Revisiting the Contract Between Layers Sachin Katti Dina Katabi, Hari Balakrishnan, Muriel Medard.

Slides:



Advertisements
Similar presentations
Chorus: Collision Resolution for Efficient Wireless Broadcast Xinyu Zhang, Kang G. Shin University of Michigan 1.
Advertisements

XORs in The Air: Practical Wireless Network Coding
VSMC MIMO: A Spectral Efficient Scheme for Cooperative Relay in Cognitive Radio Networks 1.
Opportunistic Routing Is Missing Its Opportunities! Sachin Katti & Dina Katabi.
Symbol Level Network Coding By Sachin Katti, Dina Katabi, Hari Balakrishnan, Muriel Medard Sigcomm 2008.
Analog Network Coding Sachin Katti Shyamnath Gollakota and Dina Katabi.
Incentive-Compatible Opportunistic Routing for Wireless Networks Fan Wu, Tingting Chen, Sheng Zhong (SUNY Buffalo) Li Erran Li Li Erran Li (Bell Labs)
CMAP: Harnessing Exposed Terminals in Wireless Networks Mythili Vutukuru Joint work with Kyle Jamieson and Hari Balakrishnan.
MIMO As a First-Class Citizen in Kate C.-J. Lin Academia Sinica Shyamnath Gollakota and Dina Katabi MIT.
XPRESS: A Cross-Layer Backpressure Architecture for Wireless Multi-Hop Networks Rafael Laufer, Theodoros Salonidis, Henrik Lundgren, Pascal Le Guyadec.
Strider : Automatic Rate Adaptation & Collision Handling Aditya Gudipati & Sachin Katti Stanford University 1.
Network Coding Testbed Using Software-Defined Radio Abstract In current generation networks, network nodes operate by replicating and forwarding the packets.
1 A Framework for Joint Network Coding and Transmission Rate Control in Wireless Networks Tae-Suk Kim*, Serdar Vural*, Ioannis Broustis*, Dimitris Syrivelis.
XORs in the air: Practical Wireless Network Coding Sachin Katti, Hariharan Rahul, Wenjun Hu, Dina Katabi, Muriel Medard, Jon Crowcroft SIGCOMM ‘06 Presented.
1 University of Freiburg Computer Networks and Telematics Prof. Christian Schindelhauer Mobile Ad Hoc Networks Network Coding and Xors in the Air 7th Week.
Exploiting Opportunism in Wireless Networks Aruna Balasubramanian Guest Lecture, CS 653 (Some slides borrowed from the ExOr and MORE presentations at SigComm.
Opportunistic Routing in Multi-hop Wireless Networks Sanjit Biswas and Robert Morris MIT CSAIL Presented by: Ao-Jan Su.
Opportunistic Routing in Multi-hop Wireless Networks Sanjit Biswas and Robert Morris MIT CSAIL
ExOR: Opportunistic Multi-Hop Routing For Wireless Networks Sanjit Biswas & Robert Morris.
DAC: Distributed Asynchronous Cooperation for Wireless Relay Networks 1 Xinyu Zhang, Kang G. Shin University of Michigan.
1 Algorithms for Bandwidth Efficient Multicast Routing in Multi-channel Multi-radio Wireless Mesh Networks Hoang Lan Nguyen and Uyen Trang Nguyen Presenter:
SourceSync: A Distributed Architecture for Sender Diversity Hariharan Rahul Haitham Hassanieh Dina Katabi.
Combating Cross-Technology Interference Shyamnath Gollakota Fadel Adib Dina Katabi Srinivasan Seshan.
Network Layer: Non-Traditional Wireless Routing Localization Intro Y. Richard Yang 12/4/2012.
ExOR: Opportunistic Multi-Hop Routing for Wireless Networks Sigcomm 2005 Sanjit Biswas and Robert Morris MIT Computer Science and Artificial Intelligence.
Overcoming the Antennas-Per-AP Throughput Limit in MIMO Shyamnath Gollakota Samuel David Perli and Dina Katabi.
End-to-End Delay Analysis for Fixed Priority Scheduling in WirelessHART Networks Abusayeed Saifullah, You Xu, Chenyang Lu, Yixin Chen.
Harnessing Mobile Multiple Access Efficiency with Location Input Wan Du * and Mo Li School of Computer Engineering Nanyang Technological University, Singapore.
International Technology Alliance In Network & Information Sciences International Technology Alliance In Network & Information Sciences 1 Cooperative Wireless.
Slicing the Onion: Anonymity Using Unreliable Overlays Sachin Katti Jeffrey Cohen & Dina Katabi.
Network Coding Testbed Jeremy Bergan, Ben Green, Alex Lee.
SOAR: Simple Opportunistic Adaptive Routing Protocol for Wireless Mesh Networks Authors: Eric Rozner, Jayesh Seshadri, Yogita Ashok Mehta, Lili Qiu Published:
QoS-Aware In-Network Processing for Mission-Critical Wireless Cyber-Physical Systems Qiao Xiang Advisor: Hongwei Zhang Department of Computer Science Wayne.
A Cooperative Diversity- Based Robust MAC Protocol in wireless Ad Hoc Networks Sangman Moh, Chansu Yu Chosun University, Cleveland State University Korea,
AutoMAC : Rateless Wireless Concurrent Medium Access Aditya Gudipati, Stephanie Pereira, Sachin Katti Stanford University.
Network Coding Testbed Jeremy Bergan, Ben Green, Alex Lee.
Improving QoS Support in Mobile Ad Hoc Networks Agenda Motivations Proposed Framework Packet-level FEC Multipath Routing Simulation Results Conclusions.
IEEE Globecom 2010 Tan Le Yong Liu Department of Electrical and Computer Engineering Polytechnic Institute of NYU Opportunistic Overlay Multicast in Wireless.
Wireless Network Coding Martin Xu. Outline Introduction New Solutions – COPE – ANC Conclusions.
User Cooperation via Rateless Coding Mahyar Shirvanimoghaddam, Yonghui Li, and Branka Vucetic The University of Sydney, Australia IEEE GLOBECOM 2012 &
Pushing the Limits of Wireless Networks Prof. Dina Katabi Jan 9, 2006.
Ch 11. Multiple Antenna Techniques for WMNs Myungchul Kim
Sunghwa Son Introduction Time-varying wireless channel  Large-scale attenuation Due to changing distance  Small-scale fading Due to multipath.
Decoding Collisions Shyamnath Gollakota Dina Katabi.
Practical Network Coding for Wireless Mesh Networks Wenjun Hu Joint work with Sachin Katti, Hariharan Rahul, Dina Katabi, Jon Crowcroft and Muriel Médard.
Multirate Anypath Routing in Wireless Mesh Networks Rafael Laufer †, Henri Dubois-Ferrière ‡, Leonard Kleinrock † Acknowledgments to Martin Vetterli and.
Architectures and Algorithms for Future Wireless Local Area Networks  1 Chapter Architectures and Algorithms for Future Wireless Local Area.
15-744: Computer Networking L-12 Wireless Broadcast.
Trading Coordination For Randomness Szymon Chachulski Mike Jennings, Sachin Katti, and Dina Katabi.
PPR: Partial Packet Recovery for Wireless Networks Kyle Jamieson and Hari Balakrishnan MIT Computer Science and Artificial Intelligence Laboratory.
Cross-Layer Approach to Wireless Collisions Dina Katabi.
Nour KADI, Khaldoun Al AGHA 21 st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications 1.
2012 1/6 NSDI’08 Harnessing Exposed Terminals in Wireless Networks Mythili Vutukuru, Kyle Jamieson, and Hari Balakrishnan MIT Computer Science and Artificial.
Why PHY Really Matters Hari Balakrishnan MIT CSAIL August 2007 Joint work with Kyle Jamieson and Ramki Gummadi.
Trading Structure for Randomness in Wireless Opportunistic Routing Szymon Chachulski, Michael Jennings, Sachin Katti and Dina Katabi MIT CSAIL SIGCOMM.
Information Theory for Mobile Ad-Hoc Networks (ITMANET): The FLoWS Project Collision Helps! Algebraic Collision Recovery for Wireless Erasure Networks.
Bringing Life to Dead Spots Grace Woo Pouya Kheradpour, Dawei Shen, and Dina Katabi.
Performance Comparison of Ad Hoc Network Routing Protocols Presented by Venkata Suresh Tamminiedi Computer Science Department Georgia State University.
The Importance of Being Opportunistic Sachin Katti Dina Katabi, Wenjun Hu, Hariharan Rahul, and Muriel Medard.
Coding for Multipath TCP: Opportunities and Challenges Øyvind Ytrehus University of Bergen and Simula Res. Lab. NNUW-2, August 29, 2014.
Non-Traditional Routing, Transport and Localization Y. Richard Yang 3/3/2009.
Wireless Communication
Xors in the air Sachin Katti, Hariharan Rahul, Wenjun Hu, Dina Katabi, Muriel Medard, Jon Crowcroft.
Network Layer: Non-Traditional Wireless Routing Localization Intro
Network Routing: Link Metrics and Non-Traditional Routing
Network: Non Traditional Routing
Network Coding Testbed
Taking Advantage of Broadcast
Decoding Collisions Shyamnath Gollakota Dina Katabi.
ExOR: Opportunistic Multi-hop routing for Wireless Networks
Presentation transcript:

Revisiting the Contract Between Layers Sachin Katti Dina Katabi, Hari Balakrishnan, Muriel Medard

Mesh Networks Borrowed the Internet Contract Conflicts with wireless mesh characteristics PHY + LLDeliver correct packets NetworkForward correct packets to destination Current contract builds reliability on a link by link basis Spatial diversity more naturally provides reliability across multiple links

S R1 R2 D 99% (10 -3 BER) Wireless Naturally Provides Reliability Across Links 0% Even 1 bit in 1000 incorrect  Packet loss of 99%

S R1 R2 D 99% (10 -3 BER) Wireless Naturally Provides Reliability Across Links 0% Current contract  Link by link reliability  50 transmissions Loss

S R1 R2 D 99% (10 -3 BER) Wireless Naturally Provides Reliability Across Links 0% Spatial diversity: Even if no correct packets, every bit is likely received correctly at some node Exploit wireless characteristics  3 transmissions Current contract  50 tx  Low throughput Exploit wireless characteristics  3 tx  High throughput

Useful with High Quality Links? R1 R2 R3 R4 SaSa PbPb DbDb DaDa SbSb PbPb PaPa PaPa PaPa PbPb 1% 2% 1% 3% 0% Loss

Useful with High Quality Links? R1 R2 R3 R4 SaSa PbPb DbDb DaDa SbSb PbPb PaPa PaPa PaPa PbPb 1% 2% 1% 3% 0% Current contract  Inhibits concurrency Exploit wireless characteristics  Enables high concurrency

Current Contract Limits throughput, inhibits concurrency PHY + LLDeliver correct symbols to higher layer NetworkForward correct symbols to destination PHY + LLDeliver correct packets NetworkForward correct packets to destination High throughput, high concurrency New Contract Exploiting Wireless Characteristics

MIXIT New contract between layers to harness wireless characteristics Novel symbol-level network code that scalably routes correct symbols High concurrency MAC Implementation and evaluation 3-4x gain over shortest path routing 2-3x gain over packet-level opp. routing

How does a Router Identify Correct Symbols? PHY already estimates a confidence for every decoded symbol [JB07] PHY + LL delivers high confidence symbols to network layer PHY Confidence Packet PHY + LLDeliver correct symbols to higher layer NetworkForward correct symbols to destination

What Should Each Router Forward? R1 R2 D S P1 P2 P1 P2 P1 P2

What Should Each Router Forward? R1 R2 D S P1 P2 But overlap in correctly received symbols Potential solutions 1)Forward everything  Inefficient 2)Coordinate  Unscalable P1 P2 P1 P2 P1 P2 P1 P2

Forward random combinations of correct symbols R1 R2 D S P1 P2 MIXIT Prevents Duplicates using Symbol Level Network Coding P1 P2 P1 P2

… … R1 R2 D … … … Routers create random combinations of correct symbols … MIXIT Prevents Duplicates using Symbol Level Network Coding

R1 R2 D … … Solve 2 equations Destination decodes by solving linear equations Randomness prevents duplicates without co-ordination MIXIT Prevents Duplicates using Symbol Level Network Coding

… … R1 R2 D … … … Routers create random combinations of correct symbols … MIXIT Prevents Duplicates using Symbol Level Network Coding

R1 R2 D … … Solve 2 equations Destination decodes by solving linear equations Symbol Level Network Coding No duplicates  Efficient No coordination  Scalable Symbol Level Network Coding No duplicates  Efficient No coordination  Scalable MIXIT Prevents Duplicates using Symbol Level Network Coding

Destination needs to know which combinations it received (if both symbols were correct) (if only s 1 was correct) (if only s 2 was correct) Nothing (if neither symbol was correct)

Destination needs to know which combinations it received Use run length encoding Original Packets Coded Packet

Original Packets Coded Packet Use run length encoding Destination needs to know which combinations it received

Original Packets Coded Packet Destination needs to know which combinations it received Use run length encoding

Original Packets Coded Packet Destination needs to know which combinations it received Use run length encoding

Run length encoding efficiently expresses combinations Destination needs to know which combinations it received Use run length encoding

Symbol-level Network Coding Original Packets Coded Packet R1 Forward random combinations of correct symbols

Original Packets Coded Packet Symbol-level Network Coding R1 Forward random combinations of correct symbols

Original Packets Coded Packet Symbol-level Network Coding R1 Forward random combinations of correct symbols

Original Packets Coded Packet Symbol-level Network Coding R1 Forward random combinations of correct symbols

Use run length encoding to efficiently expresses combinations in header Symbol-level Network Coding R1 Forward random combinations of correct symbols

R1 R2 D Solve 2 equations Destination decodes by solving linear equations MIXIT: Efficient and Scalable Symbol-level Opportunistic Routing Original Symbols Symbol Level Network Coding No duplicates  Efficient No coordination  Scalable Symbol Level Network Coding No duplicates  Efficient No coordination  Scalable

Routers May Forward Erroneous Bits Despite High Confidence MIXIT has E2E error correction capability! Symbol-Level Network Coding ECC Data MIXIT’s Error Correcting Code (ECC) 1.Routers are oblivious to ECC 2.Optimal error correction capability 3.Rateless Decode ECC Data PHY + LLDeliver correct symbols to higher layer NetworkForward correct symbols to destination Source Destination

High Concurrency MAC Each node maintains a map of conflicting transmissions Map is based on empirical measurements and built in distributed, online manner w & x  NO! w & u  YES! x u w

Evaluation Implementation on GNURadio SDR and USRP Zigbee (IEEE ) link layer 25 node indoor testbed, random flows Compared to: 1.Shortest path routing based on ETX 2.MORE: Packet-level opportunistic routing

Throughput (Kbps) CDF Throughput increase: 3x over SPR, 2x over MORE Throughput Comparison 2.1x 3x Shortest Path MORE MIXIT

Throughput (Kbps) CDF Where do the gains come from? Shortest Path MORE MIXIT Take concurrency away from MIXIT

Where do the gains come from? 1.5x Without concurrency, 1.5x gain over MORE Throughput (Kbps) CDF Shortest Path MORE MIXIT without concurrency Take concurrency away from MIXIT

Where do the gains come from? Throughput (Kbps) CDF MIXIT Gains come from both moving to the symbol level and high concurrency Shortest Path MORE MIXIT without concurrency

Where do the gains come from? Higher Concurrency? 1.4x MORE, enhanced with higher concurrency is only 1.4x better Throughput (Kbps) CDF

Where do the gains come from? Throughput (Kbps) CDF 2.1x 1.5x Higher concurrency MAC fully exploits symbol-level diversity Higher concurrency MAC fully exploits symbol-level diversity

Multiple Flows Shortest Path MORE MIXIT No. of concurrent flows Avg. Network Throughput (Kbps) MORE/SPR: Higher congestion  Lower concurrency MIXIT: Higher congestion  High concurrency MORE/SPR: Higher congestion  Lower concurrency MIXIT: Higher congestion  High concurrency

Related Work Opportunistic Routing EXOR [BM05], MORE [CJKK07], Coop-diversity[LWT04] Soft information SoftPHY [JB07], SOFT [WKK07], SOVA[HH89] Increasing wireless concurrency CMAPS[VJB08], Conflict maps[JPPQ03], Interference modeling [RMRWZ06] Network Coding Linear codes[ACLY00], MRD [G:85, SKK:08, KK:07]

Conclusion MIXIT New contract harnesses wireless characteristics Symbol-level network coding to scalably route correct symbols High concurrency Implementation and evaluation demonstrating 3-4x over shortest path, 2-3x gains over MORE