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Predicting Steering for Autonomous Vehicles Based on Crowd Sensing and Deep Learning 1

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6th international conference on Internet of Vehicles and the 9th international Symposium on Cloud and Service computing, IOV/SC2 2019

作者： Liu, Taiyu Li, Jinglin Yuan, Quan State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9783030386511

ISBN: (纸本)9783030386504

the challenge in ensuring the reliability of autonomous vehicles is full awareness of the surrounding environment and high-precision steering control. the latest solutions to this challenge include deep learning technologies that provide end-to-end solutions to predict steering angles directly from environmental cognition information with high accuracy. Under the background of 5G technology, edge device has certain computing power, which can reduce the load of on-board computing equipment. In this paper, we present a new distributed perception-decision network model. this model allows the network’s computing tasks to be offloaded to the edge computing devices to reduce the consumption of vehicle-mounted computing devices. the feasibility of the model is verified by experiments. Compared with the existing methods, the model also has a higher accuracy of steering prediction. © 2020, Springer Nature Switzerland AG.

关键词： Deep neural networks

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Power Anomaly Data Detection Algorithm Based on User Tag and Random Forest 9th

Power Anomaly Data Detection Algorithm Based on User Tag and...

引用

9th international conference on Computer Engineering and Networks, CENet2019

作者： Guo, JianXun Liu, Bo Zhang, Hongyan Li, Qiang Guo, Shaoyong Yang, Yang State Grid Henan Electric Power Company Zhengzhou China State Grid Henan Information and Telecommunication Company Zhengzhou China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789811537523

In the context of the allocation of electricity big data, in order to solve the problem that the power consumption anomaly detection algorithm is less efficient due to the diverse sources, various types, and large data volume of the power consumption data, this paper proposes a power anomaly data detection algorithm based on user tag and random forest. By analyzing the power data, each data is marked with a power type, including a normal type, a less bur type, a large downward shift type, and a multi-burr type. Based on the tags, three types of user power data tags, such as basic information tags, environmental information tags, and power information tags, are constructed as attribute sets of user power data. Finally, a user data anomaly data analysis algorithm based on user tag and random forest is proposed. In the experimental part, the traditional the random forest algorithm is compared with the proposed algorithm, and the performance index of the algorithm in both accuracy and false alarm rate is better than the traditional random forest algorithm. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection

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Quantifying and Improving Performance of distributed Deep Learning with Cloud Storage 9

Quantifying and Improving Performance of Distributed Deep Le...

引用

9th IEEE international conference on Cloud Engineering, IC2E 2021

作者： Krichevsky, Nicholas St Louis, Renee Guo, Tian Worcester Polytechnic Institute Computer Science Department WorcesterMA United States

ISBN: (纸本)9781665449700

Cloud computing provides a powerful yet low-cost environment for distributed deep learning workloads. However, training complex deep learning models often requires accessing large amounts of data, which can easily exceed the capacity of local disks. Prior research often overlooks this training data problem by implicitly assuming that data is available locally or via low latency network-based data storage. Such implicit assumptions often do not hold in a cloud-based training environment, where deep learning practitioners create and tear down dedicated GPU clusters on demand, or do not have the luxury of local storage, such as in serverless workloads. In this work, we investigate the performance of distributed training that leverages training data residing entirely inside cloud storage buckets. these buckets promise low storage costs, but come with inherent bandwidth limitations that make them seem unsuitable for an efficient training solution. To account for these bandwidth limitations, we propose the use of two classical techniques, namely caching and pre-fetching, to mitigate the training performance degradation. We implement a prototype, DELI, based on the popular deep learning framework PyTorch by building on its data loading abstractions. We then evaluate the training performance of two deep learning workloads using Google Cloud's NVIDIA K80 GPU servers and show that we can reduce the time that the training loop is waiting for data by 85.6%-93.5% compared to loading directly from a storage bucket-thus achieving comparable performance to loading data directly from disk-while only storing a fraction of the data locally at a time. In addition, DELI has the potential of lowering the cost of running a training workload, especially on models with long per-epoch training times. © 2021 IEEE.

关键词： Loading

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ALGORIthM for SOLVING PROBLEM SYNthESIS the OPTIMAL LOGICAL STRUCTURE distributed DATA in ARCHITECTURE of GRID SERVICE 9

ALGORITHM for SOLVING PROBLEM SYNTHESIS the OPTIMAL LOGICAL ...

引用

9th international conference "distributed computing and Grid Technologies in Science and Education", GRID 2021

作者： Nurmatova, Elena Gusev, Victor MIREA - Russian Technological University 20 Stromynka Moscow107996 Russia National Research Center Kurchatov Institute Institute for High Energy Physics 1 Science sq Protvino142281 Russia

the questions of constructing optimal logical structure of a distributed database (DDB) are considered. Solving these issues will make it possible to increase the speed of processing requests in DDB in comparison with a traditional database. In particular, such tasks arise for the organization of systems for processing huge amounts of information from the Large Hadron Collider. In these systems various DDB are used to store information about: the system of triggers of data collection from physical experimental installations, the geometry and the operating conditions of the detector while collecting experimental data. Two interrelated stages in the synthesis algorithm are proposed. At the first stage, the problem of distribution of database clusters between the server and clients, followed by the problem of optimal distribution of data groups of each node by types of logical records are addressed. At the second stage the problem of database localization on the nodes of the computer network is solved, in addition to the results of the first stage, the characteristics of the DDB are taken into account. Optimal logical structure of DDB will ensure the efficiency of the information system on computational resources. As a result of its solution, the local network of the DDB is decomposed into a number of clusters that have minimal information connectivity with each other. Solving the problem of synthesis of the optimal logical structure is also of great practical importance for the automated design of logical structures, for the automated formation of query specifications and adjustments of the DDB. © 2021 CEUR-WS. All rights reserved.

关键词： Data warehouses

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IMPLEMENTATION of the CONDITION DATABASE for the EXPERIMENTS of the NICA COMPLEX 9

IMPLEMENTATION of the CONDITION DATABASE for the EXPERIMENTS...

引用

9th international conference "distributed computing and Grid Technologies in Science and Education", GRID 2021

作者： Gertsenberger, Konstantin Chebotov, Alexander Klimai, Peter Alexandrov, Igor Alexandrov, Evgeny Filozova, Irina Moshkin, Andrey Joint Institute for Nuclear Research 6 Joliot-Curie Dubna Moscow region 141980 Russia Moscow Institute of Physics and Technology 9 Institutskiy per. Dolgoprudny Moscow region 141701 Russia

the processing and analysis of experimental and simulated data are an integral part of all modern high-energy physics experiments. these tasks are of particular importance in the experiments of the NICA project at the Joint Institute for Nuclear Research due to the high interaction rate and large particle multiplicity of ion collision events, therefore, the task of automating the considered processes for the NICA complex has particular relevance. the paper describes a new information system based on the Condition Database, as well as related services to automate storing, managing, searching and using various parameters and operation modes of the on-going and future NICA experiments for experimental and simulated data processing. the implemented system provides the necessary information for event processing and physics analysis tasks, and organises the transparent, unified access to and management of the required parameter data in scientific research. the scheme and purposes of the Condition Database, its attributes, key aspects of the development are described. In addition, a common deployment system of the developed database and related services for the NICA experiments is noted. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Web services

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DEVELOPMENT of the EVENT METADATA SYSTEM for the NICA EXPERIMENTS 9

DEVELOPMENT of the EVENT METADATA SYSTEM for the NICA EXPERI...

引用

9th international conference "distributed computing and Grid Technologies in Science and Education", GRID 2021

作者： Alexandrov, Evgeny Alexandrov, Igor Degtyarev, Artyom Filozova, Irina Gertsenberger, Konstantin Klimai, Peter Yakovlev, Alexander Joint Institute for Nuclear Research 6 Joliot-Curie st. Dubna 141980 Russia Moscow Institute of Physics and Technology 9 Institutskiy per. Dolgoprudny 141701 Russia

Particle collision experiments are known to generate a substantial amount of data that must be stored and then analyzed. Typically, only a small subset of all collected events is relevant when performing a particular physics analysis task. Although it is possible to obtain the required subset of event records directly, by iterating through the whole volume of the collected data, the process is very time and resource consuming. Instead, a more convenient approach widely used in modern particle collision experiments is to have an event metadata or indexing system that stores summary properties of all events and allows for the fast search for and retrieval of only necessary events based on given criteria. Such a system, called the Event Metadata System, has been developed for the current fixed-target and future collider experiments of the NICA project. the design of the system, its components, user interfaces and the REST API service, its integration with existing experiment systems and software, as well as associated challenges, are presented. © 2021 CEUR-WS. All rights reserved.

关键词： Metadata

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Topology Self-optimization for Anti-tracking Network via Nodes distributed computing 17th

Topology Self-optimization for Anti-tracking Network via Nod...

引用

17th EAI international conference on Collaborative computing: networking, Applications, and Worksharing, CollaborateCom 2021

作者： Tian, Changbo Zhang, Yongzheng Yin, Tao Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9783030926342

Anti-tracking network aims to protect the privacy of network users’ identities and communication relationship. the research of P2P-based anti-tracking network has attracted more and more attentions because of its decentralization, scalability, and widespread distribution. But, P2P-based anti-tracking network still faces the attacks on network structure which can destroy the usability of anti-tracking network effectively. So, a secure and resilient network structure is an important prerequisite to maintain the stability and security of anti-tracking network. In this paper, we propose a topology self-optimization method for anti-tracking network via nodes distributed computing. Based on convex-polytope topology (CPT), our proposal achieves topology self-optimization by each node optimizing its local topology in optimum structure. through the collaboration of all nodes in network, the whole network topology will evolve into the optimum structure. Our experimental results show that the topology self-optimization method improves the network robustness and resilience of anti-tracking network when confronting to the dynamic network environment. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Structural optimization

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Load Balance Awared Data Sharing Systems In Heterogeneous Edge Environment 26

Load Balance Awared Data Sharing Systems In Heterogeneous Ed...

引用

26th IEEE international conference on Parallel and distributed Systems (IEEE ICPADS)

作者： Chen, Sheng Chen, Zheng Gu, Siyuan Chen, Baochao Xie, Junjie Guo, Deke Tianjin Univ Colledge Intelligence & Comp Tianjin Peoples R China Natl Univ Def Technol Sci & Technol Informat Syst Engn Lab Changsha Hunan Peoples R China PLA Inst Syst Engn AMS Beijing Peoples R China

ISBN: (纸本)9781728190747

Edge computing has become the de facto method for delay-sensitive applications, in which the computation and storage resources are placed at the edge of network. the main responsibility of edge computing is to carry data from the Cloud downlinks and terminal uplinks, and organize these data well on the edge side. this is the basis for subsequent analysis and processing of data. therefore, this brings about a question as to how those data should be organized on the edge and how to store and retrieve them. In response to this demand, some methods have been proposed to solve the related problems of how to build data storage and retrieval services on the edge side. those methods propose three different solutions: structured, unstructured, and hybrid schemes. However, the data storage and retrieval services for the heterogeneous edge environment is still lack of research. It is still not considered an important design test load balancing when the data is stored on the edge side. In this paper, we design and implement w-strategy, a load balance approach that implements the appropriate load balance among the heterogeneous edge nodes by using the weighted Voronoi diagram. Our solution utilizes the software defined networking paradigm to support a virtual-space based distributed hash tables (DHTs) to distribute data. Evaluation results show that w-strategy achieves better load balancing among the heterogeneous edge nodes compared to the existing methods, GRED and Chord. And, the w-strategy improves the average underutilization of the resources by 20%.

关键词： weighted Voronoi SDN load balance edge computing

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A Method of Dynamic Resource Adjustment for 5G Network Slice 9th

A Method of Dynamic Resource Adjustment for 5G Network Slice

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9th international conference on Computer Engineering and Networks, CENet2019

作者： Ou, Qinghai Song, Jigao Wang, Yanru Wang, Zhiqiang Yang, Yang Ran, Diya Feng, Lei Beijing Fibrlink Communications Co. Ltd. Beijing China State Grid Shanxi Electric Power Company Xi’an China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789811537523

5G network supports multiple application scenarios with different performance requirements. In various application scenarios, the network needs to meet different delay, reliability, and speed requirements. Network slicing is the best solution to this problem. the traffic volume changes over time, which causes the resource utilization of network slices to change frequently. the static allocation of resources in the network slice may have insufficient resource utilization or resource overload. therefore, it is necessary to dynamically adjust the resource amount of the network slice to optimize the utilization of network resources. Based on the above analysis and requirements, this paper proposes a dynamic resource adjustment scheme (DRAS) in the network slice of 5G local transmission network, in order to improve the utilization of network resources. Finally, the simulation results show that the scheme is superior to the non-dynamic resource allocation scheme (non-DRAS). © 2021, Springer Nature Singapore Pte Ltd.

关键词： 5G mobile communication systems

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Resource Allocation Mechanism in Electric Vehicle Charging Scenario for Ubiquitous Power-IoT Coverage Enhancement 9th

Resource Allocation Mechanism in Electric Vehicle Charging S...

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9th international conference on Computer Engineering and Networks, CENet2019

作者： Wang, Yao Liang, Yun Tian, Wenfeng Sun, Xiaoyan Yin, Xiyang Zhu, Liang Ran, Diya Global Energy Interconnection Research Institute State Grid Corporation of China Beijing China State Grid Tianjin Electric Power Company Tianjin China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789811537523

For the coverage enhancement of the electric vehicles charging terminals at the edge of the NB-IoT cellular system, a D2D pairing strategy is proposed. the optimal cellular terminal for D2D pairing is determined by the corresponding transmission outage. the limited radio resource in the cellular system can be reused by the D2D links, which will increase the spectrum utilization but will also result in co-channel interference and system throughput loss. therefore, a joint power control and channel assignment algorithm is adopted to improve the system capacity as much as possible. the optimal power strategy can be obtained by the geometric programming solution, and the subchannels can be assigned properly through the KM (Kuhn-Munkres) algorithm in the weighted bipartite graph matching problem. Finally, results showed that the proposed strategy can reduce edge terminals’ outage probability and improve throughput. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Outages

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