1. Advisor: Dr. Frank Y. S. Lin Presented by Pei-Wei Li
A Low-latency and Energy-efficient Scheduling Algorithm for Multi-group Multicasting in Mobile Ad Hoc Networks 具移動性隨意網路下多群組群播之 低延遲與能耗排程演算法 進度報告
Advisor: Dr. Frank Y. S. Lin Presented by Pei-Wei Li

2. Outline Problem Description
Heuristics for getting primal feasible solutions
Schedule

3. Problem Description
Step1: construct a multicast tree for each multicast source to reach its group members
Step 2: schedule the transmission time of the nodes on these multicast trees and avoid the collision of transmission
Objective: minimize the latency of multicasting
Consider the mobility of nodes and the energy consumption of transmission.

4. Problem Description

5. Problem Description
Source 4 6
Source 5 1 7 8 2 3

6. Problem Description

7. Problem Description Assumption
We get the location and mobility information of all nodes from GPS.
Prediction of velocities and oncoming positions of nodes can be provided by Gauss-Markov mobility model.
Transmission time can be divided into discrete slots.
All nodes in the network have their clocks synchronized.
Packet propagation delay can be ignored.
Each multicast source has single data to send.

8. Problem Description

9. Problem Description
Objective function

10. Heuristics for getting primal feasible solutions

11. Heuristics for getting primal feasible solutionss

12. Heuristics for getting primal feasible solutions=2 =3
max_t=2
max_t=3 =4 =3 =5 =1
max_t=4
max_t=5
max_t=3
max_t=1

13. Heuristics for getting primal feasible solutions
max_t=-1
max_t=2
max_t=3
max_t=2
max_t=0
max_t=5
max_t=4
max_t=3
max_t=1

14. Heuristics for getting primal feasible solutions
max_t=0
max_t=3
max_t=1

15. Heuristics for getting primal feasible solutions
max_t=0
max_t=1
max_t=0
max_t=1
max_t=3
max_t=2
max_t=3
max_t=1 =2
max_t=3 =1 =3

16. Heuristics for getting primal feasible solutions
Multicast tree 1: A.B.C.D.E
node[A][A].t=1
node[B][A].t=2
Multicast tree 2: F.C.E.G.H
node[F][F].t=1
node[C][F].t=2
node[E][F].t=3 A F B C G D E H

17. Heuristics for getting primal feasible solutions
v=1 node[A][A].t= set1:A
node[F][F].t= set2:F B C G D E H

18. Heuristics for getting primal feasible solutions
set1:A set[1].t=2
set2:F set[2].t=1 2 1 B C 3 4
node[F][F].t=1
node[C][F].t=2 node[A][A].t=2
node[E][F].t=3 node[B][A].t=3 G D E 4 H

19. Heuristics for getting primal feasible solutions
v=2 node[A][A].t= set1:A
node[C][F].t= set2:C B C G D E H

20. Heuristics for getting primal feasible solutions
set1:A set[1].t=2
set2:C set[2].t=3 A F 2 1 B C 3 4
node[A][A].t=2
node[B][A].t= node[C][F].t=3
node[E][F].t=4 G D E 4 H

21. Heuristics for getting primal feasible solutions
v=3 node[B][A].t= set1:B
node[C][F].t= set2:C B C G D E H

22. Heuristics for getting primal feasible solutions
set1:B set[1].t=4
set2:C set[2].t=3 2 1 B C 3 4 G D
node[C][F].t=3 node[B][A].t=4
node[E][F].t=4 E 4 H

23. Heuristics for getting primal feasible solutions
v=4 node[B][A].t= set1:B
node[E][F].t= set2:E 2 1 B C 3
set1:B set[1].t=4
set2:E set[2].t=4 4 G D E 4 H

24. Heuristics for getting primal feasible solutions6 4 G G E E 5 4 D D H H

25. Heuristics for getting primal feasible solutions
set1:B set[1].t=6
set2:E set[2].t=4
node[E][F].t=4
node[B][A].t=5 B C 6 G E 4 D H

26. Schedule 2009.3~2009.4 — Coding、實驗設計 2009.5 — 問題處理
(包括修改heuristic、調整參數)
— 實驗數據整理、論文撰寫
— 論文口試