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 Baden-Wuerttemberg and www.kit.edu National Research Center of the Helmholtz Association
Introduction and project scope Emergent radio Weak transmission power Collaborative transmission Reaction and dependence on environment Adapt transmission method to environmental stimuli Technology for 2 15.02.2011 Pervasive Computing
Introduction and project scope Transmission scheme: Feedback based beamforming Low computational demand for nodes Random iterative search Technology for 3 15.02.2011 Pervasive Computing
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 Technology for 4 15.02.2011 Pervasive Computing
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 Technology for 5 15.02.2011 Pervasive Computing
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 Technology for 6 15.02.2011 Pervasive Computing
Collaborative beamforming Technology for 7 15.02.2011 Pervasive Computing
Collaborative beamforming Asymptotically optimum approach: Solve multivariable equations In simulations: Runtime approximately 12n Technology for [6] Masri, Sigg, Beigl: An asymptotically optimal approach to distributed adaptive transmit beamforming in WSN, EW‘10 8 15.02.2011 Pervasive Computing
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 Technology for 9 15.02.2011 Pervasive Computing
Collaborative beamforming A protocol for collaborative transmission Technology for 1 0 15.02.2011 Pervasive Computing
Collaborative beamforming A protocol for collaborative transmission Technology for 1 1 15.02.2011 Pervasive Computing
Collaborative beamforming [1] Sigg, Beigl: Implicit situation awareness for a distributed adaptive transmit beamforming protocol in Pervasive Computing, PerCom2011 (submitted) Technology for 1 2 15.02.2011 Pervasive Computing
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) Technology for [6] Masri, Sigg, Beigl: An asymptotically optimal approach to distributed adaptive transmit beamforming in WSN, EW‘10 1 3 15.02.2011 Pervasive Computing
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 Technology for 1 4 15.02.2011 Pervasive Computing
Collaborative beamforming Adaptive protocol for collaborative transmision Learning of environmental impacts Parameters: Mutation probability, variance, Prob. distribution Simple binary search LCS / M-LCS Technology for [1] Sigg, Beigl: An adaptive protocol for distributed beamforming, KIVS’11 (submitted) 1 5 15.02.2011 Pervasive Computing
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 Technology for 1 6 15.02.2011 Pervasive Computing
Further work Situation awareness based on channel variations Achievable accuracy Feasible context types Awareness based on normal network communication Cost for situation detection Technology for 1 7 15.02.2011 Pervasive Computing
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 Technology for 1 8 15.02.2011 Pervasive Computing
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 Technology for 1 9 15.02.2011 Pervasive Computing
Further work In-network processing Cheap context representation Technology for 2 0 15.02.2011 Pervasive Computing
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 Technology for 2 1 15.02.2011 Pervasive Computing
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
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
cussion Questions? Behnam Banitalebi, Stephan Sigg { behnam,sigg} @teco.edu
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