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 3 3

4. Applications 4 4

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 5 5

6. I wish… 6 6

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

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

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

10. Outline Graph coloring and the broadcast scheduling problem
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 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 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 13

14. The Broadcast Scheduling Problem (BSP)
Find sets of nodes which transmission do not collide
Minimum-frame length problem:
Solution gives the frame length
Topology-based BSP (T-BSP) [6]
minimum number of time-slots 14 14

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

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

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

18. 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

19. 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

20. 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

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

22. Throughput
Fixed topology
Time-varying topology 22 22

23. Transmission Delay 23 23

24. Summary Problem: Our Solution: Performance:
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
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

25. 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

26. 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 Acoust Networks,” 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 Multihop Packet Radio Networks,” vol. 52, no. 4, pp. 970–984, Jul. 2003 26 26 26

27. Thank you
Questions?