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IEEE International Conference on Blockchain and Cryptocurrency (ICBC)

作者： Elsi Ahmadieh Nour El Madhoun Department Communications and Computer Networks Engineering Faculty of Technology Lebanese University Beirut Lebanon LISITE Laboratory ISEP Issy-les-Moulineaux France Sorbonne Université CNRS LIP6 Paris France

Smart contracts are an important key component of blockchain technology. They allow the creation of unique digital assets (such as artwork, real estate, collectibles, etc.) by representing them as NFTs. These NFTs can then be sold and purchased transparently and securely on a decentralized NFT marketplace. In this paper, we develop a smart contracts application that allows the creation of an NFT, which represents an artwork, that can be sold on an NFT marketplace while transferring the ownership from the seller to the buyer.

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MARVELO - A framework for signal processing in wireless acoustic sensor networks 13

MARVELO - A framework for signal processing in wireless acou...

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13th ITG Conference on Speech Communication

作者： Afifi, Haitam Schmalenstroeer, Joerg Ullmann, Joerg Haeb-Umbach, Reinhold Karl, Holger Computer Networks Group Paderborn University Germany Department of Communications Engineering Paderborn University Germany

ISBN: (纸本)9783800747672

Signal processing in Wireless Acoustic Sensor networks (WASN) is based on a software framework for hosting the algorithms as well as on a set of wireless connected devices representing the hardware. Each of the nodes contributes memory, processing power, communication bandwidth and some sensor information for the tasks to be solved on the network. In this paper we present our MARVELO framework for distributed signal processing. It is intended for transforming existing centralized implementations into distributed versions. To this end, the software only needs a block-oriented implementation, which MARVELO picks-up and distributes on the network. Additionally, our sensor node hardware and the audio interfaces responsible for multi-channel recordings are presented. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach

关键词： Sensor nodes

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RLink: Accelerate On-Device Deep Reinforcement Learning with Inference Knowledge at the Edge

RLink: Accelerate On-Device Deep Reinforcement Learning with...

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International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Tianyu Zeng Xiaoxi Zhang Daipeng Feng Jingpu Duan Zhi Zhou Xu Chen School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Institute of Future Networks Southern University of Science and Technology Shenzhen China Department of Communications Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

Deep reinforcement learning (DRL) has been a successful paradigm in machine learning that enables solving complex control problems at the human level. However, the sampling and training efficiency of state-of-the-art DRL frameworks can not satisfy the stringent latency and throughput requirements of today’s mobile environments. Existing distributed and offline reinforcement learning algorithms along with the libraries for training acceleration are inherently designed for DRL tasks performed in the cloud rather than on distributed mobile devices, on which the computing resources are highly constrained, heterogeneous, and possibly dynamically changing. With the rise of edge computing and intelligence services, this paper presents RLink, a novel distributed training library to accelerate on-device deep reinforcement learning with inference knowledge at the edge. We leverage knowledge distillation to realize lightweight interaction between our on-device training task and the remote models that can provide inference knowledge. In this way, RLink is designed to be event-driven and agnostic to heterogeneous deep reinforcement learning algorithms and libraries. To tackle the communication bottleneck, a novel asynchronous sampling algorithm is proposed to facilitate real-time training in RLink. Tuned for unstable-connected mobile devices, RLink is robust and efficient by using a semantic-aware communication pipeline for lossless data compression. Extensive experimental results show that, compared with state-of-the-art algorithms and libraries, RLink can accelerate deep reinforcement learning at the edge with up to decuple speedups in convergence and ideal computational performance.

关键词： Training Performance evaluation Computational modeling Pipelines Deep reinforcement learning Libraries Inference algorithms

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CALCULATION OF RAIN ATTENUATION AT MILLIMETER WAVELENGTHS BY MODELLING REALISTIC NON-ROTATIONAL SYMMETRIC RAIN DROPS DURING TURBULENT WEATHER SITUATIONS 27

CALCULATION OF RAIN ATTENUATION AT MILLIMETER WAVELENGTHS BY...

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27th Ka and Broadband communications Conference and the 39th International communications Satellite Systems Conference, ICSSC 2022

作者： Teschl, Franz Thurai, Merhala Steger, Sophie Schönhuber, Michael Graz University of Technology Institute of Communication Networks and Satellite Communications Graz8010 Austria Department of Electrical and Computer Engineering Colorado State University Fort CollinsCO80523 United States Graz University of Technology Graz8010 Austria JOANNEUM RESEARCH Forschungsgesellschaft MbH Graz8010 Austria

When determining scattering and attenuation of microwaves due to rain, so far, mainly rotationally symmetric rain drops are considered. This is true for remote sensing and communication applications, as weather radar meteorology as well as satellite and terrestrial communication. In turbulent weather situations, however, a significant number of drops exhibit no rotational symmetry. The reason can be found in the turbulent winds and in the collision of drops that can lead to asymmetric modes of drop oscillation. In recent years, therefore, several rain events during tropical depressions and storms were analyzed and the individual drops that were detected during those events with the use of disdrometers were fully reconstructed in 3D. By calculating the scattering parameters of the individual drops with the use of commercial electromagnetic solvers and by reconstructing their distribution, a good agreement between the determined measured polarimetric parameters and actual radar measurements could be archived. Those calculations were carried out at C- and S-band radar frequencies. The present study investigated if the approach is also suitable for the calculation of attenuation for sub-centimeter wavelengths and what influence the asymmetry can have. © 2022 FGM Events LLC. All rights reserved.

关键词： Drops

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GUEST EDITORIAL

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China communications 2023年第3期 4-6页

作者： Shanzhi Chen F.Richard Yu Weisong Shi Changle Li China Information and Communication Technology Group Co. Ltd.(CICT) State Key Laboratory of Wireless Mobile Communications SMIC IEEE VTS Mobile World newsletter IEEE Canadian Academy of Engineering(CAE) Engineering Institute of Canada(EIC) IET Department of Computer and Information Sciences at the University of Delaware(UD) State Key Laboratory of Integrated Services Networks of Xidian University

With the development of new generation of information and communication technology, the Internet of Vehicles(Io V)/Vehicle-to-Everything(V2X), which realizes the connection between vehicle and X(i.e., vehicles, pedestrians, infrastructures, clouds, etc.), is playing an increasingly important role in improving traffic operation efficiency and driving safety as well as enhancing the intelligence level of social traffic services.

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An Optimized Wireless Video Transmission in a Smart Agricultural Solutions

An Optimized Wireless Video Transmission in a Smart Agricult...

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National Conference on Radio Science (NSRC)

作者： Mohamed Naeem Mohamed Aboul-Dahab Computer Networks and Data Centre-Cairo Arab Academy for Science Technology and Maritime Transport Cairo Egypt Department of Electronics and Communications Engineering. Arab Academy for Science Technology and Maritime Transport Cairo Egypt

Smart agriculture requires field sensing, especially video and photo transmissions, to detect severe issues in this field. Although the most suitable communication infrastructure is wireless transmission, the agriculture field suffers from severe diffraction and scattering of the wirelessly propagated waves. This paper proposes an optimization technique for improving wireless transmission in the agricultural environment by utilizing the most appropriate wireless impairment mitigation methods. The proposed technique adopts a joint wireless transmission optimization approach for both the client and the network sides. The amendment in the wireless transmission from the client-side perspective utilized the concurrent load balance over the heterogeneous wireless connections. While the refinement of the heterogeneous wireless network achieved better coverage by minimizing the number of propagating nodes by optimizing the propagating nodes' placement within the agricultural plot. The simulation, experiment, and results proved the effectiveness of the proposed solution for achieving better wireless video transmission performance.

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Fault Tolerance and Cost Effectiveness Analysis of Low-Powered 3D-TESH Network

Fault Tolerance and Cost Effectiveness Analysis of Low-Power...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Faiz Al Faisal M.M. Hafizur Rahman Yasushi Inoguchi Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Department of Computer Networks & Communications CCSIT King Faisal University Saudi Arabia Research Center for Advanced Computing Infrastructure JAIST Nomi Ishikawa Japan

Exa-scale supercomputing is the prime target for the supercomputers. To reach at that point, supercomputers require redesigning the interconnection networks to support high network scalability, suitable network performance, low cost-performance ratio, low power usage, high fault-tolerance, low network congestion and high throughput with low latency. Among the thousand of interconnection networks, one of the most capable one is 3D-TESH, is a hierarchical interconnection network (HIN) interconnected with multiple basic modules (basic modules are basically treated as single on-chip network), where each on-chip network is similar to a 3Dmesh network and off-chip network are built using the 2Dtorus connections. Some static network parameters as well as dynamic communication performance (on-chip level) had been studied earlier for 3DTESH, which ensured the high significance of this network in comparing with others. Moreover, in case of power consumption, it requires about 41% less router power than 5Dtorus at the on-chip level. In this paper, we like to evaluate some other important static parameters like- packing density, fault tolerance, wiring complexity, cost effectiveness factor and etc. Here, we found that 3D-TESH yields much better results than network like - 3Dmesh, 2Dmesh, 2Dtorus, TESH and even the 3Dtorus.

关键词： Wiring Fault tolerance Costs Multiprocessor interconnection Scalability Heuristic algorithms Fault tolerant systems

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Microservice-Based Fog Testbed for 6G Applications

Microservice-Based Fog Testbed for 6G Applications

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International Conference on Advanced Communication Technology (ICACT)

作者： Ekaterina Kuzmina Meriem Tefikova Artem Volkov Ammar Muthanna Abdelhamied A. Ateya Andrey Koucheryavy Department of Telecommunication Networks and Data Transmission Saint-Petersburg State University of Telecommunications Saint Petersburg Russia Department of Electronics and Communications Engineering Zagazig University Zagazig Egypt EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia

This paper provides a real-time fog computing model based on a microservice architecture that enables testing and modeling of eventual implementations of ultra-reliable low-latency communications (uRLLC) services. The work provides fog-based architecture for sixth-generation cellular (6G) applications, including telepresence and uRLLC. A testbed of a robot swarm was developed to prototype the proposed network architecture. Computing tasks are offloaded and handled based on a proposed microservice scheme introduced to meet the 6G requirements. Furthermore, we developed a novel migration scheme for the proposed architecture to support the mobility of end devices. The optimum server for migrating computing tasks is allocated by solving a proposed optimization problem using particle swarm optimization (PSO). All proposed algorithms were implemented in the developed prototype. The proposed work is introduced to provide an architectural foundation for testing fog-based 6G applications and services and to implement and test novel network methods in the future.

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Microcomb-driven photonic convolutional engine

Microcomb-driven photonic convolutional engine

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Bai, Bowen Yang, Qipeng Shu, Haowen Chang, Lin Wu, Yichen Yang, Fenghe Shen, Bitao Tao, Zihan Xie, Weiqiang Hu, Weiwei Bowers, John E. Wang, Xingjun State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Frontiers Science Center for Nano-optoelectronics Peking University Beijing100871 China Zhangjiang Laboratory Shanghai201210 China Peking University Yangtze Delta Institute of Optoelectronics Nantong226010 China

We demonstrate a microcomb-driven chip-based photonic convolutional engine utilizing time-wavelength plane stretching approach. A preeminent photonic-core compute density of 1.04 TOPS/mm2 and a record high weight cont... 详细信息

ISBN: (纸本)9781957171258

关键词： Convolution

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DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset

arXiv

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arXiv 2023年

作者： Liu, Weijie Zhang, Xiaoxi Duan, Jingpu Joe-Wong, Carlee Zhou, Zhi Chen, Xu The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China The Institute of Future Networks Southern University of Science and Technology Shenzhen518055 China The Department of Communications Peng Cheng Laboratory Shenzhen518066 China Department of Electrical and Computer Engineering Carnegie Mellon University CA94035 United States

Federated Learning (FL) is a distributed learning paradigm that can coordinate heterogeneous edge devices to perform model training without sharing private data. While prior works have focused on analyzing FL convergence with respect to hyperparameters like batch size and aggregation frequency, the joint effects of adjusting these parameters on model performance, training time, and resource consumption have been overlooked, especially when facing dynamic data streams and network characteristics. This paper introduces novel analytical models and optimization algorithms that leverage the interplay between batch size and aggregation frequency to navigate the trade-offs among convergence, cost, and completion time for dynamic FL training. We establish a new convergence bound for training error considering heterogeneous datasets across devices and derive closed-form solutions for co-optimized batch size and aggregation frequency that are consistent across all devices. Additionally, we design an efficient algorithm for assigning different batch configurations across devices, improving model accuracy and addressing the heterogeneity of both data and system characteristics. Further, we propose an adaptive control algorithm that dynamically estimates network states, efficiently samples appropriate data batches, and effectively adjusts batch sizes and aggregation frequency on the fly. Extensive experiments demonstrate the superiority of our offline optimal solutions and online adaptive algorithm. Copyright © 2023, The Authors. All rights reserved.

关键词： Edge computing

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