1 06.10.2015

Reliable Wireless Sensor Networks Towards the Internet of Important Things

TUHH - ComNets

Dr. Maciej Mühleisen, Prof. Andreas Timm-Giel

2 06.10.2015

Motivation

Selected Ongoing R&D Topics

Modelling & Evaluation

Proposed Reliable WSN

Conclusion & Future Research

Outline

3 06.10.2015

Motivation

Transportation

Healthcare

Industry

4 06.10.2015

Motivation

Transportation

Healthcare

Industry

5 06.10.2015

Motivation

Transportation

Healthcare

Industry

6 06.10.2015

Motivation

Transportation

Healthcare

Industry

7 06.10.2015

Motivation

5G-PPP “Creating a secure, reliable and dependable Internet with a “zero perceived” downtime for services provision”

Transportation

Healthcare

Industry

8 06.10.2015

Full Mode 95 %

Enhanced Mode 99 %

Selected Ongoing R&D Topics

EU Project METIS: Ultra-Reliable Communication (URC)

Dedicated spectrum & robust PHY-mechanisms Cooperation among multiple Radio Access Technologies (RATs)

Reliable service composition through cross-layer information exchange Availability Estimation and Indication (AEI) of Reliable Transmission Links

(RTLs) [1]

[1] H. D. Schotten et al. “Availability Indication as Key Enabler for Ultra-Reliable Communication in 5G”, European Conference on Network and Communications (EuCNC) 2014

[2] P. Popovski, “Ultra-Reliable Communication in 5G Wireless Systems”, 1st International Conference on 5G for Ubiquitous Connectivity, 2014

[2]

Basic Mode 99.99%

9 06.10.2015

Modelling & Evaluation: Fault Trees and Block Diagrams

Remote Monitoring

Current Status

AND

AND

OR

? WLAN

10 06.10.2015

Modelling & Evaluation: Fault Trees and Block Diagrams

Remote Monitoring

Current Status

Medical Personel on Duty

Alarm Signal

AND

AND

OR

? WLAN

11 06.10.2015

Modelling & Evaluation: Fault Trees and Block Diagrams

Remote Monitoring

Current Status

Medical Personel on Duty

Alarm Signal

AND

AND

OR

WLAN

?

P(„working“) = P(WLAN,not(?)) + P(not(WLAN), ?) + P(WLAN, ?) The math is simple, even correlation possible But where to get the values from?

? WLAN

12 06.10.2015

Modelling & Evaluation: Fault Trees and Block Diagrams

Remote Monitoring

Current Status

Medical Personel on Duty

Alarm Signal

AND

Long Distance

Com.

AND

OR

WLAN

?

P(„working“) = P(WLAN,not(?)) + P(not(WLAN), ?) + P(WLAN, ?) The math is simple, even correlation possible But where to get the values from?

? WLAN

13 06.10.2015

Modelling & Evaluation: Fault Trees and Block Diagrams

Remote Monitoring

Current Status

Medical Personel on Duty

Alarm Signal

AND

Long Distance

Com.

AND

OR

WLAN

?

P(„working“) = P(WLAN,not(?)) + P(not(WLAN), ?) + P(WLAN, ?) The math is simple, even correlation possible But where to get the values from?

? WLAN

𝜆𝜆𝐺𝐺𝐺𝐺 𝜆𝜆𝐺𝐺𝑌𝑌

𝜇𝜇𝐺𝐺𝐺𝐺

14 06.10.2015

Modelling & Evaluation: SDL & TTCN-3

ITU SDL1) allows to model communication systems Discrete signals model incoming and outgoing packets

ITU TTCN-32) used to test communication systems Newest version TTCN-3-2014 supports “Continuous Signals” to test

physical components [1] TTCN-3 tests can be automatically generated from SDL models (but

not yet for TTCN-3-2014 ) We have theoretically closed the

gab without changing SDL

[1] J. Großmann „Testing Hybrid Systems with TTCN-3“, PhD thesis, Technischen Universität Berlin, 2014 1) Specification and Description Language 2) Testing and Test Control Notation Version 3

15 06.10.2015

RUDP IP

Inmarsat

Wireless Channel

Source

IP

Inmarsat

RUDP IP

Sink

Internet

Air / Space Ground

Modelling & Evaluation: “Wireless Black Box” Example

16 06.10.2015

RUDP IP

Inmarsat

Wireless Channel

Source

IP

Inmarsat

RUDP IP

Sink

Internet

Air / Space Ground

Modelling & Evaluation: “Wireless Black Box” Example

17 06.10.2015

Modelling & Evaluation: “Wireless Black Box” Example Signal Flow Graphs (SFGs) allow to systematically evaluate the delay

distribution of many networks SFGs allow Markovian transmission channels

Source Sink

(1 - eInet)GE2E(z)

eInetGE2E(z) GNACK(z)

Source Sink

[1] M. Mühleisen, M. Venzke, C. Petersen, A. Timm-Giel, V. Turau, “Reliable Transmission of Aircraft Data”, 5th International Workshop on Aircraft System Technologies (AST 2015)

[2] Y. Zang, “Analysis of CSMA Based Broadcast Communication in Vehicular Networks with Hidden Stations”, PhD thesis, RWTH Aachen, 2015

18 06.10.2015

Modelling & Evaluation: “Wireless Black Box” Example Signal Flow Graphs (SFGs) allow to systematically evaluate the delay

distribution of many networks SFGs allow Markovian transmission channels

10-4 10-3 10-2 10-1 100500

1000

1500

2000

2500

3000

3500

4000

4500

5000

eSat

(Tai

l) D

elay

[ ∆T]

1-10-6 - Percentile; eInet = 0

1-10-4 - Percentile; eInet = 0

1-10-2 - Percentile; eInet = 0Average; eInet = 0

1-10-6 - Percentile; eInet = 10-6

1-10-4 - Percentile; eInet = 10-6

1-10-2 - Percentile; eInet = 10-6

Average; eInet = 10-6

19 06.10.2015

Modelling & Evaluation: Result Confidence Student-t confidence intervals (CI) are for mean values only! Percentiles are “yes-no” decisions Binomial Proportional CIs for

uncorrelated measurements Limited Relative Error Algorithm (LRE) can determine the confidence of a

percentile even if measurements are correlated

0 5 10 15x 104

0

200

400

600

800

1000

1200

Simulation Time [s]

Wai

ting

Tim

e w

[s]

[1] F. Schreiber and C. Görg “Stochastic Simulation: A Simplified LRE-AIgorithm for Discrete Random Sequences”

20 06.10.2015

Modelling & Evaluation: Result Confidence Student-t confidence intervals (CI) are for mean values only! Percentiles are “yes-no” decisions Binomial Proportional CIs for

uncorrelated measurements Limited Relative Error Algorithm (LRE) can determine the confidence of a

percentile even if measurements are correlated

0 5 10 15x 104

0

200

400

600

800

1000

1200

Simulation Time [s]

Wai

ting

Tim

e w

[s]

300

0

900

wMax

600

[1] F. Schreiber and C. Görg “Stochastic Simulation: A Simplified LRE-AIgorithm for Discrete Random Sequences”

21 06.10.2015

Modelling & Evaluation: Result Confidence Student-t confidence intervals (CI) are for mean values only! Percentiles are “yes-no” decisions Binomial Proportional CIs for

uncorrelated measurements Limited Relative Error Algorithm (LRE) can determine the confidence of a

percentile even if measurements are correlated

0 5 10 15x 104

0

200

400

600

800

1000

1200

Simulation Time [s]

Wai

ting

Tim

e w

[s]

300

0

900

wMax

6000 1000 2000 300010-5

10-4

10-3

10-2

10-1

100

Waiting Time w [s]

P(x

< w

)

0 1000 2000 30000

0.01

0.02

0.03

0.04

0.05

Waiting Time w [s]R

elat

ive

Erro

r dG

(w)

[1] F. Schreiber and C. Görg “Stochastic Simulation: A Simplified LRE-AIgorithm for Discrete Random Sequences”

22 06.10.2015

Modelling & Evaluation: Result Confidence Student-t confidence intervals (CI) are for mean values only! Percentiles are “yes-no” decisions Binomial Proportional CIs for

uncorrelated measurements Limited Relative Error Algorithm (LRE) can determine the confidence of a

percentile even if measurements are correlated

0 5 10 15x 104

0

200

400

600

800

1000

1200

Simulation Time [s]

Wai

ting

Tim

e w

[s]

300

0

900

wMax

6000 1000 2000 300010-5

10-4

10-3

10-2

10-1

100

Waiting Time w [s]

P(x

< w

)

0 1000 2000 30000

0.01

0.02

0.03

0.04

0.05

Waiting Time w [s]R

elat

ive

Erro

r dG

(w)

[1] F. Schreiber and C. Görg “Stochastic Simulation: A Simplified LRE-AIgorithm for Discrete Random Sequences”

23 06.10.2015

Proposed Reliable WSN: Redundant & Adaptive

Redundant: PHY: Multi channel/band/frequency MAC: (H)ARQ

Network: Multi path/route for wireless and fixed Application: Availability aware, virtual

(migration) Adaptive:

PHY: Code-rate vs. packet error rate MAC: CSMA & TDMA with optimized slot

allocation also for (H)ARQ Network: Coordinated channel switching,

optimized TDMA slot allocation

Application: network state aware

Distributed cross-layer information on current state is a key challenge Minimize Time to Failure & Time to Repair on all layers

S

Route 1

Route 2

24 06.10.2015

Proposed Reliable WSN: Redundant & Adaptive

Redundant: PHY: Multi channel/band/frequency MAC: (H)ARQ

Network: Multi path/route for wireless and fixed Application: Availability aware, virtual

(migration) Adaptive:

PHY: Code-rate vs. packet error rate MAC: CSMA & TDMA with optimized slot

allocation also for (H)ARQ Network: Coordinated channel switching,

optimized TDMA slot allocation

Application: network state aware

Distributed cross-layer information on current state is a key challenge Minimize Time to Failure & Time to Repair on all layers

S

Route 1

Route 2

25 06.10.2015

Proposed Reliable WSN: Redundant & Adaptive

Redundant: PHY: Multi channel/band/frequency MAC: (H)ARQ

Network: Multi path/route for wireless and fixed Application: Availability aware, virtual

(migration) Adaptive:

PHY: Code-rate vs. packet error rate MAC: CSMA & TDMA with optimized slot

allocation also for (H)ARQ Network: Coordinated channel switching,

optimized TDMA slot allocation

Application: network state aware

Distributed cross-layer information on current state is a key challenge Minimize Time to Failure & Time to Repair on all layers

S

Route 1

Route 2

26 06.10.2015

Proposed Reliable WSN: Redundant & Adaptive

Redundant: PHY: Multi channel/band/frequency MAC: (H)ARQ

Network: Multi path/route for wireless and fixed Application: Availability aware, virtual

(migration) Adaptive:

PHY: Code-rate vs. packet error rate MAC: CSMA & TDMA with optimized slot

allocation also for (H)ARQ Network: Coordinated channel switching,

optimized TDMA slot allocation

Application: network state aware

Distributed cross-layer information on current state is a key challenge Minimize Time to Failure & Time to Repair on all layers

S

Route 1

Route 2

27 06.10.2015

Conclusion & Future Research

There is a clear idea how to improve reliability in (wireless) networks Evaluating reliability by means of analysis, simulation and testbeds is

still an open research topic Sufficiently detailed traffic-, mobility- and channel models “Worst case” models Reusable mathematic frameworks How to shorten long time experiments?

Specifying, developing, testing and certifying highly reliable networks for safety critical applications takes time

Can we (further) combine Markov Models from communication with

the ones from reliability analysis? Reusable reliability assessment framework No need to be significantly more reliable than the other system components

28 06.10.2015

Thank you! Questions?

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