1. ST-MAC: Spatial-Temporal MAC Scheduling for Underwater Sensor Networks Chih-Cheng Hsu, Kuang-Fu Lai, Cheng-Fu Chou, Kate Ching-Ju Lin IEEE INFOCOM 2009

2. Outline  Introduction  Related Work  ST-MAC Framework  Performance Evaluation  Conclusion

3. Introduction  Similar to terrestrial sensors, energy efficiency is critical considerations in UWSNs unlike terrestrial sensor utilize acoustic waves propagation is slower than RF  In UWSNs, must consider the locations of the receiver and potential interferers “ Spatial-Temporal Uncertainty ”

4. Spatial-Temporal Uncertainty

5. TDMA-based MAC protocols  To utilize time slots efficiently, the vertex coloring scheme is used for scheduling  Propose a novel heuristic algorithm, called Traffic-based + One-Step Trial Approach (TOTA)  Model the scheduling problem as a Mixed Integer Linear Programming (MILP) model

6. ST-MAC Framework  Most of underwater sensors are deployed to get data of interest periodically  Each node can estimate signal-to-noise-ratio determining interference relationships measuring the propagation delay  ST-MAC is to compute the schedule each sensor nodes knows when to switch to sleeping mode

7. ST-GG Construction  Base station can acquire the routing topology G(V,E), V is a set of sensors E denotes a set of transmission links  Define PD(v i, v j ) as the propagation delay between node v i and v j

8. Spatial-Temporal Conflict Graph  Spatial-Temporal Conflict Graph (ST-CG), a directed graph G(V,E) V = E and E is the set of conflict relationships between any two transmissions  Conflict relation Conflict(u → v), exists if transmission of link u affects reception of link v

9. Example of Conflict

10. Two Links With Common Node  Case 1.1: u.dst = v.dst  Case 1.2: u.src = v.src  Case 1.3: u.src = v.dst

11. Two Links Without Common Nodes

12.  Case 2.1: ONLY one of INTER(u, v) and INTER(v, u) is TRUE c c,d = −3  Case 2.2: both INTER(u, v) and INTER(v, u) are TRUE conflict delays c b,c = −4 and c c,b = −2

13. Traffic-based One-step Trial Approach S M Real M Test

14. Traffic-based One-step Trial Approach M Real M Test S

15. Theoretical Analysis  Propose mixed Integer Linear Programming model solve the new type of the vertex-coloring problem in ST-CG optimally as a benchmark to quantitatively evaluate the performance of existing heuristic methods

16. Propagation Delay Constraint Modified equations by using the Big-M method binary variable used to transform disjunction

17. Inter-frame Constraint  Transmission of link j in next frame must not conflict with the reception of link I  Transmission of link i in the next frame must not conflict with the reception of link j

18. Minimize Problem

19. Performance Evaluation  All simulations are implemented in NS2  Two different scales the small topology case: 6 - 12 nodes the large-topology case: 81 - 144 nodes

20. Small Central-Sink Topology

21. Large Central-Sink Topology

22. Large Cluster-Sink Topology

23. Energy Cost

24. Unknown Traffic Scenarios

25. Conclusion  Proposes a TDMA-based scheduling to solve Spatial-Temporal Uncertainty in UWSNs  Construct ST-CG that includes the propagation delay information present TOTA, to solve more effectively  Derive a MILP formulation solving the optimal solution of the vertex- coloring problem in ST-CG graph