dynamical systems Smart grids

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Empowering Smart Homes with Fog computing for IoT Connectivity

Empowering Smart Homes with Fog Computing for IoT Connectivi...

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Decision Aid Sciences and Application (DASA), international conference on

作者： Amjed Abbas Ahmed Ali Hamza Najim Ali Jafer Mahdi Khattab M. Ali Alheeti Nurhizam Safie Mohd Satar Aisha Hassan Abdallah Hashim Faculty of Information Science and Technology Center for Cyber Security Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Imam AIKadhum College (IKC) Baghdad Iraq Department of Computer Technical Engineering Imam Al-Kadhum College (IKC) Al-Diwaniyah Iraq Department of Scientific Affairs AI-Zahraa University for Women Bangi Malaysia Computer Networking Systems Department College of Computer and Information Technology University of Anbar Anbar Iraq Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Malaysia Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Innovative grid technology leverages Information and Communication Technology (ICT) to enhance energy efficiency and mitigate losses. This paper introduces a “novel three-tier hierarchical framework for smart homes within the Internet of Things (IoT)” ecosystem, addressing the current shortage of intelligent solutions that underutilize IoT advantages. Our framework's primary objective is to elevate smart homes to the microgrid level, enabling the seamless integration from the microgrid's renewable distributed energy sources and attaining maximum energy efficiency. In addition to traditional data processing, we propose the application of fog computing in smart homes. Through a rigorous simulation using real smart meter datasets, our study demonstrates that fog computing, mainly when using predictive filtering, may drastically cut down on the amount of data being sent and the load on the home's network. This innovation holds immense potential for revolutionizing energy management and resource allocation in the context of smart homes, thus contributing to a sustainable and efficient future. This research represents a critical step towards harnessing the power of IoT and fog computing in smart grids and homes, offering a promising path towards more intelligent, eco-friendly, and energy-efficient living spaces.

关键词： Renewable energy sources Smart homes Microgrids Data processing Energy efficiency Smart grids Information and communication technology Internet of Things Optimization Edge computing

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Co-training with Progressive Distribution Alignment and Uncertainty-Interactive Relabeling for Semi-Supervised Domain Adaptive Semantic Segmentation

Co-training with Progressive Distribution Alignment and Unce...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ruiguo Yu Yida Wang Xuewei Li Xuzhou Fu Zijian Zhang Yuan Tian Jie Gao College of Intelligence and Computing Tianjin University China Tianjin Key Laboratory of Cognitive Computing and Application China Tianjin Key Laboratory of Advanced Networking China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Self-training is a strong baseline for semi-supervised domain adaptive semantic segmentation. However, it inevitably introduces biased links between features and concepts in the prediction of certain "hard pixels", which may mislead the generalization of models. We consider these hard pixels to come from two aspects: First, the labels are severely imbalanced and distributed across domains and classes, which may lead to features biased towards source domains and majority classes. Second, the naive threshold filtering pseudo-label methods limit the supervision of hard pixels. To address the above problems, we propose a novel co-training framework with progressive distribution alignment and uncertainty-interactive relabeling strategies. More concretely, a progressive distribution alignment strategy is proposed to match distribution across domains while providing additional supervision for tail class pixels. Additionally, an uncertainty-interactive relabeling strategy is proposed to retain more supervisory information for hard pixels and reduce the overall uncertainty of the pseudo-labels. Experiments on two widely-used benchmarks demonstrate the effectiveness of the proposed PDAUR, achieving state-of-the-art results.

关键词： Adaptation models Uncertainty Filtering Semantic segmentation Semantics Tail Signal processing Predictive models Benchmark testing Speech processing

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Task Migration in Volunteer computing Systems

Task Migration in Volunteer Computing Systems

引用

Advances in computing, Communication Control and networking (ICAC3N), international conference on

作者： Ehab Saleh Chandrasekar Shastry Department of Computer Science and Engineering Jain University Bangalore India

Volunteer computing is the use of underutilised computing power donated by volunteers who want to participate in ongoing high-throughput scientific projects. Due to the general simplicity of this concept, this type of computing attracts thousands of volunteers from all over the world. Many of them, however, leave the network, prompting the main server to initiate Task Migration, which involves transferring the remaining job to another volunteer’s device on the network. In this paper, we conduct two experiments in the task migration procedure in peer-to-peer volunteer network. In the first experiment, the server starts the migration procedure when it detects the first available volunteer, whereas in the second one, the server starts the migration after all of the server’s sub-peers have completed their jobs. To ensure the network’s heterogeneity, we select the dataset GWA-T-13 Materna, which contains performance metrics described as trace files of over 1500 VMs from the distributed Materna Data Centers in Dortmund, Germany. In both experiments, we compare the total execution time of the entire task and the maximum execution time that a peer can have to complete its assigned work. The simulation results show that in the second experiment, the main task was completed in less time than in the first experiment.

关键词： Measurement distributed processing Data centers Simulation Computer applications Peer-to-peer computing Servers

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RIFFLE: A distributed Network Operating System with Re-configurability for Multiple Domains

RIFFLE: A Distributed Network Operating System with Re-confi...

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IEEE international Symposium on Parallel and distributed Processing with Applications and IEEE international conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Boyang Zhou Chunming Wu Shuangxi Chen Zhen Li Hong Fan Zhejiang Lab Intelligent Networks Institute Hangzhou China School of Intelligent Science and Technology Hangzhou Institute for Advanced Study UCAS Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Jiaxing Key Laboratory of Industrial Internet Jiaxing China Research Center for Intelligent Technology Standardization Zhejiang Lab Hangzhou China Department of Electrical Engineering Shanghai University of Electric Power Shanghai China

The distributed network control in software-defined networking is greatly challenged by the design demands of evolving a running distributed control plane (DCP), and by the complexity of handling the control state dynamics. In this paper, we propose RIFFLE to address the problem using a distributed network operating system approach. RIFFLE enables the evolvability of control states of DCP deployed on a global scale. RIFFLE handles spatial and temporal dynamics that occur when updating and migrating the distributed control states, which overcomes the critical barrier to the evolvability. It fills the gap by enabling temporal reconfigurability in DCP. Moreover, RIFFLE reduces the complexities of building and maintaining network control services in DCP by enabling componentization and abstracting the underlying network dynamics. We evaluated the prototype RIFFLE through experiments running in PlanetLab. The results show that the prototype scales well for 135 nodes. We also validate that RIFFLE ensures the continuity and low content request delay for the supported the information-centric network supported during cache update periods owing to the enabled evolvability.

关键词： Runtime Roads Decentralized control Information-centric networking Prototypes Ubiquitous computing Dynamic scheduling

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An SGX and Blockchain-Based System for Federated Learning in Classifying COVID-19 Images

An SGX and Blockchain-Based System for Federated Learning in...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Sihang Qin Xianjun Gu Xing Jun Haitao Li Mengqi Huang Weiqi Dai Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China State Grid Wuhan Power Supply Company China

Federated learning, an emerging distributed learning framework, has gained prominence recently. However, two challenges arise when applying federated learning to medical data. The first challenge stems from the inability of model-generating nodes to ensure computational fairness. The second challenge involves the potential leakage of private information through gradient differences. This paper introduces a federated learning system that leverages Software Guard Extensions (SGX) and blockchain technology to enhance the fairness and security of federated learning for analyzing chest imaging data. The modeling of imaging factors for chest radiography data can effectively regulate the data quality of semi-trusted nodes. A blockchain smart contract is employed for data quality-aware proof, and the quality-aware block undergoes a two-stage rewriting process across distinct nodes. A notary membership model achieves notarized cluster consensus for imaging informatics data. The multi-channel layer model quantifies the distribution of computational workload across participating nodes within the medical consortium. Upon successful verification of the enclave command in the aggregation cycle, traceable and packaged signatures are generated to support computational fairness. To ensure robust information security, SGX remote authentication is used between downstream and upstream nodes. The federated learning system, built upon SGX and blockchain, has been implemented using the Fabric federated chain platform. The ResNet model underwent training through cross-sectional federated learning to effectively classify novel coronavirus lung medical image data. Following this, multiple experimental control and security analyses were conducted, revealing that the time loss incurred due to crossing channels is approximately 21.31%, while the time overhead of SGX remains within 5.2%, as allowed by the number of nodes.

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Network link weight optimization based on antisymmetric deep graph networks and reinforcement learning

Network link weight optimization based on antisymmetric deep...

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Next Generation Data-driven Networks (NGDN), international conference on

作者： Yuhan Chen Yingya Guo Department of Computer and Data Science Fuzhou University Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

Route optimization is a key core technology to optimize network traffic distribution, achieve network load balancing, and improve network performance. Traditional distributed networks widely run shortest-path based routing protocols, and the path of traffic is determined by the link weights of the distributed network, so routing optimization methods are usually optimized around the link weights of the network. Heuristics-based link weight optimization methods are widely used. However, heuristic methods rely on manually set rules, which are poorly generalized and cannot adapt well to dynamically changing traffic demands. Compared to heuristics, deep reinforcement learning (DRL) has the advantage of extracting more accurate feature representations in addition to its ability to handle high-dimensional state and action spaces. We propose a network link weight optimization method based on anti-symmetric deep graph networks (A-DGN) and reinforcement learning using a novel GNN framework anti-symmetric deep graph networks, where link weights are adjusted to reduce the network link utilization with the optimization objective of minimizing the maximum link utilization in the network. Experimental results show that the proposed method achieves significant performance improvements in the link weight optimization problem in four real-world network topology scenarios.

关键词： Measurement Network topology Optimization methods Telecommunication traffic Feature extraction Deep reinforcement learning Routing Load management Routing protocols Next generation networking

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