SLIDE 1 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
SLIDE 2 Technology for Pervasive Computing
2 15.02.2011
Introduction and project scope
Emergent radio
Weak transmission power Collaborative transmission Reaction and dependence on environment Adapt transmission method to environmental stimuli
SLIDE 3 Technology for Pervasive Computing
3 15.02.2011
Introduction and project scope
Transmission scheme: Feedback based beamforming
Low computational demand for nodes Random iterative search
SLIDE 4 Technology for Pervasive Computing
4 15.02.2011
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
SLIDE 5 Technology for Pervasive Computing
5 15.02.2011
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
SLIDE 6 Technology for Pervasive Computing
6 15.02.2011
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
SLIDE 7 Technology for Pervasive Computing
7 15.02.2011
Collaborative beamforming
SLIDE 8 Technology for Pervasive Computing
8 15.02.2011
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 15.02.2011
Overview
Introduction and project scope Carrier synchronisation by 1-bit-feedback-based method
Asymptotic bounds Algorithms Simulations/ experiments
Design of an em ergent transm ission 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
1 0 15.02.2011
Collaborative beamforming
A protocol for collaborative transmission
SLIDE 11 Technology for Pervasive Computing
1 1 15.02.2011
Collaborative beamforming
A protocol for collaborative transmission
SLIDE 12 Technology for Pervasive Computing
1 2 15.02.2011
Collaborative beamforming
[1] Sigg, Beigl: Implicit situation awareness for a distributed adaptive transmit beamforming protocol in Pervasive Computing, PerCom2011 (submitted)
SLIDE 13 Technology for Pervasive Computing
1 3 15.02.2011
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
SLIDE 14 Technology for Pervasive Computing
1 4 15.02.2011
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 environm ental im pacts
Situation awareness based on channel characteristics Future work Publication list
SLIDE 15 Technology for Pervasive Computing
1 5 15.02.2011
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)
SLIDE 16 Technology for Pervasive Computing
1 6 15.02.2011
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 aw areness from the RF-channel
Future work Publication list
SLIDE 17 Technology for Pervasive Computing
1 7 15.02.2011
Further work
Situation awareness based on channel variations
Achievable accuracy Feasible context types Awareness based on normal network communication Cost for situation detection
SLIDE 18 Technology for Pervasive Computing
1 8 15.02.2011
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
1 9 15.02.2011
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 w ork
Publication list
SLIDE 20 Technology for Pervasive Computing
2 0 15.02.2011
Further work
In-network processing Cheap context representation
SLIDE 21 Technology for Pervasive Computing
2 1 15.02.2011
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
SLIDE 22
laborative beamforming
g, Beigl: Implicit situation awareness for a distributed adaptive transmit beamforming protocol in ervasive Computing, PerCom’11 (submitted) g, Beigl: An adaptive protocol for distributed beamforming, KIVS’11 (submitted) g, Beigl: A sharp asymptotic bound for feedback based closed-loop distributed adaptive amforming in WSNs (submitted and currently reviewed in 2nd revision to IEEE TMC) g, Beigl: Distributed adaptive expectation aware in-network contxt processing, Cosdeo‘10 nitalebi, Sigg, Beigl: On the Feasibility of Receive Collaboration in Wireless Sensor Networks, MRC‘10 nitalebi, Sigg, Beigl: Performance analysis of receive collaboration in TDMA based WSN, Ubicom‘10 sri, Sigg, Beigl: An asymptotically optimal approach to distributed adaptive transmit beamforming in reless sensor networks, EW‘10 g, Masri, Ristau, Beigl: Limitations, performance and instrumentation of closed-loop feedback based stributed adaptive transmit beamforming in WSNs, ISSNIP‘09 g, Beigl: Algorithmic approaches to distributed adaptive transmit beam forming, ISSNIP‘09 gg, Beigl: Algorithms for closed-loop feedback based distributed adaptive beamforming in wireless nsor networks, ISSNIP‘09 gg, Beigl: Randomised Collaborative Transmission of Smart Objects, Dipso‘08 gg, Beigl: Collaborative Transmission in Wireless Sensor Networks by a (1+1)-EA, ASWN‘08
SLIDE 23
anges 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
SLIDE 24
cussion Questions?
Behnam Banitalebi, Stephan Sigg { behnam,sigg} @teco.edu
