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An Opportunistic Network Routing Protocol for Maritime Voluntary Observation Ships

Procedia computer Science 2022年 202卷 390-393页

作者： Meiqi Ji Xuerong Cui Juan Li Hao Zhang Shibao Li Jianhang Liu College of Oceanography and Space Informatics China University of Petroleum(East China) Qingdao 266580 China College of Computer Science and Technology China University of Petroleum(East China) Qingdao 266580 China Department of Electrical Engineering Ocean University of China Qingdao 266100 China

In order to solve the issue of data transmission for maritime voluntary observation ships, an opportunistic network routing protocol (MVOS-RP) is proposed for the complicated maritime environment. The proposed protocol transmits data through the encounter between ships. A connected graph is constructed with the sending node as the vertex, and then the selection of the relay node is divided into three scenes by traversing the graph through the Breadth First Search (BFS) algorithm. We also use the ship’s Automatic identification System (AIS) information, such as the estimated time of arrival and the route of ships, to select the appropriate relay node. MVOS-RP protocol solves the transmission issue of opportunistic network in the environment of sparse nodes and poor communication link quality. Simulation is performed on Opportunistic Network Environment (ONE) platform. The results show that, compared with other protocols, the proposed protocol has a better balance in terms of data delivery rate, average latency and routing overhead. It is a reliable and efficient opportunistic network routing protocol.

关键词： Opportunistic network Routing protocol AIS

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What improves the generalization of graph transformers? a theoretical dive into the self-attention and positional encoding 24

What improves the generalization of graph transformers? a th...

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Proceedings of the 41st International Conference on Machine Learning

作者： Hongkang Li Meng Wang Tengfei Ma Sijia Liu Zaixi Zhang Pin-Yu Chen Department of Electrical Computer and System Engineering Rensselaer Polytechnic Institute Troy NY Department of Biomedical Informatics Stony Brook University Stony Brook NY Department of Computer Science and Engineering Michigan State University East Lansing MI and MIT-IBM Watson AI Lab IBM Research MA Department of Computer Science and Engineering University of Science and Technology of China Hefei Anhui China IBM Thomas J. Watson Research Center Yorktown Heights NY

Graph Transformers, which incorporate self-attention and positional encoding, have recently emerged as a powerful architecture for various graph learning tasks. Despite their impressive performance, the complex non-convex interactions across layers and the recursive graph structure have made it challenging to establish a theoretical foundation for learning and generalization. This study introduces the first theoretical investigation of a shallow Graph Transformer for semi-supervised node classification, comprising a self-attention layer with relative positional encoding and a two-layer perceptron. Focusing on a graph data model with discriminative nodes that determine node labels and non-discriminative nodes that are class-irrelevant, we characterize the sample complexity required to achieve a desirable generalization error by training with stochastic gradient descent (SGD). This paper provides the quantitative characterization of the sample complexity and number of iterations for convergence dependent on the fraction of discriminative nodes, the dominant patterns, and the initial model errors. Furthermore, we demonstrate that self-attention and positional encoding enhance generalization by making the attention map sparse and promoting the core neighborhood during training, which explains the superior feature representation of Graph Transformers. Our theoretical results are supported by empirical experiments on synthetic and real-world benchmarks.

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Diffusion Models as Network Optimizers: Explorations and Analysis

arXiv

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arXiv 2024年

作者： Liang, Ruihuai Yang, Bo Chen, Pengyu Li, Xianjin Xue, Yifan Yu, Zhiwen Cao, Xuelin Zhang, Yan Debbah, Mérouane Poor, H. Vincent Yuen, Chau School of Computer Science Northwestern Polytechnical University Shaanxi Xi’an710129 China School of Cyber Engineering Xidian University Shaanxi Xi’an710071 China Department of Informatics University of Oslo Oslo0316 Norway KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States School of Electrical and Electronics Engineering Nanyang Technological University Singapore

Network optimization is a fundamental challenge in the Internet of Things (IoT) network, often characterized by complex features that make it difficult to solve these problems. Recently, generative diffusion models (GDMs) have emerged as a promising new approach to network optimization, with the potential to directly address these optimization problems. However, the application of GDMs in this field is still in its early stages, and there is a noticeable lack of theoretical research and empirical findings. In this study, we first explore the intrinsic characteristics of generative models. Next, we provide a concise theoretical proof and intuitive demonstration of the advantages of generative models over discriminative models in network optimization. Based on this exploration, we implement GDMs as optimizers aimed at learning high-quality solution distributions for given inputs, sampling from these distributions during inference to approximate or achieve optimal solutions. Specifically, we utilize denoising diffusion probabilistic models (DDPMs) and employ a classifier-free guidance mechanism to manage conditional guidance based on input parameters. We conduct extensive experiments across three challenging network optimization problems. By investigating various model configurations and the principles of GDMs as optimizers, we demonstrate the ability to overcome prediction errors and validate the convergence of generated solutions to optimal solutions. We provide code and data at https://***/qiyu3816/DiffSG. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Observation of Su-Schrieffer-Heeger Topological Model Band Structure in Integrated LNOI Coupled Ring Cavities 16

Observation of Su-Schrieffer-Heeger Topological Model Band S...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Dinh, Hiep X. Balcytis, Armandas Ozawa, Tomoki Ota, Yasutomo Baba, Toshihiko Iwamoto, Satoshi Mitchell, Arnan Nguyen, Thach G. Rmit University Integrated Photonics and Applications Centre MelbourneVIC3000 Australia Advanced Institute for Materials Research Tohoku University Sendai980-8577 Japan Keio University Department of Applied Physics and Physico-Informatics Yokohama223-8522 Japan Yokohama National University Department of Electrical and Computer Engineering Yokohama240-8501 Japan The University of Tokyo Research Center for Advanced Science and Technology Tokyo153-8904 Japan

We report synthetic frequency dimension Su-Schrieffer-Heeger model and its band structure observation using coupled ring cavities on an integrated photonic chip. Intra-cell and inter-cell couplings between hybridized ... 详细信息

ISBN: (纸本)9798350372076

关键词： Photonic integrated circuits

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Multi-tenant cross-slice resource orchestration: A deep reinforcement learning approach

arXiv

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arXiv 2018年

作者： Chen, Xianfu Zhao, Zhifeng Wu, Celimuge Bennis, Mehdi Liu, Hang Ji, Yusheng Zhang, Honggang VTT Technical Research Centre of Finland Oulu Finland Zhejiang Lab College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Graduate School of Informatics and Engineering University of Electro-Communications Tokyo Japan Centre for Wireless Communications University of Oulu Finland Department of Electrical Engineering and Computer Science Catholic University of America United States Information Systems Architecture Research Division National Institute of Informatics Tokyo Japan College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

With the cellular networks becoming increasingly agile, a major challenge lies in how to support diverse services for mobile users (MUs) over a common physical network infrastructure. Network slicing is a promising solution to tailor the network to match such service requests. This paper considers a system with radio access network (RAN)-only slicing, where the physical infrastructure is split into slices providing computation and communication functionalities. A limited number of channels are auctioned across scheduling slots to MUs of multiple service providers (SPs) (i.e., the tenants). Each SP behaves selfishly to maximize the expected long-term payoff from the competition with other SPs for the orchestration of channels, which provides its MUs with the opportunities to access the computation and communication slices. This problem is modelled as a stochastic game, in which the decision makings of a SP depend on the global network dynamics as well as the joint control policy of all SPs. To approximate the Nash equilibrium solutions, we first construct an abstract stochastic game with the local conjectures of channel auction among the SPs. We then linearly decompose the per-SP Markov decision process to simplify the decision makings at a SP and derive an online scheme based on deep reinforcement learning to approach the optimal abstract control policies. Numerical experiments show significant performance gains from our scheme. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

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An out-of-core dataflow middleware to reduce the cost of large scale iterative solvers

An out-of-core dataflow middleware to reduce the cost of lar...

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41st International Conference on Parallel Processing Workshops, ICPPW 2012

作者： Zhou, Zheng Saule, Erik Aktulga, Hasan Metin Yang, Chao Ng, Esmond G. Maris, Pieter Vary, James P. Çatalyürek, Ümit V. Dept. of Biomedical Informatics Ohio State University Columbus OH 43210 United States Dept. of Electrical and Computer Engineering Ohio State University Columbus OH 43210 United States Wuhan University China Computational Research Division Lawrence Berkeley National Lab. Berkeley CA 94720 United States Department of Physics and Astronomy Iowa State University Ames IA 50011 United States

ISBN: (纸本)9780769547954

The emergence of high performance computing (HPC) platforms equipped with solid state drives (SSD) presents an opportunity to dramatically increase the efficiency of out-of-core numerical linear algebra computations. In this paper, we explore the advantages and challenges associated with performing sparse matrix vector multiplications (SpMV) on a small SSD test bed. Such an endeavor requires programming abstractions that ease implementation, while enabling an efficient usage of the resources in the test bed. For this purpose, we adopt a task-based out-of-core programming model on top of a dataflow middleware based on the filter stream programming model. We compare the performance of the resulting out-of-core iterated SpMV procedure running on the SSD test bed to the performance of an in-core implementation on a multi-core cluster for solving large-scale eigen value problems. Preliminary experiments indicate that the out-of-core implementation on the SSD test bed can compete with an in-core implementation in terms of the total CPU-hour cost. We conclude with some architectural design suggestions that can enable numerical linear algebra computations in general to be carried out with high efficiency on SSD-equipped platforms. © 2012 IEEE.

关键词： Middleware

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Application of IoT-LoRa Technology and Design in irrigation canals to improve the quality of agricultural products in Batu Indonesia 2

Application of IoT-LoRa Technology and Design in irrigation ...

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2nd International Conference on Smart Cities, Automation and Intelligent Computing Systems, ICON-SONICS 2021

作者： Adi, Puput Dani Prasetyo Prasetya, Dwi Arman Arifuddin, Rahman Sari, Anggraini Puspita Mukti, Fransiska Sisilia Sihombing, Volvo Anjar, Agus Sagala, Elviana Junita Yanris, Gomal Juni University of Merdeka Malang Department of Electrical Engineering East Java Malang City Indonesia Institut Teknologi Dan Bisnis Asia Department of Informatic Technology and Design Faculty Malang Indonesia University of Labuhan Department of Computer Sciences Batu Indonesia University of Labuhan Faculty of Teacher Training and Education Batu Indonesia University of Labuhan Faculty of Law Batu Indonesia University of Labuhan Department of Informatics Management Sains and Technology Batu Indonesia

ISBN: (纸本)9781665449922

One factor that leads to excellence in terms of plant quality is how to treat plants well by giving the proper water, neither abundantly nor lacking. The weakness of farmers is their lack of routine in watering plants, coupled with lousy weather factors that will damage plants and the type of water used for watering plants. Monitoring is carried out on agricultural land in watersheds utilizing an irrigation system. This study monitors and conducts water pH, soil pH, and soil moisture using an Arduino Atmega 328p microcontroller with LoRa 915 MHz. Internet-based automation was carried out on plant monitoring using soil pH sensors, water pH, and soil moisture. The device used is Long Range Radio Frequency 915 MHz. One of the plants used for the experiment is Pakcoy. Water is distinguished by two containers that have been integrated with LoRa 915 MHz, and the water is ensured to have an average pH that is flowed to agricultural land automatically. The data is displayed in real-time in the application Server the Thingspeak IoT. © 2021 IEEE.

关键词： Internet of things

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Transactions on Rough Sets XII 1

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丛书名： Lecture Notes in computer Science

1000年

作者： James F. Peters Andrzej Skowron Roman Słowiński Pawan Lingras Duoqian Miao Shusaku Tsumoto

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Detection of paroxysmal atrial fibrillation using attention-based bidirectional recurrent neural networks

arXiv

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arXiv 2018年

作者： Shashikumar, Supreeth P. Shah, Amit J. Clifford, Gari D. Nemati, Shamim School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Department of Epidemiology Rollins School of Public Health Emory University AtlantaGA United States Department of Biomedical Informatics Emory University Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States Department of Biomedical Informatics Emory University AtlantaGA United States

Detection of atrial fibrillation (AF), a type of cardiac arrhythmia, is difficult since many cases of AF are usually clinically silent and undiagnosed. In particular paroxysmal AF is a form of AF that occurs occasionally, and has a higher probability of being undetected. In this work, we present an attention based deep learning framework for detection of paroxysmal AF episodes from a sequence of windows. Time-frequency representation of 30 seconds recording windows, over a 10 minute data segment, are fed sequentially into a deep convolutional neural network for image-based feature extraction, which are then presented to a bidirectional recurrent neural network with an attention layer for AF detection. To demonstrate the effectiveness of the proposed framework for transient AF detection, we use a database of 24 hour Holter Electrocardiogram (ECG) recordings acquired from 2850 patients at the University of Virginia heart station. The algorithm achieves an AUC of 0.94 on the testing set, which exceeds the performance of baseline models. We also demonstrate the cross-domain generalizablity of the approach by adapting the learned model parameters from one recording modality (ECG) to another (photoplethysmogram) with improved AF detection performance. The proposed high accuracy, low false alarm algorithm for detecting paroxysmal AF has potential applications in long-term monitoring using wearable sensors. Copyright © 2018, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Performance evaluation of network header compression schemes for UDP, RTP and TCP

Periodica polytechnica Electrical engineering and computer s...

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Periodica polytechnica electrical engineering and computer science 2016年第3期60卷 151-162页

作者： Tömösközi, Máté Seeling, Patrick Ekler, Péter Fitzek, Frank H. P. Acticom GmbH Berlin Germany Department of Electronic Systems Aalborg University Denmark Communication Networks Group Technische Universität Dresden Germany Department of Computer Science Central Michigan University Mount PleasantMI48859 United States Department of Automation and Applied Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Hungary

Modern cellular networks utilising the Long-Term Evolution (LTE) set of standards face an ever-increasing demand for mobile data from connected devices. Header compression is employed to minimise the overhead for IP-based cellular network traffic. In this paper, we evaluate the three header compression implementations used by such networks with respect to their potential throughput increase and complexity for different mobile service scenarios. We compare RTP, UDP and TCP profile compressions regarding their compression gain and complexity. Specifically, we consider header compression as defined by (i) IP Header Compression (RFC 2507), (ii) Robust Header Compression version 1 (RFC 3095), and (iii) the recently updated Robust Header Compression version 2 (RFC 5225) with TCP/IP profile (RFC 6846).This paper presents the performance evaluation of these header compression schemes for UDP, RTP and TCP, for both IPv4 and IPv6 streams in error-free and error-prone scenarios. A comparison between the Robust Header Compression methods and IP Header Compression is also provided. Our results show that all implementations have great potential for saving bandwidth in IP-based wireless networks, even under varying channel conditions. We also present for the first time an analysis of certain RTP header fields which, depending on the transmission characteristics, could have high impact on the overall compression gain. © 2016, Technical University of Budapest. All rights reserved.

关键词： Bandwidth

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