5G and edge computing in railways: applications and challenges (IN2DREAMS project)

Stefanos Gogos & Markos Anastasopoulos

12-13 September 2019 International Workshop ICT for RAILWAYS - Edition 5 - Naples - IT 1

5G and edge computing in railways: applications and challenges (IN2DREAMS project)

12-13 September 2019 International Workshop ICT for RAILWAYS - Edition 5 - Naples - IT 2

Stefanos Gogos

Technical Affairs Manager

UNIFE

Joined UNIFE in October 2013. Hestarted his career as a StructuralEngineer, following his degree inCivil Engineering with Architecture

Markos Anastasopoulos

Senior Research Associate

University of Bristol

Researcher at the HPN Group, University ofBristol, U.K. Co-author of over 90 papers ininternational journals and conferences.Recipient of the Best Doctoral DissertationAward and the Best Paper Award at the17th IEEE/IFIP ONDM Conference.

▪ 5G systems▪ Motivation: 5G Vision▪ Overview of 5G architectures: The

IN2DREAMS solution▪ IN2DREAMS Building blocks (Multi-

technology access, HeterogeneousTransport Network InterconnectingCompute Resources with Remote RadioUnits)

▪ Vertical use case: Railway systems▪ Current status▪ 5G for railways▪ Use case: Smart metering for railway

systems over 5G Networks

▪ Conclusions

3

Content Outline

▪ IN2DREAMS intro

▪ Project partners

▪ Challenge & Scope

▪ High-level objectives

▪ Project structure

▪ Links with S2R

4

Project Partners

• Predicted growth of transport, especially in European railwayinfrastructures, is expected to introduce a dramatic increase infreight and passenger services by the end of 2050.

• To support sustainable development of these infrastructures, noveldata-driven ICT solutions are required.

• These will enable monitoring, analysis and exploitation of energyand asset information for the entire railway system including powergrid, stations, rolling stock and infrastructure.

• IN2DREAMS addressed these challenges through two distinct workstreams: Work Stream 1 (WS1), focusing on the management ofenergy-related data and Work Stream 2 (WS2), focusing on themanagement of asset-related data

Challenge & Scope

5

• Work Stream 1 – Management of Energy-related Data

• WS1 aimed at removing thecurrent and anticipated limitationsof REMS, by making these capableof supporting a much wider arrayof requirements than it is currentlythe case.

• Work Stream 2 –Management of Asset-related

Data

• WS2 aimed at improvingefficiency and sustainability ofthe railway asset datamanagement, by applyingresearch advances in machinelearning, data visualization anddecentralized architecturewith smart contracts.

High-level Objectives

6

7

Project Structure

WP1: Project Management & Technical CoordinationW

P6:

Use

r A

pp

licat

ion

WP

4:

Smar

t C

on

trac

ts f

or

Rai

lway

Dat

a Tr

ansa

ctio

ns

WP2: Integrated Communication Platform

WP7: Dissemination, Exploitation & Cooperation with S2R

WP3 WP5

Sensing Monitoring

Devices

Knowledge Extraction from Railway Asset Data

Data Management

Platform

WS1 WS2

8

Links with Shift2Rail

9

Join us at the IN2DREAMS final conference!

Where: MilanWhen: 2nd October 2019

To register keep an eye on our public website:

www.in2dreams.eu

▪New business opportunities for a large variety of use cases – ICT & Verticals

▪5G Networks to be future proof: designed to evolve and not to be replaced

10

Motivation - 5G Vision

• Provides the necessary processing power for computational

intensive tasks AI, ML, NN etc

• Supports delay sensitive services (i.e. using MEC)

• Multi-technology networks for

access and transport

• Improved efficiency, capacity

etc

• Common Platform for ALL services

• Efficient resource sharing and multi-tenancy

• Multiplexing of

Mission Critical

Services with

Passenger services

• Multiplexing of

Telecom with end

user services

• Replacement of costly equipment

with general purpose devices

• Paradigm shift from Network

Entities to Network Functions

11

Overall Architecture and Building Blocks

Sensing/Monitoring Devices

Integrated Communication Platform

Operational Data Management

Platform

12

Building blocks (I)

• Sensing/Monitoring Devices and Data Management Platform

◦ Interconnecting a variety of sensing andmonitoring devices providing on-boardand track side energy measurements.

◦ Supporting applications related tosurveillance, observation, monitoring ofenvironmental parameters (temperature,CO2, humidity, noise), energy monitoring(voltage, current), localization andenvironmental parameters.

12

13

Building blocks (II)

Integrated Communication Platform

• Based on a heterogeneous secure andresilient telecommunication platform,consisting of both wireless (e.g. LongTerm Evolution – LTE, WiFi, LiFI) andwireline (e.g. optical) systemsconverging energy and telecomservices.

• This infrastructure interconnects aplethora of monitoring devices andend-users to the OSS.

• Control and management of theintegrated network infrastructureusing an open SDN-based networkmanagement framework

14

Building blocks (III)

ODM Platform

ODM platform offering Scalable Data Collection, Aggregation, Processing and Interfaces offering the following features:

• Operation in accordance to the cloudcomputing paradigm

• Secure access to data based on a provenuser management system

• Hybrid scalable data storage mechanismsbased on open source SQL/Non-SQL DBs

• Large-scale data processing engine toexecute machine learning, (de)compression algorithms or extractinganalytics data.

• Multi-protocol support: MQTT, AMQP,Sockets, etc).

• Synchronization

Basic Network Segments of railway systems• ON_BOARD

• ON_BOARD TO TRACKSIDE

• TRACKSIDE TO OPERATIONS CONTROL CENTER

15

Communication Networks for railways

▪#1 On-board segment: Multiple-application specific networks• Increased Operational/Maintenance costs

16

Railway Communication Systems: Challenges

Source: http://www.questertangent.com/solutions/train-communication-networks/

▪#2 On-board to trackside: based on 3G/4G for passengers and GSM-R for operators

• Service disruptions, non-guaranteed QoS across the track

• Example: User 1 (2) associated with provider 1 (2)• Provider A: High SNR, region A. Provider B: High SNR, region B

17

Railway Communication Systems: Challenges

Rece

ived

si

gnal

Provider A Provider B

User 1

User 2

▪#3 On-board-to-trackside (cont)• Inefficient use of resources i.e., highly

underutilized BBU resources, especially in rural environments, low bandwidth (for GSM-R in the order of kbps)

18

Railway Communication Systems: Challenges

BW

BW

BBU1 BBU2

BBU1 capacity

BBU2 capacity

▪#4 Trackside-to-operationscontrol center

• Vendor Specific i.e.

https://www.globalrailwayreview.com/whitepape

r/27717/mission-critical-communications-

networks-railway-operators/

▪#1: Common platform for operational and user services• Achieved through infrastructure slicing and virtualization

19

5G-Applications – Sharing of ON_BOARD services

Data

Voice Operational Data

CCTVFleet ManagementVOICEPassenger Information

VOICE

CBTC

TETRA

IEEE 802.11

Radio

CBTC

TETRA

IEEE 802.11

Radio

5G-PICTURE

c)

Service Tags:• VLAN 10: Internet Access and file transfers (Passengers services)

• VLAN 11: CCTV (Railway services)

• VLAN 12: Management

Transport Tags:• VLAN 80: Only on-board. Between the on-board FlowBlaze and

the Rear Antenna of each TN201-LC unit (one VLAN 80 per unit)

• VLAN 81: Only on-board. Between the on-board FlowBlaze and the Front Antenna of each TN201-LC unit (one VLAN 81 per unit)

• VLAN 100-107: Between the Martorell FlowBlaze and each of the eight DN101-LC trackside antennas (one per antenna)

• VLAN 1: To avoid loops

20

Field Validation

21

Demo (II)

▪#2 : Integrated multi-technology on-board and trackside network -- ofmmwave technology

▪Coordination of the remote antennas (RU) adopting the C-RANparadigm

22

5G-Applications –ON_BOARD to TRACKSIDE

AP

mmWave Links

Server

Fiber

a)

AP

mmWave Links

Server

Fiber

a)

23

Field Trial (1)

Antenna Module

(on the train roof)

▪ On-board elements, mmWave antennas

Field Trial (2)

24

Field Trial (2)

• Demo location

25

Field Trial (3)

240V

ACcabinet

48V

DC

cabinet

48V

DC

240V

AC

Fibre

pair

cabinet

48V

DC

Olesa de

Monserr

at

stationcabinet

240 m

Fibre

Cabinet: holds

AC-DC adapter

and remote (via

SMS) power

switch; also

includes four

passive WPON

add/drop filter

modules

Abrera

station

T1 T2 T3 T4

280.15

m

515.35 m

617.12 m

Fibre

pair

▪ Ground elements

▪#3: Integrated multi-technology ground infrastructure (Lab validation@Millennium Square Bristol)

26

5G-Applications – Multi-technology access and transport

• #4 Energy Metering and Optimization

• Objective: Fast and reliable monitoring, analysis and optimization ofthe whose railway system including the railway electrificationsystem, the rolling stock and the track.

27

5G-Applications – Softwarization and Optimization

Internet

Optical Transport

Metro/Core OpticalEPC

P-S GW/GSN

PDN-GW

Wireless Access/Transport

LTE Macro cell

Data Centers

Power Grid

Railway Electrification

Substation

Control Center

• Adopting the concept of Mobile Edge Cloud (MEC)

• Efficient coordination of disaggregated compute resources• Collaborative edge-central cloud data processing → reduced latency

28

Proposed Solution

Mobile Network Operator #1 Mobile Network Operator #2

LTE

z

LTE LTE WiFi

Multi-technology Access Network

Metro/Core Optical

Heterogeneous Transport Network

EventsWeather

Facilities

Sensors

Devices

MQTT, AMQP, STOMP

Rest APIs CoAP

Storage Processing

Analytics

IoT Data Management Platform

1

2

Data Centers

3

Data Centers

MQ

TT

Co

ntro

l Server

Stora

ge

Data

NN-LEARNING

Server

Server

4

Edge Processing

4

29

Field Trials

▪REIMS Tramway▪ Step 1: On-board and trackside datacollection

▪ Step 2: Transmission of data to thecentral cloud

▪ Step 3: Knowledge extraction based onNeural Networks

▪ Step 4: Push the trained Neural networksat the edge to enable real time optimaldecision making

Mobile Network Operator #1 Mobile Network Operator #2

LTE

z

LTE LTE WiFi

Multi-technology Access Network

Metro/Core Optical

Heterogeneous Transport Network

EventsWeather

Facilities

Sensors

Devices

MQTT, AMQP, STOMP

Rest APIs CoAP

Storage Processing

Analytics

IoT Data Management Platform

1

2

Data Centers

3

Data Centers

MQ

TT

Co

ntro

l Server

Stora

ge

Data

NN-LEARNING

Server

Server

4

Edge Processing

4

@Network Rail

▪ Use case: Fault detection over highfrequency sampling at substations

▪@REIMS

▪ Fault detection in an operationaltramway system

30

• Synchronization of measurements

•

• Positioning (Without the need of GPS) using telecom network

• In-tunnel positioning using LiFi

31

Applications

• Estimation of trackside fromon-board measurements(software sensors usingpurposely developed NNmodels)

• Energy forecasting using LSTMNNs

• Optimization (optimal drivingprofiles) (using Machinelearning Techniques)

32

Applications

• Enhanced mobile broadband under high speed mobility in Rail environments• eMBB functionality through heterogeneous technology access for on-

board network connectivity in a railway setup

• Interconnection of on-board devices with the trackside and the trackside with the core network

33

Next steps and Large-Scale Demos

5G CTORI

• Objective: common platform forMission Critical (MC) voice and videoand other MC rail-related data andsignalling services addressing on-board and trackside elements.• a softwarized end-to-end MC

services solution that will enable theenhanced support of railway specificservices

• A softwarized MC solution for railenvironments enabling control andmanagement of the on-boardelements and trackside components(i.e. interlockings)

34

Next steps and Large-Scale Demos

5G CTORI