421 research outputs found
Using Machine Learning for Handover Optimization in Vehicular Fog Computing
Smart mobility management would be an important prerequisite for future fog
computing systems. In this research, we propose a learning-based handover
optimization for the Internet of Vehicles that would assist the smooth
transition of device connections and offloaded tasks between fog nodes. To
accomplish this, we make use of machine learning algorithms to learn from
vehicle interactions with fog nodes. Our approach uses a three-layer
feed-forward neural network to predict the correct fog node at a given location
and time with 99.2 % accuracy on a test set. We also implement a dual stacked
recurrent neural network (RNN) with long short-term memory (LSTM) cells capable
of learning the latency, or cost, associated with these service requests. We
create a simulation in JAMScript using a dataset of real-world vehicle
movements to create a dataset to train these networks. We further propose the
use of this predictive system in a smarter request routing mechanism to
minimize the service interruption during handovers between fog nodes and to
anticipate areas of low coverage through a series of experiments and test the
models' performance on a test set
Detecting Irregular Patterns in IoT Streaming Data for Fall Detection
Detecting patterns in real time streaming data has been an interesting and
challenging data analytics problem. With the proliferation of a variety of
sensor devices, real-time analytics of data from the Internet of Things (IoT)
to learn regular and irregular patterns has become an important machine
learning problem to enable predictive analytics for automated notification and
decision support. In this work, we address the problem of learning an irregular
human activity pattern, fall, from streaming IoT data from wearable sensors. We
present a deep neural network model for detecting fall based on accelerometer
data giving 98.75 percent accuracy using an online physical activity monitoring
dataset called "MobiAct", which was published by Vavoulas et al. The initial
model was developed using IBM Watson studio and then later transferred and
deployed on IBM Cloud with the streaming analytics service supported by IBM
Streams for monitoring real-time IoT data. We also present the systems
architecture of the real-time fall detection framework that we intend to use
with mbientlabs wearable health monitoring sensors for real time patient
monitoring at retirement homes or rehabilitation clinics.Comment: 7 page
์ฃ์ง ํด๋ผ์ฐ๋ ํ๊ฒฝ์ ์ํ ์ฐ์ฐ ์คํ๋ก๋ฉ ์์คํ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ ๋ณด๊ณตํ๋ถ,2020. 2. ๋ฌธ์๋ฌต.The purpose of my dissertation is to build lightweight edge computing systems which provide seamless offloading services even when users move across multiple edge servers. I focused on two specific application domains: 1) web applications and 2) DNN applications.
I propose an edge computing system which offload computations from web-supported devices to edge servers. The proposed system exploits the portability of web apps, i.e., distributed as source code and runnable without installation, when migrating the execution state of web apps. This significantly reduces the complexity of state migration, allowing a web app to migrate within a few seconds. Also, the proposed system supports offloading of webassembly, a standard low-level instruction format for web apps, having achieved up to 8.4x speedup compared to offloading of pure JavaScript codes.
I also propose incremental offloading of neural network (IONN), which simultaneously offloads DNN execution while deploying a DNN model, thus reducing the overhead of DNN model deployment. Also, I extended IONN to support large-scale edge server environments by proactively migrating DNN layers to edge servers where mobile users are predicted to visit. Simulation with open-source mobility dataset showed that the proposed system could significantly reduce the overhead of deploying a DNN model.๋ณธ ๋
ผ๋ฌธ์ ๋ชฉ์ ์ ์ฌ์ฉ์๊ฐ ์ด๋ํ๋ ๋์์๋ ์ํํ ์ฐ์ฐ ์คํ๋ก๋ฉ ์๋น์ค๋ฅผ ์ ๊ณตํ๋ ๊ฒฝ๋ ์ฃ์ง ์ปดํจํ
์์คํ
์ ๊ตฌ์ถํ๋ ๊ฒ์
๋๋ค. ์น ์ดํ๋ฆฌ์ผ์ด์
๊ณผ ์ธ๊ณต์ ๊ฒฝ๋ง (DNN: Deep Neural Network) ์ด๋ผ๋ ๋ ๊ฐ์ง ์ดํ๋ฆฌ์ผ์ด์
๋๋ฉ์ธ์์ ์ฐ๊ตฌ๋ฅผ ์งํํ์ต๋๋ค.
์ฒซ์งธ, ์น ์ง์ ์ฅ์น์์ ์ฃ์ง ์๋ฒ๋ก ์ฐ์ฐ์ ์คํ๋ก๋ํ๋ ์ฃ์ง ์ปดํจํ
์์คํ
์ ์ ์ํฉ๋๋ค. ์ ์๋ ์์คํ
์ ์น ์ฑ์ ์คํ ์ํ๋ฅผ ๋ง์ด๊ทธ๋ ์ด์
ํ ๋ ์น ์ฑ์ ๋์ ์ด์์ฑ(์์ค ์ฝ๋๋ก ๋ฐฐํฌ๋๊ณ ์ค์นํ์ง ์๊ณ ์คํํ ์ ์์)์ ํ์ฉํฉ๋๋ค. ์ด๋ฅผ ํตํด ์ํ ๋ง์ด๊ทธ๋ ์ด์
์ ๋ณต์ก์ฑ์ด ํฌ๊ฒ ์ค์ฌ์ ์น ์ฑ์ด ๋ช ์ด ๋ด์ ๋ง์ด๊ทธ๋ ์ด์
๋ ์ ์์ต๋๋ค. ๋ํ, ์ ์๋ ์์คํ
์ ์น ์ดํ๋ฆฌ์ผ์ด์
์ ์ํ ํ์ค ์ ์์ค ์ธ์คํธ๋ญ์
์ธ ์น ์ด์
๋ธ๋ฆฌ ์คํ๋ก๋๋ฅผ ์ง์ํ์ฌ ์์ํ JavaScript ์ฝ๋ ์คํ๋ก๋์ ๋น๊ตํ์ฌ ์ต๋ 8.4 ๋ฐฐ์ ์๋ ํฅ์์ ๋ฌ์ฑํ์ต๋๋ค.
๋์งธ, DNN ์ดํ๋ฆฌ์ผ์ด์
์ ์ฃ์ง ์๋ฒ์ ๋ฐฐํฌํ ๋, DNN ๋ชจ๋ธ์ ์ ์กํ๋ ๋์ DNN ์ฐ์ฐ์ ์คํ๋ก๋ ํ์ฌ ๋น ๋ฅด๊ฒ ์ฑ๋ฅํฅ์์ ๋ฌ์ฑํ ์ ์๋ ์ ์ง์ ์คํ๋ก๋ ๋ฐฉ์์ ์ ์ํฉ๋๋ค. ๋ํ, ๋ชจ๋ฐ์ผ ์ฌ์ฉ์๊ฐ ๋ฐฉ๋ฌธ ํ ๊ฒ์ผ๋ก ์์๋๋ ์ฃ์ง ์๋ฒ๋ก DNN ๋ ์ด์ด๋ฅผ ์ฌ์ ์ ๋ง์ด๊ทธ๋ ์ด์
ํ์ฌ ์ฝ๋ ์คํํธ ์ฑ๋ฅ์ ํฅ์์ํค๋ ๋ฐฉ์์ ์ ์ ํฉ๋๋ค. ์คํ ์์ค ๋ชจ๋น๋ฆฌํฐ ๋ฐ์ดํฐ์
์ ์ด์ฉํ ์๋ฎฌ๋ ์ด์
์์, DNN ๋ชจ๋ธ์ ๋ฐฐํฌํ๋ฉด์ ๋ฐ์ํ๋ ์ฑ๋ฅ ์ ํ๋ฅผ ์ ์ ํ๋ ๋ฐฉ์์ด ํฌ๊ฒ ์ค์ผ ์ ์์์ ํ์ธํ์์ต๋๋ค.Chapter 1. Introduction 1
1.1 Offloading Web App Computations to Edge Servers 1
1.2 Offloading DNN Computations to Edge Servers 3
Chapter 2. Seamless Offloading of Web App Computations 7
2.1 Motivation: Computation-Intensive Web Apps 7
2.2 Mobile Web Worker System 10
2.2.1 Review of HTML5 Web Worker 10
2.2.2 Mobile Web Worker System 11
2.3 Migrating Web Worker 14
2.3.1 Runtime State of Web Worker 15
2.3.2 Snapshot of Mobile Web Worker 16
2.3.3 End-to-End Migration Process 21
2.4 Evaluation 22
2.4.1 Experimental Environment 22
2.4.2 Migration Performance 24
2.4.3 Application Execution Performance 27
Chapter 3. IONN: Incremental Offloading of Neural Network Computations 30
3.1 Motivation: Overhead of Deploying DNN Model 30
3.2 Background 32
3.2.1 Deep Neural Network 33
3.2.2 Offloading of DNN Computations 33
3.3 IONN For DNN Edge Computing 35
3.4 DNN Partitioning 37
3.4.1 Neural Network (NN) Execution Graph 38
3.4.2 Partitioning Algorithm 40
3.4.3 Handling DNNs with Multiple Paths. 43
3.5 Evaluation 45
3.5.1 Experimental Environment 45
3.5.2 DNN Query Performance 46
3.5.3 Accuracy of Prediction Functions 48
3.5.4 Energy Consumption. 49
Chapter 4. PerDNN: Offloading DNN Computations to Pervasive Edge Servers 51
4.1 Motivation: Cold Start Issue 51
4.2 Proposed Offloading System: PerDNN 52
4.2.1 Edge Server Environment 53
4.2.2 Overall Architecture 54
4.2.3 GPU-aware DNN Partitioning 56
4.2.4 Mobility Prediction 59
4.3 Evaluation 63
4.3.1 Performance Gain of Single Client 64
4.3.2 Large-Scale Simulation 65
Chapter 5. RelatedWorks 73
Chapter 6. Conclusion. 78
Chapter 5. RelatedWorks 73
Chapter 6. Conclusion 78
Bibliography 80Docto
Predicting Temporal Aspects of Movement for Predictive Replication in Fog Environments
To fully exploit the benefits of the fog environment, efficient management of
data locality is crucial. Blind or reactive data replication falls short in
harnessing the potential of fog computing, necessitating more advanced
techniques for predicting where and when clients will connect. While spatial
prediction has received considerable attention, temporal prediction remains
understudied.
Our paper addresses this gap by examining the advantages of incorporating
temporal prediction into existing spatial prediction models. We also provide a
comprehensive analysis of spatio-temporal prediction models, such as Deep
Neural Networks and Markov models, in the context of predictive replication. We
propose a novel model using Holt-Winter's Exponential Smoothing for temporal
prediction, leveraging sequential and periodical user movement patterns. In a
fog network simulation with real user trajectories our model achieves a 15%
reduction in excess data with a marginal 1% decrease in data availability
Street Smart in 5G : Vehicular Applications, Communication, and Computing
Recent advances in information technology have revolutionized the automotive industry, paving the way for next-generation smart vehicular mobility. Specifically, vehicles, roadside units, and other road users can collaborate to deliver novel services and applications that leverage, for example, big vehicular data and machine learning. Relatedly, fifth-generation cellular networks (5G) are being developed and deployed for low-latency, high-reliability, and high bandwidth communications. While 5G adjacent technologies such as edge computing allow for data offloading and computation at the edge of the network thus ensuring even lower latency and context-awareness. Overall, these developments provide a rich ecosystem for the evolution of vehicular applications, communications, and computing. Therefore in this work, we aim at providing a comprehensive overview of the state of research on vehicular computing in the emerging age of 5G and big data. In particular, this paper highlights several vehicular applications, investigates their requirements, details the enabling communication technologies and computing paradigms, and studies data analytics pipelines and the integration of these enabling technologies in response to application requirements.Peer reviewe
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