-1cmqos provisioning in 0.4cmindustrial wireless sensor ... samuele zoppi | itg fachausschuss 5.2...

QoS Provisioning inIndustrial Wireless Sensor Networks

Chair of Communication NetworksDepartment of Electrical and Computer EngineeringTechnical University of Munich

Samuele Zoppi, H. Murat Gürsu, Wolfgang Kellerer

Chair of Communication NetworksTechnical University of Munich, Germany

Munich, 1st December 2017

Background

Next-generation industrial automation systems willbe wirelessly interconnected [HPO16].

Industrial NCS.

Samuele Zoppi | ITG Fachausschuss 5.2 workshop on “Cellular Internet of Things” | Munich, Germany 2

Chair of Communication Networks, Department of Electrical and Computer Engineering, Technical University of Munich

Background


Networked Control Systems (NCS): control loopsclosed over the network.

Industrial NCS.

Controller

Sensor Actuator

Plant

xk uk



Background



Stochastic LTI control system:

xk+1 = Axk +Buk +wk ,

uk =−Kxk ,

xk plant dynamic, uk control law.

Industrial NCS.

Controller

Sensor Actuator

Plant

xk uk



Background



Stochastic LTI control system:

xk+1 = Axk +Buk +wk ,

uk =−Kxk ,

xk plant dynamic, uk control law.

Sensor sends xk to the Controller.

Controller computes and sends uk to the Actuator.

Industrial NCS.

Controller

Sensor Actuator

Plant

xk uk



Background (2)

Inverted pendulum as benchmark NCS application.

Industrial NCS.

Controller

Sensor Actuator

Plant

xk uk



Background (2)


Sampling frequency: 20 Hz.

Industrial NCS.

Controller

Sensor Actuator

Plant

xk uk



Background (2)



Uplink traffic:

xk =

xkxkθk

θk

→ 256 bits @ 20Hz = 5 kbps.Industrial NCS.

Controller

Sensor Actuator

Plant

xk uk



Background (2)



Uplink traffic:

xk =

xkxkθk

θk

→ 256 bits @ 20Hz = 5 kbps.

Downlik traffic:

u→ 64 bits @ 20Hz = 1.25 kbps.

Industrial NCS.

Controller

Sensor Actuator

Plant

xk uk



Background (2)



Uplink traffic:

xk =

xkxkθk

θk

→ 256 bits @ 20Hz = 5 kbps.

Downlik traffic:

u→ 64 bits @ 20Hz = 1.25 kbps.

WSN (PHY IEEE 802.15.4) link→ 250 kbps.

Industrial NCS.

Controller

Sensor Actuator

Plant

xk uk



Motivation

Wireless Sensor Networks (WSN) can support NCS traffic.



Motivation


Control loops pose strict QoS requirements on wireless communications.



Motivation



WSN suffers from external interference and unreliable links [GVZK16].



Motivation




Problem: Current WSN lack dynamic real-time QoS provisioning.



Motivation




Problem: Current WSN lack dynamic real-time QoS provisioning.

Approach:

1. Definition of a QoS provisioning framework for IWSN.2. Implementation of the framework in a testbed.



Outline

Background & Motivation

QoS Provisioning Framework

Implementation

Conclusions & Further Work



Network Architecture

Centralized, star topology.

GW1 GW2

NMApp1 App2

s1 s2 s3

Network architecture.





Network elements:

1. Application (App): industrial NCS application

2. Network Manager (NM): manager of theNetwork Resources of the entire WSN

3. Gateway (GW): interface btwthe WSN devices, the NM and Apps

4. Sensor (s): WSN device

GW1 GW2

NMApp1 App2

s1 s2 s3






Network elements:

1. Application (App): industrial NCS application

2. Network Manager (NM): manager of theNetwork Resources of the entire WSN

3. Gateway (GW): interface btwthe WSN devices, the NM and Apps

4. Sensor (s): WSN device

Data links btw NM and WSN devicesthrough the GW.

Control links btw App and WSN devicesthrough the GW.

GW1 GW2

NMApp1 App2

s1 s2 s3




QoS Framework (1)

QoSRequirement

App

Scheduler /Radio Res. Manager

NM

LinkQuality Info.

GW

WSNDevice

S

t

f

Radio Resource Model

Control Data

Wireless DetServ



QoS Framework (1)

QoSRequirement

App


NM

LinkQuality Info.

GW

WSNDevice

S

t

f


Control Data

Wireless DetServ

Radio Resource Manager inputs:

1. QoS requirements from the application.2. QoS Model of the MAC radio resources.3. Link Quality Information of the radio resources.



QoS Framework (1)

QoSRequirement

App


NM

LinkQuality Info.

GW

WSNDevice

S

t

f


Control Data

Wireless DetServ

Radio Resource Manager inputs:

1. QoS requirements from the application.2. QoS Model of the MAC radio resources.3. Link Quality Information of the radio resources.

Radio Resource Manager outputs:

1. Radio resources for Data packets(application).

2. Radio resources for Control packets(schedules, LQI probes, ...).



QoS Framework (2)

Dynamic scheduling is possible in a TDMA-FDMAradio resource grid model. f

t

Beacon Data Probe

Schedules Link Info

Dynamic scheduling protocol.



QoS Framework (2)

Dynamic scheduling is possible in a TDMA-FDMAradio resource grid model.

Dynamic scheduling protocol:

1. Acquisition of Link Quality Information (input)→ estimated Packet Delivery Ratio→ EWMA for estimation

f

t

Beacon Data Probe

Schedules Link Info




QoS Framework (2)




2. Acquisition of QoS requirements (input)→ Target application reliability (i.e. 90%)→ Target delay bound (deadline)

f

t

Beacon Data Probe

Schedules Link Info




QoS Framework (2)




2. Acquisition of QoS requirements (input)→ Target application reliability (i.e. 90%)→ Target delay bound (deadline)

3. Distribution of new schedules (output)→ sequence of radio resources (time-freq. pairs)→ distributed using the beacon→ calculated with a reliability-based scheduler

f

t

Beacon Data Probe

Schedules Link Info




QoS Framework (3) - Scheduling algorithm

Reliability is provided allocating multiple transmissions in the frame.





The radio resources are modeled using a scheduling graph:

• Nodes represent time instants before/after time slots.

• Edges represent different frequencies and they are weighted by their PDR.

n0 n1 n2 n3 n4

γ0,0 = 0.1

γ0,1 = 0.7

γ0,2 = 0.1

γ0,3 = 0.6

γ0,4 = γs = 0

︸︷︷︸1st time slot

γ1,0 = 0.2

γ1,1 = 0.1

γ1,2 = 0.9

γ1,3 = 0.1

γ1,4 = γs = 0

︸︷︷︸2nd time slot

γ2,0 = 0.2

γ2,1 = 0.1

γ2,2 = 0.1

γ2,3 = 0.8

γ2,4 = γs = 0

︸︷︷︸3rd time slot

γ3,0 = 0.1

γ3,1 = 0.1

γ3,2 = 0.8

γ3,3 = 0.2

γ3,4 = γs = 0

︸︷︷︸4th time slot





The radio resources are modeled using a scheduling graph:

• Nodes represent time instants before/after time slots.

• Edges represent different frequencies and they are weighted by their PDR.

A Constrained Shortest Path scheduling algorithm finds the schedule (path)fulfilling the target reliability. → {(0,1) ,(1,3) ,(2,0)}

n0 n1 n2 n3 n4

γ0,0 = 0.1

γ0,1 = 0.7

γ0,2 = 0.1

γ0,3 = 0.6

γ0,4 = γs = 0

︸︷︷︸1st time slot

γ1,0 = 0.2

γ1,1 = 0.1

γ1,2 = 0.9

γ1,3 = 0.1

γ1,4 = γs = 0

︸︷︷︸2nd time slot

γ2,0 = 0.2

γ2,1 = 0.1

γ2,2 = 0.1

γ2,3 = 0.8

γ2,4 = γs = 0

︸︷︷︸3rd time slot

γ3,0 = 0.1

γ3,1 = 0.1

γ3,2 = 0.8

γ3,3 = 0.2

γ3,4 = γs = 0

︸︷︷︸4th time slot



QoS Framework (4) - Results

Simulation results of dynamic scheduling with latency and reliability constraints.

1 2500 5000 7500 10000 12500

Time [no frames]

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Avg

.n

et.

link

succ

ess

pro

bab

ility�

Freq. no : 1,6,11Ptx : -20 dBm

p : 1.0Freq. no : 1,6,11Ptx : -10 dBm

p : 1.0Freq. no : 1,6,11Ptx : 0 dBm

p : 1.0Freq. no : 1,6,11Ptx : 10 dBm

p : 1.0

10 40 70 100 130 160

Delays [ms]

0.0

0.2

0.4

0.6

0.8

1.0

EC

DF

0.900 0.905 0.910 0.915 0.920Application reliability

0.0

0.2

0.4

0.6

0.8

1.0

Network avg.

Delay bound

Network avg.

Target reliability

0

10

20

30

40

50

60

No

sup

por

ted

dev

icesI

22

0.37

Reliability-based scheduling [eaED].





WSN operating in a dynamic interference scenario (Wi-Fi APs, @2.4GHz).

1 2500 5000 7500 10000 12500

Time [no frames]

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Avg

.n

et.

link

succ

ess

pro

bab

ility�


p : 1.0Freq. no : 1,6,11Ptx : -10 dBm

p : 1.0Freq. no : 1,6,11Ptx : 0 dBm

p : 1.0Freq. no : 1,6,11Ptx : 10 dBm

p : 1.0

10 40 70 100 130 160

Delays [ms]

0.0

0.2

0.4

0.6

0.8

1.0

EC

DF


0.0

0.2

0.4

0.6

0.8

1.0

Network avg.

Delay bound

Network avg.

Target reliability

0

10

20

30

40

50

60

No

sup

por

ted

dev

icesI

22

0.37







Dynamic scheduling in presence of increasing Wi-Fi transmission power (Ptx).

1 2500 5000 7500 10000 12500

Time [no frames]

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Avg

.n

et.

link

succ

ess

pro

bab

ility�


p : 1.0Freq. no : 1,6,11Ptx : -10 dBm

p : 1.0Freq. no : 1,6,11Ptx : 0 dBm

p : 1.0Freq. no : 1,6,11Ptx : 10 dBm

p : 1.0

10 40 70 100 130 160

Delays [ms]

0.0

0.2

0.4

0.6

0.8

1.0

EC

DF


0.0

0.2

0.4

0.6

0.8

1.0

Network avg.

Delay bound

Network avg.

Target reliability

0

10

20

30

40

50

60

No

sup

por

ted

dev

icesI

22

0.37







Dynamic scheduling in presence of increasing Wi-Fi transmission power (Ptx).

1 2500 5000 7500 10000 12500

Time [no frames]

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Avg

.n

et.

link

succ

ess

pro

bab

ility�


p : 1.0Freq. no : 1,6,11Ptx : -10 dBm

p : 1.0Freq. no : 1,6,11Ptx : 0 dBm

p : 1.0Freq. no : 1,6,11Ptx : 10 dBm

p : 1.0

10 40 70 100 130 160

Delays [ms]

0.0

0.2

0.4

0.6

0.8

1.0

EC

DF


0.0

0.2

0.4

0.6

0.8

1.0

Network avg.

Delay bound

Network avg.

Target reliability

0

10

20

30

40

50

60

No

sup

por

ted

dev

icesI

22

0.37


WDetServ guarantees reliability and delay bounds reacting against interference.



Outline



Implementation




Implementation (1)

Deployment of an WDetServ NCS testbed:

1. Control logic (Controller) in the Cloud.

2. Sensing and Actuation in the WSN devices.

3. Gateway acts as forwarding entity.

4. Inverted Pendulum as benchmark controlapplication.

GW1 GW2

NMApp1 App2

s1 s2 s3



Implementation (2)

Problem: several HW and SW latency bottlenecks.

Z1 min OM min Z1 max OM max

Setup

0

5

10

15

Sensorto

Con

trollerDelay

(ms)

L4 / L3

Tx MAC

SPI

Transmission

SPI

Radio

Rx MAC

UART

UART to USB

USB

Cloud USB Cont.

Cloud

Sensor-to-cloud delay measurements[GZO+].



Implementation (2)

Problem: several HW and SW latency bottlenecks.

Solution: ad-hoc HW solutions for GW and WSN:• Gateway

high perf., multi-radio, multi-processor−→ low-latency, multi-channel SDR

• Sensorlimited perf., single antenna, single processor−→ Zolertia Z1/RE-Mote, TI SimpleLink

Z1 min OM min Z1 max OM max

Setup

0

5

10

15

Sensorto

Con

trollerDelay

(ms)

L4 / L3

Tx MAC

SPI

Transmission

SPI

Radio

Rx MAC

UART

UART to USB

USB

Cloud USB Cont.

Cloud

Sensor-to-cloud delay measurements[GZO+].



Outline



Implementation




Conclusions

NCS traffic can be supported by WSN if QoS provisioning is implemented.



Conclusions


Wireless DetServ provides the building blocks for QoS provisioning (latency, reliability, QoC, ...)in WSN.



Conclusions



The implemented reliability-based scheduler is able to react to changes in the wireless environment.



Conclusions



The implemented reliability-based scheduler is able to react to changes in the wireless environment.

Latency is the major issue for HW implementation (radio, processing, ext. interface).



Further Work

Measurements of NCS Inverted Pendulum operating over the testbed will be performed.

NCS cross-layer scheduling algorithms will be developed.

Different Link Quality Estimators will be evaluated in the testbed.

Multi-radio, multi-processor, high-speed interface solutions will be implemented.



ReferencesSamuele Zoppi et al. Reliability-based scheduling for delay guarantees in hybrid wired-wirelessindustrial networks. IEEE Transactions on Industrial Informatics, SUMBITTED.

M. Gürsu, M. Vilgelm, S. Zoppi, and W. Kellerer. Reliable co-existence of 802.15.4e TSCH-basedWSN and Wi-Fi in an aircraft cabin. In 2016 IEEE International Conference on CommunicationsWorkshops, pages 663–668, May 2016.

Halit Murat Gürsu, Samuele Zoppi, Hasan Yagiz Ozkan, Yadhunandana R. K., and WolfgangKellerer. Tactile sensor to cloud delay: A hardware and processing perspective. In IEEE ICC 2018SAC Symposium Internet of Things Track (ICC’18 SAC-6 IoT), SUBMITTED.

Mario Hermann, Tobias Pentek, and Boris Otto. Design principles for industrie 4.0 scenarios. InSystem Sciences (HICSS), 2016 49th Hawaii International Conference on, pages 3928–3937.IEEE, 2016.



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