OC Colloquium DFG SPP 1183 11th Colloquium October 07 th 2010 Munich - - PowerPoint PPT Presentation

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Technology for Pervasive Computing OC Colloquium DFG SPP 1183 11th Colloquium October 07 th 2010 Munich Stephan Sigg www.teco.edu Pervasive Computing Systems Prof. Dr.-Ing. Michael Beigl KIT University of the State of

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KIT – University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz Association

www.kit.edu Technology for Pervasive Computing

Pervasive Computing Systems – Prof. Dr.-Ing. Michael Beigl

OC Colloquium DFG SPP 1183 – 11th Colloquium

October 07th 2010 Munich

Stephan Sigg www.teco.edu

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Technology for Pervasive Computing

2 08.10.2010

Introduction and project scope

Emergent radio

Weak transmission power Collaborative transmission Reaction and dependence on environment Adapt transmission method to environmental stimuli

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Technology for Pervasive Computing

3 08.10.2010

Introduction and project scope

Transmission scheme: Feedback based beamforming

Low computational demand for nodes Random iterative search

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Technology for Pervasive Computing

4 08.10.2010

Overview

Introduction and project scope Carrier synchronisation by 1-bit-feedback

Asymptotic bounds Algorithms Simulations/experiments

Design of an emergent transmission protocol

Impact of environmental parameters Adaptive protocol

Learning of environmental impacts Situation awareness based on channel characteristics Future work

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Technology for Pervasive Computing

5 08.10.2010

Overview

Introduction and project scope

Carrier synchronisation by 1-bit-feedback

Asymptotic bounds Algorithms Simulations/experiments

Design of an emergent transmission protocol

Impact of environmental parameters Adaptive protocol

Learning of environmental impacts Situation awareness based on channel characteristics Future work Publication list

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Technology for Pervasive Computing

6 08.10.2010

Collaborative beamforming

Feedback based carrier synchronisation – results:

Traditional approach can be modelled as EA ([11,12]) Sharp asymptotic bounds on the expected synchronisation time ([3] Algorithmic modifications [8,9,10]

Improvement by factor ½ typically

Asymptotically optimum approach [7]

[3] Sigg, Beigl: A sharp asymptotic bound for feedback based closed-loop distributed adaptive beamforming in WSNs (submitted and currently reviewed in 2nd revision to IEEE TMC) [7] Masri, Sigg, Beigl: An asymptotically optimal approach to distributed adaptive transmit beamforming in WSNs, EW‘10 [8] Sigg, Masri, Ristau, Beigl: Limitations, performance and instrumentation of closed-loop feedback based distributed adaptive transmit beamforming in WSNs, ISSNIP‘09 [9] Sigg, Beigl: Algorithmic approaches to distributed adaptive transmit beam forming, ISSNIP‘09 [10] Sigg, Beigl: Algorithms for closed-loop feedback based distributed adaptive beamforming in WSNs, ISSNIP‘09 [11] Sigg, Beigl: Randomised Collaborative Transmission of Smart Objects, DIPSO‘08 [12] Sigg, Beigl: Collaborative Transmission in Wireless Sensor Networks by a (1+1)-EA, ASWN’08

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Technology for Pervasive Computing

7 08.10.2010

Collaborative beamforming

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Technology for Pervasive Computing

8 08.10.2010

Collaborative beamforming

Asymptotically optimum approach:

Solve multivariable equations In simulations: Runtime approximately 12n

[6] Masri, Sigg, Beigl: An asymptotically optimal approach to distributed adaptive transmit beamforming in WSN, EW‘10

SLIDE 9

Technology for Pervasive Computing

9 08.10.2010

Overview

Introduction and project scope Carrier synchronisation by 1-bit-feedback-based method

Asymptotic bounds Algorithms Simulations/experiments

Design of an emergent transmission protocol

Impact of environmental parameters Adaptive protocol

Learning of environmental impacts Situation awareness based on channel characteristics Future work Publication list

SLIDE 10

Technology for Pervasive Computing

10 08.10.2010

Collaborative beamforming

A protocol for collaborative transmission

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Technology for Pervasive Computing

11 08.10.2010

Collaborative beamforming

A protocol for collaborative transmission

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Technology for Pervasive Computing

12 08.10.2010

Collaborative beamforming

[1] Sigg, Beigl: Implicit situation awareness for a distributed adaptive transmit beamforming protocol in Pervasive Computing, PerCom2011 (submitted)

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Technology for Pervasive Computing

13 08.10.2010

Collaborative beamforming

Environmental effects

Noise Mobility Network size …

[1] Sigg, Beigl: Implicit situation awareness for a distributed adaptive transmit beamforming protocol in Pervasive Computing, PerCom2011 (submitted) [6] Masri, Sigg, Beigl: An asymptotically optimal approach to distributed adaptive transmit beamforming in WSN, EW‘10

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Technology for Pervasive Computing

14 08.10.2010

Overview

Introduction and project scope Carrier synchronisation by 1-bit-feedback

Asymptotic bounds Algorithms Simulations/experiments

Design of an emergent transmission protocol

Impact of environmental parameters Adaptive protocol

Learning of environmental impacts

Situation awareness based on channel characteristics Future work Publication list

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Technology for Pervasive Computing

15 08.10.2010

Collaborative beamforming

Adaptive protocol for collaborative transmision

Learning of environmental impacts

Parameters: Mutation probability, variance, Prob. distribution

Simple binary search LCS / M-LCS

[1] Sigg, Beigl: An adaptive protocol for distributed beamforming, KIVS’11 (submitted)

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Technology for Pervasive Computing

16 08.10.2010

Overview

Introduction and project scope Carrier synchronisation by 1-bit-feedback

Asymptotic bounds Algorithms Simulations/experiments

Design of an emergent transmission protocol

Impact of environmental parameters Adaptive protocol

Learning of environmental impacts

Situation awareness from the RF-channel

Future work Publication list

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Technology for Pervasive Computing

17 08.10.2010

Further work

Situation awareness based on channel variations

Achievable accuracy Feasible context types Awareness based on normal network communication Cost for situation detection

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Technology for Pervasive Computing

18 08.10.2010

Further work

Results

Situation mean median Standard-deviation Door (opened /closed) 0.952 0.9513 0.0099 Presence of individual 0.817 0.8238 0.0455 Phone call (gsm) 0.900 1.0 0.32 Door opened (cond.: Empty room) 1.0 1.0 0.0 Door closed (cond.: Empty room) 1.0 1.0 0.0 Door closed (room occupied) 0.832 0.83 0.041 Door opened (room occupied) 0.976 0.98 0.0184

[6] Sigg, Beigl, Distributed adaptive expectation aware in-network context processing, Cosdeo‘10

SLIDE 19

Technology for Pervasive Computing

19 08.10.2010

Overview

Introduction and project scope Carrier synchronisation by 1-bit-feedback

Asymptotic bounds Algorithms Simulations/experiments

Design of an emergent transmission protocol

Impact of environmental parameters Adaptive protocol

Learning of environmental impacts Situation awareness from the RF-channel

Future work

Publication list

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Technology for Pervasive Computing

20 08.10.2010

Further work

In-network processing Cheap context representation

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Technology for Pervasive Computing

21 08.10.2010

Overview

Introduction and project scope Carrier synchronisation by 1-bit-feedback

Asymptotic bounds Algorithms Simulations/experiments

Design of an emergent transmission protocol

Impact of environmental parameters Adaptive protocol

Learning of environmental impacts Situation awareness from the RF-channel Future work

Publication list

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Technology for Pervasive Computing

22 08.10.2010

Collaborative beamforming

[1] Sigg, Beigl: Implicit situation awareness for a distributed adaptive transmit beamforming protocol in Pervasive Computing, PerCom’11 (submitted) [2] Sigg, Beigl: An adaptive protocol for distributed beamforming, KIVS’11 (submitted) [3] Sigg, Beigl: A sharp asymptotic bound for feedback based closed-loop distributed adaptive beamforming in WSNs (submitted and currently reviewed in 2nd revision to IEEE TMC) [4] Sigg, Beigl: Distributed adaptive expectation aware in-network contxt processing, Cosdeo‘10 [5] Banitalebi, Sigg, Beigl: On the Feasibility of Receive Collaboration in Wireless Sensor Networks, PIMRC‘10 [6] Banitalebi, Sigg, Beigl: Performance analysis of receive collaboration in TDMA based WSN, Ubicom‘10 [7] Masri, Sigg, Beigl: An asymptotically optimal approach to distributed adaptive transmit beamforming in wireless sensor networks, EW‘10 [8] Sigg, Masri, Ristau, Beigl: Limitations, performance and instrumentation of closed-loop feedback based distributed adaptive transmit beamforming in WSNs, ISSNIP‘09 [9] Sigg, Beigl: Algorithmic approaches to distributed adaptive transmit beam forming, ISSNIP‘09 [10] Sigg, Beigl: Algorithms for closed-loop feedback based distributed adaptive beamforming in wireless sensor networks, ISSNIP‘09 [11] Sigg, Beigl: Randomised Collaborative Transmission of Smart Objects, Dipso‘08 [12] Sigg, Beigl: Collaborative Transmission in Wireless Sensor Networks by a (1+1)-EA, ASWN‘08

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Technology for Pervasive Computing

23 08.10.2010

Changes in the project

Reserach opportunity in the Group of Prof. Yusheng Ji

National Institute of Informatics, Tokyo http://research.nii.ac.jp/~kei/ Research focus:

In-network context processing Context awareness from RF-channel information

Current line of work in DFG-SPP continued by Behnam Banitalebi

behnam@teco.edu 0721 / 464 70412

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Technology for Pervasive Computing

24 08.10.2010