• Title/Summary/Keyword: fog node

Search Result 22, Processing Time 0.026 seconds

Traffic-based reinforcement learning with neural network algorithm in fog computing environment

  • Jung, Tae-Won;Lee, Jong-Yong;Jung, Kye-Dong
    • International Journal of Internet, Broadcasting and Communication
    • /
    • v.12 no.1
    • /
    • pp.144-150
    • /
    • 2020
  • Reinforcement learning is a technology that can present successful and creative solutions in many areas. This reinforcement learning technology was used to deploy containers from cloud servers to fog servers to help them learn the maximization of rewards due to reduced traffic. Leveraging reinforcement learning is aimed at predicting traffic in the network and optimizing traffic-based fog computing network environment for cloud, fog and clients. The reinforcement learning system collects network traffic data from the fog server and IoT. Reinforcement learning neural networks, which use collected traffic data as input values, can consist of Long Short-Term Memory (LSTM) neural networks in network environments that support fog computing, to learn time series data and to predict optimized traffic. Description of the input and output values of the traffic-based reinforcement learning LSTM neural network, the composition of the node, the activation function and error function of the hidden layer, the overfitting method, and the optimization algorithm.

Graph Assisted Resource Allocation for Energy Efficient IoT Computing

  • Mohammed, Alkhathami
    • International Journal of Computer Science & Network Security
    • /
    • v.23 no.1
    • /
    • pp.140-146
    • /
    • 2023
  • Resource allocation is one of the top challenges in Internet of Things (IoT) networks. This is due to the scarcity of computing, energy and communication resources in IoT devices. As a result, IoT devices that are not using efficient algorithms for resource allocation may cause applications to fail and devices to get shut down. Owing to this challenge, this paper proposes a novel algorithm for managing computing resources in IoT network. The fog computing devices are placed near the network edge and IoT devices send their large tasks to them for computing. The goal of the algorithm is to conserve energy of both IoT nodes and the fog nodes such that all tasks are computed within a deadline. A bi-partite graph-based algorithm is proposed for stable matching of tasks and fog node computing units. The output of the algorithm is a stable mapping between the IoT tasks and fog computing units. Simulation results are conducted to evaluate the performance of the proposed algorithm which proves the improvement in terms of energy efficiency and task delay.

A Study on Data Movement Method between For for Cloud Computing (클라우드를 위한 포그 간의 데이터 이동 기법에 관한 연구)

  • Hwang, Chi-Gon;Yoon, Chang-Pyo;Lee, Hae-Jun
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
    • /
    • 2017.05a
    • /
    • pp.294-296
    • /
    • 2017
  • Cloud computing is a computing technique that uploads all the data from a cloud node to a cloud server and provides it to users as a service. This is difficult to provide services in real time depending on the network conditions. This is because it is necessary to download information to the remote site through the network, not the local area, and to download additional services to provide services in the cloud. So fog computing has been proposed as an alternative. In this paper, we propose an efficient data exchange technique between cloud, fog and user. The proposed fog provides services to users and collects and processes data. The cloud is responsible for the flow of data exchange and control between the fog. We propose a standard method for data exchange. The application for this is to process and service the information generated by the BAN (Body Area Network) in the fog, and the cloud serves as a mediator. This can resolve data heterogeneity between devices or services and provide efficient data movement.

  • PDF

Design and Evaluation of an Edge-Fog Cloud-based Hierarchical Data Delivery Scheme for IoT Applications (사물인터넷 응용을 위한 에지-포그 클라우드 기반 계층적 데이터 전달 방법의 설계 및 평가)

  • Bae, Ihn-Han
    • Journal of Internet Computing and Services
    • /
    • v.19 no.1
    • /
    • pp.37-47
    • /
    • 2018
  • The number of capabilities of Internet of Things (IoT) devices will exponentially grow over the next years. These devices may generate a vast amount of time-constrained data. In the context of IoT, data management should act as a layer between the objects and devices generating the data and the applications accessing the data for analysis purposes and services. In addition, most of IoT services will be content-centric rather than host centric to increase the data availability and the efficiency of data delivery. IoT will enable all the communication devices to be interconnected and make the data generated by or associated with devices or objects globally accessible. Also, fog computing keeps data and computation close to end users at the edge of network, and thus provides a new breed of applications and services to end users with low latency, high bandwidth, and geographically distributed. In this paper, we propose Edge-Fog cloud-based Hierarchical Data Delivery ($EFcHD^2$) method that effectively and reliably delivers IoT data to associated with IoT applications with ensuring time sensitivity. The proposed $EFcHD^2$ method stands on basis of fully decentralized hybrid of Edge and Fog compute cloud model, Edge-Fog cloud, and uses information-centric networking and bloom filters. In addition, it stores the replica of IoT data or the pre-processed feature data by edge node in the appropriate locations of Edge-Fog cloud considering the characteristic of IoT data: locality, size, time sensitivity and popularity. Then, the performance of $EFcHD^2$ method is evaluated through an analytical model, and is compared to fog server-based and Content-Centric Networking (CCN)-based data delivery methods.

Research on Security Model and Requirements for Fog Computing: Survey (포그 컴퓨팅 보안 모델과 보안 요구사항 연구: 서베이)

  • Hong, Sunghyuck
    • Journal of the Korea Convergence Society
    • /
    • v.9 no.5
    • /
    • pp.27-32
    • /
    • 2018
  • IoT technology is developing with various application areas in $4^{th}$ Industrial revolution. There are many users using the application services. Sensing data from various environment need to be transferred to cloud computing storage and store in the cloud storage. However, physical distance from the end node to cloud computing storage is far away, and it is not efficient to transfer data from sensors and store the sensing data in the cloud storage whenever sensing data happen. Therefore, Fog computing is proposed to solve these problems which can process and store the sensing data. However, Fog computing is new emerging technology, there is no standard security model and requirements. This research proposes to security requirements and security model for Fog computing to establish a secure and efficient cloud computing environment.

An Efficient Method for Improving the Reliability of Sensing Data Using Multi-sensors in Wireless Sensor Network Systems (다중센서를 이용한 무선센서네트워크시스템에서의 효율적인 측정데이터 신뢰성 향상 방법)

  • Lee, Sang-Shin;Song, Min-Hwan;Won, Kwang-Ho;Kim, Joong-Hwan
    • Journal of The Institute of Information and Telecommunication Facilities Engineering
    • /
    • v.8 no.3
    • /
    • pp.116-121
    • /
    • 2009
  • A novel method for improving the reliability of sensing data using multi-sensors in wireless sensor network systems is presented in this paper. This method is successfully applied a fog monitoring system in the mountain area.

  • PDF

Service Deployment Strategy for Customer Experience and Cost Optimization under Hybrid Network Computing Environment

  • Ning Wang;Huiqing Wang;Xiaoting Wang
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.17 no.11
    • /
    • pp.3030-3049
    • /
    • 2023
  • With the development and wide application of hybrid network computing modes like cloud computing, edge computing and fog computing, the customer service requests and the collaborative optimization of various computing resources face huge challenges. Considering the characteristics of network environment resources, the optimized deployment of service resources is a feasible solution. So, in this paper, the optimal goals for deploying service resources are customer experience and service cost. The focus is on the system impact of deploying services on load, fault tolerance, service cost, and quality of service (QoS). Therefore, the alternate node filtering algorithm (ANF) and the adjustment factor of cost matrix are proposed in this paper to enhance the system service performance without changing the minimum total service cost, and corresponding theoretical proof has been provided. In addition, for improving the fault tolerance of system, the alternate node preference factor and algorithm (ANP) are presented, which can effectively reduce the probability of data copy loss, based on which an improved cost-efficient replica deployment strategy named ICERD is given. Finally, by simulating the random occurrence of cloud node failures in the experiments and comparing the ICERD strategy with representative strategies, it has been validated that the ICERD strategy proposed in this paper not only effectively reduces customer access latency, meets customers' QoS requests, and improves system service quality, but also maintains the load balancing of the entire system, reduces service cost, enhances system fault tolerance, which further confirm the effectiveness and reliability of the ICERD strategy.

Analysis of Open Source Edge Computing Platforms: Architecture, Features, and Comparison (오픈 소스 엣지 컴퓨팅 플랫폼 분석: 구조, 특징, 비교)

  • Lim, Huhnkuk;Lee, Heejin
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.24 no.8
    • /
    • pp.985-992
    • /
    • 2020
  • Edge computing is a technology that can prepare for a new era of cloud computing. Edge computing is not a remote data center where data is processed and computed, but low-latency/high-speed computing is realized by adding computing power and data processing power to the edge side close to an access point such as a terminal device or a gateway. It is possible. The types of edge computing include mobile edge computing, fog computing, and cloudlet computing. In this article, we describes existing open source platforms for implementing edge computing nodes. By presenting and comparing the structure, features of open source edge platforms, it is possible to acquire knowledge required to select the best edge platform for industrial engineers who want to build an edge node using an actual open source edge computing platform.

Clinical Application of $^{18}F-FDG$ PET in Gastric Cancer (위암에서 $^{18}F-FDG$ PET의 임상 이용)

  • Yun, Mi-Jin;Kim, Tae-Sung;Hwang, Hee-Sung
    • Nuclear Medicine and Molecular Imaging
    • /
    • v.42 no.sup1
    • /
    • pp.39-45
    • /
    • 2008
  • PET or PET/CT detects only less than 50% of early gastric cancer and 62-98% of advanced gastric cancer. Therefore, mass screening programs are recommended for all adults over the age of 40 for early detection and early treatment of gastric cancer through endoscopy or various radiological tests. The most important step after diagnosis of gastric cancer is accurate staging, which mainly evaluates tumor resectability to avoid unnecessary surgery. Important factors that affect tumor resectability are whether the tumor can be separated from adjacent organs or important blood vessels, the extent of lymph node metastasis, presence of peritoneal metastasis, or distant organ metastasis. To evaluate the extent of local tumor invasion, anatomical imaging that has superior spatial resolution is essential. There are a few studies on prognostic significance of FDG uptake with inconsistent results between them. In spite of lower sensitivity for lymph node staging, the specificity of CT and PET are very high, and the specificity for PET tends to be higher than that for CT. Limited data published so far show that PET seems less useful in the detection of lung and bone metastasis. In the evaluation of pleural or peritoneal metastasis, PET seems very specific but insensitive as well. When FOG uptake of primary tumor is low, distant metastasis also tends to show low FDG uptake reducing its detection on PET. There are only a few data available in the evaluation of recurrence detection and treatment response using FDG PET or PET/CT.

Software-Defined Cloud-based Vehicular Networks with Task Computation Management

  • Nkenyereye, Lionel;Jang, Jong-Wook
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
    • /
    • 2018.05a
    • /
    • pp.419-421
    • /
    • 2018
  • Cloud vehicular networks are a promising paradigm to improve vehicular through distributing computation tasks between remote clouds and local vehicular terminals. Software-Defined Network(SDN) can bring advantages to Intelligent Transportation System(ITS) through its ability to provide flexibility and programmability through a logically centralized controlled cluster that has a full comprehension of view of the network. However, as the SDN paradigm is currently studied in vehicular ad hoc networks(VANETs), adapting it to work on cloud-based vehicular network requires some changes to address particular computation features such as task computation of applications of cloud-based vehicular networks. There has been initial work on briging SDN concepts to vehicular networks to reduce the latency by using the fog computing technology, but most of these studies do not directly tackle the issue of task computation. This paper proposes a Software-Defined Cloud-based vehicular Network called SDCVN framework. In this framework, we study the effectiveness of task computation of applications of cloud-based vehicular networks with vehicular cloud and roadside edge cloud. Considering the edge cloud service migration due to the vehicle mobility, we present an efficient roadside cloud based controller entity scheme where the tasks are adaptively computed through vehicular cloud mode or roadside computing predictive trajectory decision mode. Simulation results show that our proposal demonstrates a stable and low route setup time in case of installing the forwarding rules of the routing applications because the source node needs to contact the controller once to setup the route.

  • PDF