http://SHIFT.shanghaitech.edu.cn
Multi-tier Computing Networks
for Intelligent IoT Services
Professor Yang Yang
SHIFT, ShanghaiTech University
Fall 2019 OpenAirInterface Workshop
Beijing, China, 3-5 Dec 2019
Introduction
Multi-tier Computing Networks
Open and Shared Computing Resources for
• Wireless Channel Modeling
• Robot SLAM
• Robot Rescue
• Multi-user Task Scheduling
One More Thing …
Contents
AI is Everywhere
Google (2017): DeepDream: The Art of Neural Network
Microsoft (2017): The Next Rembrandt
Obvious (2018): Edmond de Belamy
Machine Learning in 5G
Machine Learning Paradigms for Next-Generation Wireless Networks, IEEE Wireless Communications, Apr. 2017.
Machine Learning for 5G RRM
A Deep-Learning-Based Radio Resource Assignment Technique for 5G Ultra Dense Networks, IEEE Network, Dec. 2018.
Gartner: 20.4 billion connected things by 2020
4 billion connected people
People-Centric Network IoT Network
图片来自互联网
Yesterday
Data Sensing
Today
Information Processing
Tomorrow
Knowledge Creation
More and More Intelligent IoT Services
NetworksBig Dataat Things and Edges
Delay Requirements
Devices That Require Local
Support
Network Connectivity
Network Bandwidth
Cloud-Edge-ThingService/App Interoperability
IT-OT-CTConvergence
HorizontalService/App Interoperability
Courtesy of Tao Zhang
Cloud Alone Cannot Support AI Everywhere
FA2ST: Fog as a Service Technology, IEEE Communications Magazine, Oct. 2018.
Multi-tier Computing Networks for Intelligent IoT, Nature Electronics, Jan. 2019.
Multi-tier Computing Networks
Cloud, Fog, Edge and Things
FA2ST: Fog As A Service Technology
Fog-enabled Intelligent IoT Services
Comparison of Cloud-based and
Fog-based IoT Applications
V3
V1
V2
SDN-based Fog Nodes
Master Fog Nodewith GPU
Slave FN
Slave FN Slave FN
• Each Fog Node is equipped Hadoop, Spark, and TensorFlow
• The fog nodes form an AI tree topology, performing distributed AI computing
Fog Node = Communication + Computing+ Storage + AI Algorithms
Introduction
Multi-tier Computing Networks
Open and Shared Computing Resources for
• Wireless Channel Modeling
• Robot SLAM
• Robot Rescue
• Multi-user Task Scheduling
One More Thing …
Contents
17
Wireless Channel Modeling
• Channel modelling
○ The fifth generation (5G) wireless communication systems
machine-to-machine, device-to-device, and vehicle-to-vehicle communications,
have more application scenarios for vertical industries, such as enhanced mobile broadband (eMBB), massive machine type communications (mMTC), and ultra-reliable and low-latency communications (URLLC)
○ Accurate channel models
understand the exact physical impacts of different wireless channels on transmitted radio signals,
design and deploy effective and feasible communication technologies for different propagation channels in real application environments.
• Stored Channel Impulse Responses: Channel Sounder – Measure channel parameters: Direction of Departure (DOD), Direction of
Arrival (DOA), Time delay, Doppler shift generate Channel Impulse Response (CIR)
– Expensive, time-consuming, environment-dependent, not flexible
• Deterministic Channel Models: Ray-Tracing Techniques– Simulate reflection, diffraction, refraction, and scattering by using channel
parameters in exact communication environments and the propagation law of electromagnetic waves.
– Unrealistic assumptions, environment-dependent, not flexible
• Stochastic Channel Models: Geometry-based Stochastic Channel Model– Use the laws of reflection, diffraction, and scattering of electromagnetic waves
in an environment of many scatterers under a certain distribution reproduce stochastic characteristics of different wireless channels over time, frequency, and space.
– Very complex, difficult to analyze, time-consuming
Traditional Channel Modeling Methods
Requirement: in-depth domain-specific knowledge and technical expertise in radio signal propagation across electromagnetic fields
• Machine Learning techniques are very effective in approximating arbitrary functions and hidden features.
• Fog/edge computing technologies support regional/local environments with very relevant measurement data, system parameters, and network resources.
GAN-Based Wireless Channel Modeling
Minimize the need for domain-specific knowledge and technical expertise in wireless communications and signal propagation.
GAN-based Wireless Channel Modeling Framework
• Example: AWGN Channel
• Mean: 4, Standard Deviation: 0.5
GAN-Based Wireless Channel Modeling
Beginning of the Training Process
Key Parameters
Generative Adversarial Network-based Wireless Channel Modelling, IEEE Communications Magazine, Mar. 2019.
• Example: AWGN Channel
• Mean: 4, Standard Deviation: 0.5
GAN-Based Wireless Channel Modeling
Without MinibatchDiscrimination
With MinibatchDiscrimination
①Initialize visual odometer
② Collect sensor data and make front end
processing
③Report new key-frame streams
④ Optimize the map and make other back
end processing
⑤Update poses
A. Report new key-frame streams
B. Store data and merge maps from different
robots
Fog NetworkCloud End
Robots
Simultaneous Localization and Mapping (SLAM): a robot in an a priori unknown environment and tries to build a map of the environment and also locate itself within the map simultaneously.
Robot SLAM
Robot SLAM
Fig. A heterogeneous fog network with 4 TNs, 4 HNs and 3 BNs.
A computation
task can be
executed by
its owner, i.e.,
the local TN.
A computation task
can be entirely
offloaded to one
neighbor HN.
A HN can
accommodate
multiple tasks.
POMT: Paired Offload of Multiple Tasks
Problem Formulation
Every TN wants to minimize the delay of its own task, however there existscompetition among them for the communication resources and computation capabilities of HNs.
-𝑎𝑛0 ,𝑎𝑛
𝑘 : the offloading decision indicator.
-𝒂𝑛: offloading decision vector.
- 𝑨−𝑛: offloading decision vectors of other TNs except 𝑛.
-𝑏𝑛𝑘: the connectivity indicator between TNs and HNs.
Main Contributions
POMT game: model the competition between TNs.
Existence of Nash Equilibrium (NE): by proving the POMT game is a potential game.
POMT algorithm: a distributed algorithm for achieving an NE.
The performance of POMT is comparable with the optimal solution in terms of system average delay.
A new metric, namely delay reduction ratio (DRR), to evaluate the performance of computation offloading.
DRR: 𝑇𝑛0−𝑇𝑛 𝑎𝑛,𝑨−𝑛
𝑇𝑛0
• Algorithms: POMT, Optimal, Random, All, Local Execution
Average Delay and Beneficial Task Nodes
DDR: Delay Reduction Ratio
Screen: Journey
Sensor: Position
and Speed
Projector: Interactive
Experience
One more thing: Art Installation
• Multi-tier Computing Networks for Intelligent IoT, Nature Electronics,
Jan. 2019.
• DOTS: Delay-Optimal Task Scheduling among Voluntary Nodes in
Fog Networks, IEEE Internet of Things Journal, Dec. 2018.
• DATS: Dispersive Stable Task Scheduling in Heterogeneous Fog
Networks, IEEE Internet of Things Journal, Dec. 2018.
• FEMTO: Fair and Energy-Minimized Task Offloading for Fog-Enabled
IoT Networks, IEEE Internet of Things Journal, Dec. 2018.
• FA2ST: Fog as a Service Technology, IEEE Communications
Magazine, Nov. 2018.
• MEETS: Maximal Energy Efficient Task Scheduling in Homogeneous
Fog Networks, IEEE Internet of Things Journal, Oct. 2018.
• DEBTS: Delay Energy Balanced Task Scheduling in Homogeneous
Fog Networks, IEEE Internet of Things Journal, Jun. 2018.
• FEMOS: Fog-Enabled Multi-tier Operations Scheduling in Dynamic
Wireless Networks, IEEE Internet of Things Journal, Apr. 2018.
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