1. Improving Throughput in Multihop Wireless Networks Zongpeng Li and Baochun Li, Senior Member, IEEE IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 55, NO. 3, MAY 2006 Advisor: Yeong-Sung, Lin Presented by Yen-Yi, Hsu OPLAB, IM, NTU.

2. Company Logo OPLab, NTUIM Author Zongpeng Li received the B.E. degree in computer science and technology from Tsinghua University,Beijing, China,in 1999 the M.S. degree in computer science and the Ph.D. degree in electrical and computer engineering from the University of Toronto, Toronto, ON, Canada, in 2001 and 2005 He is currently an Assistant Professor at the Department of Computer Science, University of Calgary, Calgary, AB, Canada. His research interests are in data networks and distributed algorithms. 2

3. Company Logo OPLab, NTUIM Author Baochun Li (M’00–SM’05) B.Eng. Degree in computer science and technology from the Department of Computer Science and Technology, Tsinghua University, Beijing, China, in 1995 The M.S. and Ph.D. degrees in computer science from the Department of Computer Science, University of Illinois at Urbana-Champaign, in 1997 and 2000 an Associate Professor in Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada held the Nortel Networks Junior Chair in Network Architecture and Services since October 2003 and the Bell University Laboratories Endowed Chair in computer engineering since August 2005. interests include application-level QoS provisioning and wireless and overlay networks. a member of the ACM He was the recipient of the IEEE Communications Society Leonard G. Abraham Award in the Field of Communications Systems, in 2000. 3

4. Company Logo OPLab, NTUIM Agenda Introduction Preliminaries Data Dissemination Data aggregation Conclusion 4

5. Company Logo OPLab, NTUIM Introduction 5

6. Company Logo OPLab, NTUIM Introduction(1/2)  Broadcast nature of wireless ad hoc networks lead to interferences and spatial contention  Flows also compete for shared channel bandwidth if they are within the transmission ranges of each other (contention in the “spatial domain”)  Broadcast may be of assistance in the multicast scenario  Maximum achievable throughput depends on 1.Arranging network topology between source and destination 2.Activating per-node algorithms such as network coding 6

7. Company Logo OPLab, NTUIM Introduction(2/2)  We discuss solutions to the problem of increasing end-to-end throughput in three cases that cover all scenarios of end-to-end communications  Unicast data dissemination  Multicast data dissemination  Data aggregation 7

8. Company Logo OPLab, NTUIM Preliminaries 8

9. Company Logo OPLab, NTUIM Preliminaries  Two single-hop subflows of a multihop flow interfere with each other if and only if either the source or the destination of both flows are within the single-hop transmission range.  focus on multihop flows that traverse more than two hops, thus consisting of more than two subflows, because these multihop flows exhibit spatial contention even among its own subflows 9

10. Company Logo OPLab, NTUIM Preliminaries  One-hop away ：  Nodes are not within transmission range of each other 10

11. Company Logo OPLab, NTUIM Preliminaries  C ： single-hop wireless channel capacity  r ： the achievable throughput  T ： total transmit time  S ： the set of sources  t i ： the amount of time where source i is scheduled to transmit during the scheduling period T  r=C ‧ ∑ i ∈ S t i /T 11

12. Company Logo OPLab, NTUIM Preliminaries  Maximum contention clique(mcc)  A set of links such that any two links within the set interfere with each other with maximum size 6 12 6

13. Company Logo OPLab, NTUIM Preliminaries  Theorem ： r ≤ C ‧ ∑i ∈ S ti/|mcc|,equality holds if transmission topology is a forest  we have r = (C · 1 + C · 1)/6 = C/3, and the achievable throughput for each session is r/2 = C/6 13 6

14. Company Logo OPLab, NTUIM Data dissemination 14

15. Company Logo OPLab, NTUIM Data Dissemination A.Unicast ：  in wireline network, the throughput of the unicast session is able to C as well  in wireline ad hoc networks, the achievable session throughput r is only C/3, because the mcc has size 3  Due to the intraroute spatial contention, only one out of every three links can be transmitting as a given time. 15

16. Company Logo Data Dissemination  One route is not sufficient to effectively utilize the available channel capacity at the source  Using multipath routing(the broadcasting nature) to break the bound  To achieve load balancing and fault tolerance, the set of routes being chosen are required to be disjoint or partially disjoint  However, intense contention may still exist  To reduce interroute interference and achieve a higher session throughput, the routes need to be one-hop away OPLab, NTUIM 16

17. Company Logo Data Dissemination  The total number of hops on both routes is a multiple of 3, all subflows can be scheduled without interference in three equal-length phases  r = C ∑ i ∈ S ti/T = 2C/3  The total number of hops of subflows is not a multiple of 3 it takes four phases to schedule all of them  r =C/2 OPLab, NTUIM 17

18. Company Logo OPLab, NTUIM Data Dissemination  Further increase the number of routes, achievable throughput can be increased when routes is not beyond 5  Because a wireless node can have at most five one- hop away neighbors routesphasesThroughput 3 4 、 53C/4 、 3C/5 4 5 、 64C/5 、 4C/6 18

19. Company Logo Data Dissemination  In cases where a source has data to transmit to multiple unicast destinations, one-hop away multipath routing may also be applied to increase the achievable throughput  The topology is a tree  |mcc|=k+1  r = C ∑ i ∈ S ti/|mcc| = kC/(k + 1), for k ≤ 5 OPLab, NTUIM 19

20. Company Logo OPLab, NTUIM Data Dissemination B. Multicast the throughput of a single multicast session is also bounded by C/3, the throughput may be increased by activating one-hop away 20

21. Company Logo OPLab, NTUIM Data Dissemination  Branching point  branching late v.s. Branching early  Branching early leads to waste of bandwidth rather than an improvement of throughput  Both achieves C/3 ， but (a) consumes half of the bandwidth 21

22. Company Logo OPLab, NTUIM Data Dissemination  Use multiple one-hop away routes to “strengthen” longer routes before late branching, the throughput may be increased  r increased to 2C/5 22

23. Company Logo OPLab, NTUIM Data Dissemination C. Network Coding  Usually consumes less bandwidth  Achieves a throughput of 2C  R1 receive f1 and (f1 ⊕ f2), get f2 by f2=f1 ⊕ (f1 ⊕ f2), similarly, R2 get f1 by f1=f2 ⊕ (f1 ⊕ f2) 23

24. Company Logo OPLab, NTUIM Data Dissemination  not as advantageous for small and dense ad hoc networks Complicated cyclic topology Nonidentical data flows  The throughput is bounded by C/3  Multicast tree without coding using routes S-A-R 1 、 S-B-R 2 is able to achieve C/2 Coding ： C/3 Without coding ： C/2 24

25. Company Logo Data Dissemination  The advantage of coding to increase throughput can outweigh the disadvantage introduced by spatial contention if Transmission network is large and sparse(|mcc| ≥4) Spatially nearby multicast sessions exist concurrently OPLab, NTUIM Coding ： C/2 Without coding ： C/3 Each link is replaced by a multihop route 25

26. Company Logo OPLab, NTUIM Data Dissemination  Networking coding reduces contention by broadcast nature and by reducing the amount of data transmitted at the bottleneck link 26

27. Company Logo OPLab, NTUIM Data aggregation 27

28. Company Logo OPLab, NTUIM Data aggregation  Data corresponding observed by sensors are routed toward “data sink”  Aggregation ratio ： α  α=0.5→perfect aggregation  α=1→zero aggregation  Source-side amount ≥ destination –side amount 28

29. Company Logo Data aggregation  Early aggregation v.s. Late aggregation  Early aggregation reduce the overall amount of data and the total amount of energy consumption  Late aggregation is more robust  Early aggregation may introduce a higher latency OPLab, NTUIM r = 2C/(2 +4α) = C/(1 + 2α) α ∈ [0.5, 1], r ∈ [C/3, C/2] r=2C/3 Similar to the 1-to-2 independent unicast case 29

30. Company Logo Data aggregation  As the number of source flows (n) increases, the achievable throughput of late aggregation soon increases to 5C/6 where it stops  But the achievable throughput of early aggregation keeps increasing, and depending on α, it may soon become higher than C OPLab, NTUIM 30 α ∈ [0.5, 1]

31. Company Logo OPLab, NTUIM Data aggregation 31

32. Company Logo OPLab, NTUIM Data aggregation  for very small n, using one-hop away routes to transmit data directly to the sink without aggregation is the best choice regardless of α;  otherwise, source flows need to aggregate to a smaller number before reaching the sink, except for cases where the α near 1 32

33. Company Logo OPLab, NTUIM Data aggregation  It utilize the receiving capacity at the sink quite well because:  Allow source flows to aggregate into more condensed flows before being received by the sink  Allows the sink to receive data from different final flows in an interleaving way to reduce its idling time  The number of final flows  too small: single final flow forms a bottleneck  too large: intense contention around the sink 33

34. Company Logo OPLab, NTUIM Conclusion 34

35. Company Logo OPLab, NTUIM  Using strategies that include  1.multiple end-to-end paths  2.per-node algorithms such as coding  3rearranging transmission network topologies  It’s feasible and practical to increase data throughput  Adopt the best possible strategy based on the insights in this paper may help to alleviate such problems 35

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