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Aware: Adaptive distributed Training with Computation, Communication and Position Awareness for Deep Learning Model

Aware: Adaptive Distributed Training with Computation, Commu...

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ieee international conference on High Performance computing and Communications (HPCC)

作者： Yan Zeng Guangzheng Yi Yuyu Yin Jiyang Wu Meiting Xue Jilin Zhang Jian Wan Yunquan Zhang School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China School of Cyberspace Hangzhou Dianzi University Hangzhou China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

the accuracy of the neural networks can usually be improved by increasing the size of the dataset and the layers or operators of the network, as it has strong composability. But, it makes a challenge to train these models efficiently, due to the limited resources of a single device, such as memory, computational resource and so on. For this challenge, it has become an inevitable trend to automatically implement model parallelism across multiple devices. this paper proposes Aware, an adaptive distributed parallel training method, to search distributed parallel strategy automatically for deep learning model. It firstly proposes an operator fusion strategy with computation and communication awareness to simplify the computational graph of deep learning model. And then, it introduces position-aware graph embedding algorithm to extract the structural features of models, which can make the searched parallel strategies to transplant to other networks with similar structures. On this basis, we use reinforcement learning algorithm to search distributed parallel strategy automatically. this paper makes experiments with neural networks such as Inception V3, NMT, GNM, NasNet and ResNet, and uses CIFAR10 and PTB datasets to compare the two methods of Hierarchical and Placeto. the results show that compared with Placeto, Aware achieves up to 5% reductions in runtime. Aware achieves more than 8% reductions in runtime compared with Hierarchical. Moreover, it has better transplantation and generalization capabilities, and supports pre-training for large-scale network parallel strategy search and accelerates convergence.

关键词： Deep learning Training Adaptation models Runtime Computational modeling Neural networks Reinforcement learning

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ReSLB: Load Balanced Workflow for distributed Deep Learning Mass Spectrometry Database

ReSLB: Load Balanced Workflow for Distributed Deep Learning ...

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ieee international conference on High Performance computing and Communications (HPCC)

作者： Li Mocheng Liu Yang Ou Yang Chen Zhiguang Xiao Nong Chen Tao Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China School of Computer Sun Yat-sen University Guangzhou China State Key Laboratory of Proteomics Beijing Proteome Research Center National Center for Protein Sciences(Beijing) Beijing Institute of Lifeomics Beijing China

the proteomics data analysis pipeline based on the shotgun method requires efficient data processing methods. the parallel algorithm of mass spectrometry database search faces the problems of rapidly expanding database size but low scalability. the heterogeneous database search algorithm based on deep learning is an effective way to solve this problem. Still, the deep learning-based distributed parallel database search algorithm is lacking. this paper analyzes the database searching computational load using a neural network and designs ReSLB, a workflow for the restricted search of mass spectrometry data based on GPU cluster and neural network scoring algorithm. this work aims to ensure the high scalability of future deep learning-based mass spectrometry distributed databases. In the performance estimation of 256 GPUs, the load imbalance of less than 30% and the parallel efficiency of 60% are achieved. Compared with state-of-the-art, the time cost is reduced by 75%, and the parallel efficiency from 1 to 256 GPUs is 3.6x higher than that.

关键词： Deep learning Scalability Neural networks Data preprocessing distributed databases Graphics processing units Clustering algorithms

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Design and Development of Wireless sensor for Variable Temperature and for Various Security Purposes 4th

Design and Development of Wireless Sensor for Variable Tempe...

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4th international conference on Information and Communication Technology for Intelligent systems, ICTIS 2020

作者： Singh, Prabhakar Saxena, Minal SIRT Bhopal India

ISBN: (纸本)9789811570773

the word sensor simply illustrates the conversion of non-electrical signal, physical or chemical quantity into an electrical signal and the measuring quantity by it is called the measurand. there are many autonomous devices using sensors with cluster of networks and spatially distributed network to provide more facility (Rowayda and Sadek in Future Comput. Inf. J. 3(2):166–177, 2018) (Bosman et al. in Inf. Fusion 33:41–56, 2017) [1, 2]. A routine protocol in WSN is enhanced to hierarchical-based routine protocol because of its energy-saving capability, network scalability, and network topology stabilities (Zhang et al. in J. Softw. Eng. Appl. 03(12):1167–1171, 2010) [3]. this sensor will not pose high SNR as compared to Arduino/DS18B20 temperature sensors and because the use of an optimal dynamic stochastic resonance (SR) processing method is introduced to improve the SNR of the receiving signal under certain conditions, which is not been done in others. SNR = (10 log s/n) in db, where σ =1 or 0.1 or 0.01 depending on various circumstances which is quiet low as compared to other sensor noise ratios. As per result, perspective pulling sensor data from the sensor approximately every 4 s got a lot of errors throughout the day (Cheng and Chang in Expert Syst. Appl. 39(10):9427–9434, 2012) [4]. WSN is emerging as a popular and essential ways of providing pervasive computing environment for numerous applications. there are periods of times where it can go several minutes before getting an error-free value. Similarly, it can also go several hours and not have any errors, so it is very intermittent. the errors range from "no sensors found" to CRC errors. these problems will be removed in this sensor because use of DSR and percentage of error will fall to ~0.001–1% only depending on variable weather, location, and environment. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Topology

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Energy-Efficient distributed Clustering Algorithm for WSNs in Smart Grid

Energy-Efficient Distributed Clustering Algorithm for WSNs i...

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Smart Power & Internet Energy systems (SPIES), international conference on

作者： Zhiyuan Liu Huisan Wang Xiuping Shi Tianqi Li Lei Shi Liang Zhou Jian Luo Xinyang Zhao Nan Cao Yuanchao Huang Xiangnan Du State Grid Ningxia Ultra High Voltage Co. LTD. State Grid Ningxia Electric Power Co. LTD Yinchuan China C-EPRI Electric Power Engineering Co. LTD. Beijing China NARI Group Co. LTD Nanjing China

For the problems of uneven clustering, unreasonable selection of cluster heads and excessive energy consumption in wireless sensor networks for smart grid, an energy efficient clustering algorithm for wireless sensor networks in smart grid (EECSG) is proposed. the algorithm first calculates the optimal number of cluster heads, and then uses k-means algorithm to evenly divide the whole network based on the optimal number of cluster heads. In the selection of cluster head, residual node energy and distance influence factor are introduced to comprehensively select, and finally the data is transmitted stably. the simulation results show that EECSG can dynamically determine the cluster head through the introduction of cluster head selection factor, which reduces the energy loss during data communication in the network and improves the overall life of the wireless sensor network.

关键词： Wireless sensor networks Energy consumption Energy loss Heuristic algorithms Simulation Clustering algorithms Energy efficiency

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Tiny Tech Big Impact: Harnessing Nanotechnology for Cleaner Air

Tiny Tech Big Impact: Harnessing Nanotechnology for Cleaner ...

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international conference on ICT in Business Industry & Government (ICTBIG)

作者： Pranjal Yadav Piyush Gupta Department of CSE Birla Institute of Technology Jaipur Off-Campus Ranchi India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Nanotechnology has emerged as a revolutionary tool in the pursuit of cleaner air, offering innovative solutions that go beyond the limitations of traditional air purification methods this paper explores clarifications of a multitude of nanotechnology applications in air quality systems, including nanomaterials and nanoparticles' synthesis and their inclusion in air filters, catalytic converters, and pollution sensors the core idea is the utilization of nano-tech solutions to form the materials with a 100% rate of efficiency by capturing and neutralizing the wide range of gaseous pollutants and airborne contagions by using highly active devices. thus, the pollution masters are the following - particulate matter, volatile organic compounds (VOCs), and gases such as oxides of nitrogen (NOx). and carbon monoxide (CO). the paper furthers the investigation on the application of the efficiency of nanocatalysts in pollutant filters, along with one technique of nanocatalysts. Some of the topics considered the utilization of nanocatalysts to augment catalytic converter efficiency, thus, the vehicle and factory emissions are lowered and further investigated the idea of integrating nanotechnology into the pollution sensors, and establishing of devices as it facilitates real-time air monitoring is stressed in the emphasis section. While the paper is positively acclaimed for the benefits of nanotechnology, the associated challenges are also *** like scalability, manufacturing costs, and possible ecotoxicological effects are thoroughly discussed. the majorities are asserting that nanotechnology is the way to go for air quality solutions, but we must think about the considerable long-term effects and we must have the courage to do it. this piece of research emphasizes the essential role of nanotechnology in promoting environmental sustainability, thus, it underscores the importance of responsible development in the process of using nanotechnology to make a sustainab

关键词： Nanoparticles Volatile organic compounds Pollution Purification Scalability Air quality sensor systems and applications Nanoscale devices Nanotechnology Exhaust systems

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A Multi-Agent Deep Reinforcement Learning Framework for Detecting and Mitigating DDoS Attacks in IoT Networks

A Multi-Agent Deep Reinforcement Learning Framework for Dete...

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Mobile Networks and Wireless Communications (ICMNWC), ieee international conference on

作者： Bhushan Shaharkar Tanusha Mittal Krishna Chaitanya Sunkara Vishal Shukla Ishan Desai Ujal Mookherjee Department of Information Technology Walchand Institute of Technology Solapur India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Data Center Design & Modeling AL/ML Expert Oracle Cloud Engineering Raleigh USA Department of Mechanical Engineering Ramdeobaba University Nagpur India Department of Electrical Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Post Limda India Faculty of Law Symbiosis Law School Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

the rise of Internet of things (IoT) networks has introduced new opportunities for innovation but also increased vulnerability to cyberattacks, particularly distributed Denial-of-Service (DDoS) attacks. Traditional detection and mitigation mechanisms often struggle to handle the dynamic and distributed nature of IoT systems. In this study, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) framework designed to detect and mitigate DDoS attacks in IoT networks in real-time. the framework consists of multiple collaborative agents, each deployed at different network layers or nodes, trained using deep reinforcement learning to identify attack patterns and take immediate mitigation actions. through cooperative learning, these agents adapt to evolving attack strategies and optimize defense policies. Experimental evaluations demonstrate that the proposed MADRL framework outperforms traditional techniques by achieving higher detection accuracy, faster response times, and reduced false positive rates. Our results indicate that this framework can effectively secure IoT networks against complex DDoS attacks, maintaining network stability and availability.

关键词： Wireless communication Technological innovation Accuracy Prevention and mitigation Collaboration Denial-of-service attack Deep reinforcement learning Stability analysis Time factors Internet of things

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Simulation of UAS Operator's Decision-Making under Different Weather Conditions

Simulation of UAS Operator's Decision-Making under Different...

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international conference on Modern Electrical and Energy systems (MEES)

作者： Yevheniia Znakovska Yuliya Averyanova Air Navigation Systems Department National Aviation University Kyiv Ukraine

ISBN: (纸本)9798350346848

Nowadays Unmanned aircraft systems (UAS) are used for a range of industrial applications. the peculiarities of tasks require understanding and knowledge of weather-related hazards that restrict or even make impossible the UAS flight and mission realization. Moreover, the modern UAS can be used as mobile sensors to obtain information about the current state of the atmosphere and its parameters for further implementation in IoT technologies including industrial IoT systems. At the same time, there is rather a lack of systems and programs for UAS meteorological support. In this paper, we develop the algorithm and software that can be used as an application for UAS operators. the decision-making algorithm considers the type of UAS (characteristic), mission and area of planned flight, and current weather situation. the developed software is aimed not only to inform UAS operators about the current meteorological state in the intended area of flight but also to give advice and help to take the right decision on the possibility to perform a particular task in a particular area of flight on the base of a risk-oriented approach.

关键词： Simulation Decision making Software algorithms Software sensor systems Planning sensors

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distributed Fog computing Architecture for Real-Time Anomaly Detection in Smart Meter Data 6

Distributed Fog Computing Architecture for Real-Time Anomaly...

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6th ieee international conference on Big Data computing Service and Applications (ieee BigDataService)

作者： Jaiswal, Rituka Chakravorty, Antorweep Rong, Chunming Univ Stavanger Dept Elect Engn & Comp Sci Stavanger Norway

ISBN: (纸本)9781728170220

the use of Fog computing for real-time Big Data monitoring of power consumption is gaining popularity. In traditional systems, Cloud servers receive sensor Big Data, perform predictions and detect anomalies or any threat patterns and then raise the alarms. With exponentially increasing sensor data, Cloud servers are becoming impractical to process this data because of the issues of volume, velocity, variety, network bandwidth, real-time support and security issues. Fog computing is introduced as a distributed computing paradigm that uses intermediate computing infrastructure for processing to overcome the limitations of Cloud computing. In this paper, we propose a hierarchically distributed Fog computing architecture to deploy machine learning based anomaly detection models for generating insights from the collected Smart meter sensor data from the household. the anomaly detection is divided into two steps: model training and anomaly detection. We perform detailed analysis and evaluation of the models using standard open datasets obtained from UCI machine learning repository. the results confirm the efficacy of our proposed architecture. We used open source framework and software for our experiments.

关键词： Fog computing Real-time Anomaly detection Big Data Analytics Machine Learning Ensemble Learning Power Consumption Prediction distributed computing

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An Efficient Congestion Control Model utilizing IoT wireless sensors in Information-Centric Networks 6

An Efficient Congestion Control Model utilizing IoT wireless...

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Joint 6th international conference on Digital Arts, Media and Technology with 4th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunication Engineering, ECTI DAMT and NCON 2021

作者： Sukjaimuk, Rungrot Nguyen, Quang N. Sato, Takuro Chiang Rai Rajabhat University School of Computer and Information Technology Chiang Rai Thailand Waseda University Department of Computer Science and Communications Engineering Tokyo Japan Waseda University Faculty of Science and Engineering Tokyo Japan

ISBN: (纸本)9781665415699

Congestion control is one of the essential keys to enhance network efficiency so that the network can perform well even in the case of packet drop. this problem is even more challenging in Information-Centric Networking (ICN), a typical Future Internet design, which employs the packet flooding policy for forwarding the information. To diminish the high traffic load due to the huge number of packets in the era of the Internet of things (IoT), this paper proposes an effective caching and forwarding algorithm to diminish the congestion rate of the IoT wireless sensor in ICN the proposed network system utilizes accumulative popularity-based delay transmission time for forwarding strategy and includes the consecutive chunks-based segment caching scheme the evaluation results using ndnSIM, a widely-used ns-3 based ICN simulator, demonstrated that the proposed system can achieve less interest packet drop rate, more cache hit rate, and higher network throughput, compared to the relevant ICN-based benchmarks these results prove that the proposed ICN design can achieve higher network efficiency with a lower congestion rate than that of the other related ICN systems using IoT sensors. © 2021 ieee.

关键词： Wireless sensor networks

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distributed Photovoltaic and Energy Storage Collaborative Progressive Planning Method for Distribution Network

Distributed Photovoltaic and Energy Storage Collaborative Pr...

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Electrical Engineering and Control Technologies (CEECT), conference on

作者： Ning Luo Jinsen Liu Pengcheng Zhang Yu Zhang Tao Xu Zewei Liao Power Grid Planning and Research Center Guizhou Power Grid Co. Ltd. Guiyang China School of Electrical Engineering Guizhou University Guiyang China

ISBN: (纸本)9781665499002

According to the traditional planning method, it is difficult to deal with the source and load imbalance caused by the grid connection of distributed photovoltaic and the increasing number of new load, and the future development of source and load imbalance. By studying the conflict characteristics between different planning stages, the progressive planning method is proposed. Comprehensively consider economy, environmental protection and reliability to build a distributed photovoltaic and energy storage collaborative progressive planning model. COBL-PSO algorithm was used to solve the problem, and a collaborative progressive planning scheme of distributed photovoltaic and energy storage was obtained. the ieee33 node model is used for analysis to verify the effectiveness of the proposed method.

关键词： Photovoltaic systems Uncertainty Collaboration Distribution networks Power system stability Power grids Stability analysis

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