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Roee Diamant , Prof. Lutz Lampe

Robust Spatial Reuse Scheduling in Underwater Acoustic Communication Networks. Roee Diamant , Prof. Lutz Lampe. Outline. Very quick introduction on underwater communication Graph coloring and the broadcast scheduling problem Robust spatial reuse scheduling Some simulation results.

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Roee Diamant , Prof. Lutz Lampe

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  1. Robust Spatial Reuse Scheduling in Underwater Acoustic Communication Networks Roee Diamant, Prof. Lutz Lampe

  2. Outline • Very quick introduction on underwater communication • Graph coloring and the broadcast scheduling problem • Robust spatial reuse scheduling • Some simulation results

  3. Motivations • Applications [1]: • Ocean exploration • Remote data retrieval (warning systems, pollution control) • Military underwater surveillance • Offshore underwater oil exploration High traffic broadcast communications networks are required 3

  4. Applications

  5. Motivations • Applications [1]: • Ocean exploration • Remote data retrieval (warning systems, pollution control) • Military underwater surveillance • Offshore underwater oil exploration • Cables are heavy, deployment is expensive. • Wireless communication [2]: • Radio (30Hz-300Hz)‏ • Optical (short distances, pointing precision)‏ High traffic broadcast communications networks are required UWAC: Underwater Acoustic Communications 5

  6. I wish… 6

  7. Challenges of UWAC [2] Sea trial • Fast time-varying frequency-selective channel 7

  8. Challenges of UWAC [2] • Half-duplex communications (transducers limitations) 8

  9. Character RF UWAC Effect Propagation delay T 2·105.T Throughput Transmission rate ~109 bps ~102 bps Error probabilities ~10-7 ~10-4 Reliability Frequency ~GHz ~KHz SNR Challenges of UWAC [3,4] 9

  10. Outline • Very quick into. on underwater communication • Graph coloring and the broadcast scheduling problem • Robust spatial reuse scheduling • Some simulation results

  11. System Model • High traffic broadcast communication (e.g. navigation) • primary conflicts: • The network’s connectivity matrix is shared. • Changes in the network are slow Find time-slot assignment which is robust to topology uncertainties 11

  12. Coloring the network • Topology-based broadcast scheduling problem (T-BSP) [6]: • For minimal time-frame duration, maximize channel utilization • T-BSP transforms into graph-coloring [5] • Graph representation: • Node = Vertex; Link = Edge; Time-slot = Color Minimize number of colors (adjacent vertices gets different colors) 12

  13. Spatial reuse • Topology based assignment. Examples: • Each node gets a unique color • Graph colored with only two colors • Less colors = better availability. Spatial reuse: performance increase the more sparse the network is 13

  14. Topology Uncertainties • Different time frame yields different schedule: • Our approach: combining topology-information with a-priori • “skeleton” topology-transparent schedule, shared by all nodes. 1 2 4 3 total network failure! 1 2 4 3 15

  15. Flow in topology-transparent schedules • Vertex with higher degree (often bottleneck) gets fewer colors: • Additional problem – fairness in resource assignment 1 2 4 (TDMA skeleton) 3 Flow constraints are needed 16

  16. Outline • Very quick into. on underwater communication • Graph coloring and the broadcast scheduling problem • Robust spatial reuse scheduling • Some simulation results

  17. Robust Broadcast Scheduling Problem (R-BSP) • Offline: Rearrange the skeleton matrix . • For each column and a designated “slot-node” , . • Online (Given the network topology): Remove conflicts • Set for each node that is a one-hop neighbor of • Online: Maximize channel utilization (for each column ) • Find all independent sets that include and a maximum number of pre-assigned nodes in the th column of . 18

  18. Problem Formulation • Channel utilization maximization problem (CUMP)- RBSP: • Solution: • For each column of the skeleton schedule, choose the • independent set with the maximal cardinality. Used to impose minimum Flow in the network 19

  19. Last remarks • While the BPS involves solving two integer linear programming, the RBSP does not require usage of optimization techniques. • We formulated the RBSP also for differential fairness, in which the variance of the node time-slot assignments is minimized. • The choice of the skeleton matrix affects the performance. In the paper we give guidelines for choosing the skeleton matrix. Next, we show results for TDMA skeleton schedule and topology-transparent schedule from [7]. 20

  20. Outline • Very quick into. on underwater communication • Spatial reuse using graph coloring • Robust spatial reuse scheduling • Some simulation results 21

  21. Throughput Fixed topology Time-varying topology 22

  22. Transmission Delay 23

  23. Summary Topology-based BSP is not robust to topology-uncertainties, and topology-transparent schedules do not fully utilize the channel Problem: Our Solution: Combine T-BSP with topology-transparent skeleton schedule Robustness to topology-uncertainties and higher throughput Is achieved at the cost of scheduling delay Performance: Roee Diamant, Lutz Lampe. “Robust Spatial Reuse Scheduling in Underwater Acoustic Communication Networks,” submitted for publication in the IEEE Transactions on Wireless Communications journal, Feb. 2011. Roee Diamant, Lutz Lampe “Underwater Localization with Time- Synchronization and Propagation Speed Uncertainties,” IEEE vehicular technology conference (VTC), Sep. 2011, San Francisco, USA. 24

  24. Further work • In this work we utilized the (possible) sparsity of the network topology to schedule broadcast transmissions. • centralized solution that fits only small networks • Further work includes a distributed handshake scheduling protocol for unicast communications, that applies spatial and time reuse. • Additional research: • Underwater acoustic localization and tracking, LOS and NLOS classification 25

  25. Reference [1] J. Partan, J. Kurose, and B. Levine, “A Survey of Practical Issues in Underwater Networks,” in International Conference on Mobile Computing and Networking (MobiCom), Los Angeles, CA, USA, Sep. 2006 [2] W. Burdic, Underwater Acoustic System Analysis. Los Altos, CA, USA: Peninsula Publishing, 2002 [3] N. Chirdchoo, W. Soh, and K. C. Chua, “Aloha-based MAC Protocols with Collision Avoidance for Underwater AcoustNetworks,” in The IEEE Conference on Computer Communications (Infocom), Anchorage, Alaska, USA, May 2007 [4] M. Molins and M. Stojanovic, “Slotted Fama: a MAC Protocol for Underwater Acoustic Networks,” in IEEE Oceans2006, Singapore, May 2006 [5] M. Molloy and B. Reed, Graph Coloring and the Probabilistic Method. Springer-Verlag Berlin Heidelberg, 2002. [6] S. Menon, “A Sequential Approach for Optimal Broadcast Scheduling in Packet Radio Networks,” vol. 57, no. 3, pp. 764–770, Mar. 2009 [7] Z. Cai, M. Lu, and C. Georghiades, “Topology-Transparent Time Division Multiple Access Broadcast Scheduling in MultihopPacket Radio Networks,” vol. 52, no. 4, pp. 970–984, Jul. 2003 26 26

  26. Thank you • Questions?

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