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Data Flows Mapping in Fog computing Infrastructures Using Evolutionary Inspired Heuristic 1

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9th international conference on Cloud computing and Services Science (CLOSER)

作者： Canali, Claudia Lancellotti, Riccardo Univ Modena & Reggio Emilia Dept Engn Enzo Ferrari Modena Italy

ISBN: (数字)9783030494322

ISBN: (纸本)9783030494315;9783030494322

the need for scalable and low-latency architectures that can process large amount of data from geographically distributed sensors and smart devices is a main driver for the popularity of the fog computing paradigm. A typical scenario to explain the fog success is a smart city where monitoring applications collect and process a huge amount of data from a plethora of sensing devices located in streets and buildings. the classical cloud paradigm may provide poor scalability as the amount of data transferred risks the congestion on the data center links, while the high latency, due to the distance of the data center from the sensors, may create problems to latency critical applications (such as the support for autonomous driving). A fog node can act as an intermediary in the sensor-to-cloud communications where pre-processing may be used to reduce the amount of data transferred to the cloud data center and to perform latency-sensitive operations. In this book chapter we address the problem of mapping sensors over the fog nodes with a twofold contribution. First, we introduce a formal model for the mapping model that aims to minimize response time considering both network latency and processing time. Second, we present an evolutionary-inspired heuristic (using Genetic Algorithms) for a fast and accurate resolution of this problem. A thorough experimental evaluation, based on a realistic scenario, provides an insight on the nature of the problem, confirms the viability of the GAs to solve the problem, and evaluates the sensitivity of such heuristic with respect to its main parameters.

关键词： Fog computing Optimization model Evolutionary programming Genetic algorithms Smart cities

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Pixy: A privacy-increasing group creation scheme 9

Pixy: A privacy-increasing group creation scheme

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9th international conference on Networks, Communication and computing, ICNCC 2020

作者： Mödinger, David Fröhlich, Nicola Hauck, Franz J. Institute of Distributed Systems Ulm University Germany

ISBN: (纸本)9781450388566

Modern peer-to-peer networks provide a lot of value. However, as the networks handle more and more sensitive data, e.g. in cryptocurrencies, privacy becomes an issue. Several approaches to provide efficient privacy to network participants rely on group formation with little or no regard to the privacy impact of how groups are created. Group creation is often based on random selection, which can easily be highjacked by attackers. We propose Pixy, an extensible, component-based scheme to increase privacy during group formation stages beyond current approaches. Our scheme provides a two-stage setup for group formation. First, a selection based on personal and network-wide collaboration lists reduces the attack surface for group initiators. Second, a testing phase based on cryptographic puzzles and, for suitable contexts, CAPTCHAs sort out Sybil attackers. We show that this scheme improves the current state of privacy in group-creation processes. © 2020 ACM.

关键词： Network protocols

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Edge Service Migration for Vehicular Networks Based on Multi-agent Deep Reinforcement Learning 6th

Edge Service Migration for Vehicular Networks Based on Multi...

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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： Zhang, Haohan Li, Jinglin Yuan, Quan The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9783030386504

To meet the increasing resource demand of intelligent driving, roadside infrastructure is used to provide communication and computing capabilities to vehicles. Existing studies have leveraged deep reinforcement learning to perform small-scale resource scheduling for vehicles. It is critical to implement large-scale resource scheduling to deal with the high mobility of vehicles. However, this large-scale optimization is confronted with huge state and action space. To overcome this challenge, we propose an edge resource allocation method based on multi-agent deep reinforcement learning to reduce system cost while guarantee the quality of intelligent driving. the proposed method considers both immediate and long-term resource status, which helps to select appropriate base stations and edge servers. Trace driven simulations are performed to validate the efficiency of the proposed method. © 2020, Springer Nature Switzerland AG.

关键词： Resource allocation

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Enhanced Microgrid Functions for Topology Reconfiguration and Fault Restoration 2020

Enhanced Microgrid Functions for Topology Reconfiguration an...

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the 9th international conference on Smart Media and Applications

作者： Xiancheng Wang In-Ho Ra Kunsan National University S. Korea

ISBN: (纸本)9781450389259

the power systems are deeply reforming to meet future power demands. With the continuous emergence of new technologies, the novel power system represented by microgrid has received more attention, and the research on the integration of emerging technologies of microgrid has become more focused. In this paper, a microgrid communication framework based on 5G technology is proposed, which makes full use of the low communication delay of 5G technology and the computation capacity of cloud/edge computing to implement the reconfiguration of microgrid deployed with DG(s). Lastly, we estimate the computing power of the cloud servers to predict the loads, and preprocess the restoration Optimal Configuration Table (OCT) scheme for instant fault restoration in the microgrid.

关键词： distributed Energy Resources

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Design of Inter and Intra Plane Communication Protocol for Software-Defined Wireless Sensor Network 9

Design of Inter and Intra Plane Communication Protocol for S...

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9th IEEE international conference on Communication and Signal Processing, ICCSP 2020

作者： Islam, Md. Zahirul Das, Utpal Chandra Sohel, Amir Islam, Monowarul Daffodil International University Department of ETE Dhaka1205 Bangladesh Daffodil International University Department of CSE Dhaka1205 Bangladesh Daffodil International University Dhaka1205 Bangladesh

ISBN: (纸本)9781728149882

Software Defined networking (SDN) brings about innovation, simplicity in network management and configuration in network computing. Traditional networks often lack the flexibility to bring into effect instant changes because of the rigidity of the network and also the over dependency on proprietary services. SDN decouples the control plane from the data plane, thus moving the control logic from the node to a central controller. A Wireless Sensor Network (WSN) is a great platform for Low-Rate Wireless Personal Area Networks (LR-WPAN) with little resources and short communication ranges. However, as the scale of WSN expands it faces several challenges, such as network management and heterogeneous-node networks. the SDN approach to WSNs seeks to alleviate most of the challenges and ultimately foster efficiency and sustainability in WSNs. In this research paper, a protocol is designed in order to communicate inter and intra plane of data and control besides Openflow and ForCes protocol. Moreover, performance of the proposed protocol is compared with the existing protocols in terms of residual energy, delay and lifetime of the sensor nodes. © 2020 IEEE.

关键词： Wireless local area networks (WLAN)

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Improve the House Price Prediction Accuracy with a Stacked Generalization Ensemble Model 6th

Improve the House Price Prediction Accuracy with a Stacked G...

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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： Xiong, Shilong Sun, Qibo Zhou, Ao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9783030386504

House price prediction plays an important role in estate marketplace. Prediction future house price accurately can provide decision making support for home buyers. With the development of machine learning and AI technology, different machine learning models are proposed for house price prediction. However, the prediction accuracy is still not very high. Model ensembling is a very powerful technique to increase the accuracy of a variety of machine learning models. To address this issue, we propose a stacked generation model which consists of various regression models to predict house price. the experiment results show that the stacked model performs better than traditional machine learning models. © 2020, Springer Nature Switzerland AG.

关键词： Machine learning

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An Autonomous Food Wastage Control Warehouse: distributed Ledger and Machine Learning based Approach 11

An Autonomous Food Wastage Control Warehouse: Distributed Le...

引用

11th international conference on computing, Communication and networking Technologies, ICCCNT 2020

作者： Nikish Kumar, S.V. Balasubramaniam, Saiharsha tharagesh, Sanjay R. S. Kumar, Priyanka Janavi, B. Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering India

ISBN: (纸本)9781728168517

Food is an essential aspect of our lives. Back in those days, people consumed seasonal fruits and vegetables which were available locally and on a daily basis. But things have changed when refrigeration units were invented. People were now able to store and consume food products which were nonseasonal also. In today's world, warehouses and cold chains are of striking importance because most of the food products that we see in our supermarkets and grocery stores come from them. Due to improper management of food products these warehouses have become an epicenter for food wastage. Since most of the traditional warehouses are manual or RDBMS based, they can be easily tampered. With these considerations in this paper, we have proposed an autonomous warehouse system which will tackle food wastage by using Machine Learning and Blockchain technology. © 2020 IEEE.

关键词： Warehouses

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Dynamic mapping mechanism between edge capability and service in Internet of things under edge-end synergy 9

Dynamic mapping mechanism between edge capability and servic...

引用

9th IEEE Joint international Information Technology and Artificial Intelligence conference, ITAIC 2020

作者： Liu, Guofeng Zhang, Can Wang, Yuqi Zhao, Xuan State Grid Jiangsu Power Co. Ltd. Nanjing Power Supply Branch China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

With the rapid popularization of the Internet of everything era and the continuous development of communication technology, more and more terminal devices have entered people's life and become an important part of changing People's Daily life. However, these terminals, while bringing convenience, put forward higher requirements for the processing capacity of edge network How to use algorithms to efficiently utilize the resources in edge network to meet the service needs has become a hot issue that people pay attention to. this paper proposes a new network topology structure in which edge nodes and terminal devices work together, quantifying a series of capabilities such as computing resources and storage resources of edge nodes and terminal devices, and then making unified scheduling and allocation to meet the needs of different services. this paper analyzes the relationship between various physical quantities in the model, and proposes a service mapping mechanism based on the minimization of interactive energy consumption and resource occupation cost. the purpose is to minimize interactive energy consumption and resource occupation cost as much as possible on the premise of satisfying service delay and resource request. Heuristic algorithms tend to assign service priority to edge nodes, followed by terminal devices with computing or storage capacity. It mainly considers the difference in energy consumption and resource occupation cost caused by placing the service on a single terminal device and multiple terminal devices, so it tends to place the service on the terminal device with a large amount of resources, so as to reduce transmission energy consumption and the occupation cost of bandwidth and input and output resources. Finally, a simulation experiment is designed to verify the effectiveness of the heuristic algorithm. the results show that the heuristic algorithm has better performance in the case of large service tasks, and can reduce the interaction energy

关键词： Internet of things

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Learning Route Planning from Experienced Drivers Using Generalized Value Iteration Network 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： Wang, Xiao Yuan, Quan Liu, Zhihan Dong, Yushun Wei, Xiaojuan Li, Jinglin State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9783030386511

ISBN: (纸本)9783030386504

Traffic congestion has long been a serious problem in cities, and route planning can improve traffic efficiency. the existing route planning approach relies on current and future traffic status. However, because traffic prediction and route planning interact with each other, the actual driving results deviate from expectations, and the performance is not satisfactory. In order to solve this problem, considering the topology of road networks, this paper proposes a route planning algorithm based on generalized value iteration network (GVIN), which uses graph convolution to extract the features of traffic flow, and then imitates human routing experience under various traffic status. Finally we evaluate the performance of the proposed network on real map and trajectory data in Beijing, China. the experimental results show that GVIN can simulate the human’s routing decisions with high success rate and less commuting time. © 2020, Springer Nature Switzerland AG.

关键词： Traffic congestion

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distributed computing framework of intelligent sensor network for electric power internet of things

Distributed computing framework of intelligent sensor networ...

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IEEE Joint international Information Technology and Artificial Intelligence conference (ITAIC)

作者： Dawei Li Ruimo Yu Chunhe Song Shu Li Gengtao Jia Xiaoli Zhou Start Grid Shanghai Information & Telecommunication Company Shanghai China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

the power IoT terminal environment is complex, and current smart sensor sensors have problems such as poor networking capabilities and large processing delays, and few specific edge-side automatic networking capabilities. this paper analyzes and elaborates the distributed intelligent sensor computing framework from the perspective of edge intelligent networking, designs a computing model for intelligent sensors of power Internet of things terminals, and gives the edge-based deployment of convolutional neural network architecture, data aggregation, model training, and intelligent reasoning. Finally, the validity of the proposed framework model is verified by public sample data.

关键词： Training Analytical models Computational modeling Data aggregation Data models Internet of things Intelligent sensors

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