Towards the Internet of Important Things TUHH - ComNets€¦ · ITU TTCN-32) used to test...
Transcript of Towards the Internet of Important Things TUHH - ComNets€¦ · ITU TTCN-32) used to test...
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
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Motivation
Transportation
Healthcare
Industry
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Motivation
Transportation
Healthcare
Industry
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Motivation
Transportation
Healthcare
Industry
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Motivation
Transportation
Healthcare
Industry
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Motivation
5G-PPP “Creating a secure, reliable and dependable Internet with a “zero perceived” downtime for services provision”
Transportation
Healthcare
Industry
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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%
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Modelling & Evaluation: Fault Trees and Block Diagrams
Remote Monitoring
Current Status
AND
AND
OR
? WLAN
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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
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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
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RUDP IP
Inmarsat
Wireless Channel
Source
IP
Inmarsat
RUDP IP
Sink
Internet
Air / Space Ground
Modelling & Evaluation: “Wireless Black Box” Example
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RUDP IP
Inmarsat
Wireless Channel
Source
IP
Inmarsat
RUDP IP
Sink
Internet
Air / Space Ground
Modelling & Evaluation: “Wireless Black Box” Example
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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
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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
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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
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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
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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
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Thank you! Questions?
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