Cross Cross - - Layer Design For Large Layer Design For Large - - Scale Sensor Networks Scale Sensor Networks NATO Cross-Layer Workshop NRL, 3 June 2004 Ananthram Swami Lang Tong US Army Research Lab Cornell University [email protected][email protected]Adelphi, MD, 20783 Ithaca, NY 14853 USA USA APPROVED FOR PUBLIC RELEASE
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Cross-Layer Design for Large Scale Sensor Networks · 2011-05-14 · Cross-Layer Design For Large-Scale Sensor Networks NATO Cross-Layer Workshop NRL, 3 June 2004 Ananthram Swami
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CrossCross--Layer Design For LargeLayer Design For Large--Scale Sensor NetworksScale Sensor Networks
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Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18
Network Overhead is Costly!
Motivates cross-layer designArchitecture vs. Performance
(DARPA Connectionless Networks)APPROVED FOR PUBLIC RELEASE
Low Duty-Cycled Sensor Network DemandsDifferent Kind of Radio
Energy consumed in ``staying awake’’ Moore’s “law” does not extend to Shannon / MaxwellMotivates cross-layer design
Random or Regular Sampling ?Random Access or Scheduling ?
Assumptions:• Dense network; sensors know locations • AR(1) model for data : `interpolation’ • Metric: Expected Maximum Distortion• Random access needs O (log K) more packets;
• Mobility induces fading• Wait for a good channel
• Transmit with probability based on CSI : O-ALOHA
[VAT 2004; ZT 2004]APPROVED FOR PUBLIC RELEASE
Example 4: Optimal Detection for MACExample 4: Optimal Detection for MAC
ss
Optimal Detection at the Receiver: – MAC assumes accurate detection of requests.
• RTS-CTS exchanges. Busy-tone detection.– Missed detections and false alarms likely in interference-rich environment
What is the impact on the MAC?How do we model PHY / MAC interaction ?
– What is the detector that optimizes the MAC performance (throughput and delays)?Markov chain formulation / Optimal Bayesian detector
[MTS, 2003]APPROVED FOR PUBLIC RELEASE
Signal ModelSignal Model
ss
Users select random codesUnknown fades
N = # orthogonal codesf = # free codes L = packet lengthλ = arrival rate
MF output is a sufficient statistic: ~ CN(0,Ki σ2 + σ2v ) K is unknown.
Traffic: Poisson w aggregate rate λ K is Poisson (λ /f)
PHY-MAC problem: K = 1 ? Metric ?
Two Approaches: • Optimal detection + optimal scheduling• Joint optimization to maximize throughput
APPROVED FOR PUBLIC RELEASE
Markov Chain for N=2, L=3α(f) = Prob of ACK’ing a channel, given f free channelsβ(f) = 1 - α(f)
* ¥ N,L, Markov chain is finite, aperiodic, and irreducible Sty distro πδ existsAPPROVED FOR PUBLIC RELEASE
Optimal Decision Regionsvs. arrival rate vs. SNR
APPROVED FOR PUBLIC RELEASE
Utilization vs. Traffic Rate
“ML”
“Ideal”
Multi-H
Cross-Layer Design is effective at low SNR’sAPPROVED FOR PUBLIC RELEASE
Utilization Curves
A Gap still existsAPPROVED FOR PUBLIC RELEASE
Cross-Layer Design
• promises adaptibility, agility, efficiency.
• Does not always imply improved performance.
• Potential for instability
• Sensor networks are application specific; PHY+MAC+APP cross-layering natural.
APPROVED FOR PUBLIC RELEASE
References
• [MZT, 2004] G. Mergen, Q. Zhao, L. Tong, ``Sensor networks with mobile access: energy and capacity considerations’’, submitted to IEEE Trans. Comm, Jan 2004
• [STS, 2004] Y. Sung, L. Tong, A. Swami: ALOD for large scale sensor network under the Poisson regime; ICASSP 2004; to appear in IEEE Trans. Sig. Proc.
• [DTS, 2003] M. Dong, L. Tong, B.M. Sadler, ``Source reconstruction via mobile agents in sensor networks: throughput distortion characteristics’’, MILCOM 2003 .
• [VAT, 2004] P. Venkitasubramaniam, S. Adireddy, L Tong, ``Sensor networks with mobile access: optimal random access and coding’’, IEEE JSAC special issue on Sensor Networks, 2004.
• [ZT, 2004] Q. Zhao, L. Tong, Distributed opportunistic information retrieval in sensor networks: CSI-based carrier sensing’’, ICASSP 2004.
• [MTS, 2003] A. Maharshi, L. Tong, A. Swami, Cross-layer designs of multichannel reservation MAC under Rayleigh fading, IEEE Trans. Sig Proc, Special issue on SP & Networks, Aug 2003. .
• [ST 2004] A. Swami and L. Tong, Guest Editors, Special Issue on ``Signal Processing for Networking: An Integrated Approach’’, IEEE Signal Processing Magazine, Sept 2004.