1. 1 Architecture & Protocols for Supporting Routing & QoS in MANET Navid NIKAEIN http://www.eurecom.fr/~nikaeinn

2. 2 Outline  Introduction  Motivation  Topology Management  Route Determination  Quality of Service Support  Architecture  Conclusion and Open Issues

3. 3 Issues in MANET Mobile ad hoc networks: Wireless  broadcasting  collision Mobility  time-varying resources  link Failure Lack of infrastructure  fully-distributed  Complex Small devices  limited resources  multihop routing Topology Management, Routing, and QoS

4. 4 Topology of MANET B A S E F H J D C G I K Z Y Represents a node that has received packet P Represents that connected nodes are within each other’s transmission range M N L

5. 5 Motivation B A S E F H J D C G I K Represents a node that receives packet P for the first time Represents transmission of packet P Z Y Broadcast transmission M N L

6. 6 Motivation B A S E F H J D C G I K Z Y M N L

7. 7 B A S E F H J D C G I K Generate redundant and unnecessary information Z Y M N L

8. 8 Motivation B A S E F H J D C G I K Z Y M Transmissions may collide Packet P may not be delivered to node D at all, despite the use of flooding N L

9. 9 Motivation B A S E F H J D C G I K Z Y M N L Flooding generates: Redundant and unnecessary information Collision

10. 10 Related Work-Topology Management Topology Management Power ControlClustering Non-deterministic Deterministic Pb. Minimum dominating set NP-hard Core-extraction & Spanning-tree algorithms DDR TEMPO e.g. Lowest ID, Max Degree

11. 11 Topology Management  DDR- Distributed Dynamic Routing Algorithm [2,3] Intuition Preferred neighbor election Forest Construction Zone Partitioning  Simulation Model  Performance Results  Summary

12. 12 Intuition Network Topology Forest TREE TREE …. TREE ZONE ZONE …. ZONE Time BEACON Criteria

13. 13 Preferred Neighbor Election  Each node selects a neighbor, called preferred neighbor, that has the maximum degree of connectivity  If identical degrees, select the one with the higher ID a b e f c d g h 2 4 2 4 3 5 3 1 PN x ={y | y  N x  label(y) = Max(deg(N x ) | NID) } a b e f c d g h 4 2 4 3 5 3 1

14. 14 Forest Construction Theorem: Connecting each node to its preferred neighbor yields to a forest [2] Mechanism: Beaconing a b e f c d g h Forest limits the number of forwarding nodes, which reduces the redundant and unnecessary information as well as the probability of collision a b e f c d g h

15. 15 Zone Partitioning Zone Tree Maintained Proactively a b f c s y k d t x g v w z h u e in a b f c s y k d t x g v w z h u e i n DDR z1z1 z2z2 z3z3 Zones : Improves delay performance Contributes in protocol scalability Connected Dominating Set

16. 16 Criteria for FC [4]: Quality of Connectivity (QoC)  Buffer level b  unallocated buffer  Stability level s  QoC =   b +   s PN x ={y | y  N x  label(y) = Max(QoC(N x ) | NID) }  The PNs belong to the set of high quality nodes

17. 17 Protocol Model  To study the effect of the proposed topology management on routing performance ?  Hybrid Ad Hoc Routing Protocol (HARP) [5] Discover the shortest path Establish forward and reverse path

18. 18 Simulation Model [Johansson, Perkins, Broach]  Traffic model: CBR 512 byte/packet 4 packets/second Source 10, 20, 30  Performance Metrics: Packet delivery fraction Avg. E2E delay Routing overhead  Movement model: Random way point [Yoon] 50 nodes 1500mx300m 0-20 m/s (or 1-20m/s) 900 simulated seconds Pause time=0, 30, 60, 150, 300, 600, 900 10 scenarios for each pause time  Beaconing: 10 s  QoC = 2 s + b

19. 19 Packet Delivery Fraction 10 sources 20 sources 30 sources Simple routing has a slightly better PDF in low traffic load Routing+TM outperforms simple routing up to 20% as the traffic load increases

20. 20 Avg. E2E Delay Routing +TM significantly improves the delay performance up to 200ms as the network conditions become stressful Shortest path is not enough 10 sources20 sources30 sources

21. 21 Routing Overhead (pkt) Simple Routing outperforms Routing+TM in low/medium traffic load Most of the packets produced by Routing+TM are the beacons 10 sources20 sources30 sources

22. 22 Routing Overhead (bytes) 10 sources 20 sources 30 sources Simple Routing outperforms Routing+TM in low/medium traffic load

23. 23 Summary Overall Performance Low traffic load Medium traffic load High traffic load Low Mobility Flat Routing Similar Routing +TM Medium Mobility Flat Routing Routing +TM High Mobility Similar Routing +TM

24. 24 Motivation for Route Selection The effect of mobility rate and traffic load on DELAY Issue: load balancing Adjusting the weight of QoC solves the problem of load balancing within a zone 10 sources20 sources30 sources Load Balancing

25. 25 Routing Issues Dilemma at a node: “Do I keep track of routes to all destinations, or instead keep track of only those that are of immediate interest?”  Three strategies: Proactive: keep track of all routes. Reactive: only those routes of immediate interest. Hybrid: partial proactive / partial reactive.

26. 26 Related Work-Routing Ad Hoc Routing Topology-basedPosition-based Proactive Reactive Hybrid OLSR TBRPF DSDV WRP CGSR FSH-HSR LANMAR ProactiveReactiveHybrid DSR AODV RDMAR ABR SSR TORA ZHLS ZRP CBRP HARP DREAM LAR Terminodes GLS ALM

27. 27 Route Determination [5,6]  HARP- Hybrid Ad Hoc Routing Protocol [5,6] Intra-zone and Inter-zone routing Relative distance estimation [Aggelou, Tafazolli] Quality of service support  Performance Results [Osafune]  Summary

28. 28 HARP: Hybrid Routing a b f c s y k d t x g v w z h u e i n z1z1 z2z2 z3z3 Zone abstraction Z1 Z3 Z2 Intra-zone Routing Inter-zone Routing mr z4z4 Z4 src zone dst zone Shortcut intra-zone routing - Zone level routing

29. 29 Mechanisms to Achieve Load Balancing  Inter-zone routing: route selection is done at the destination  Load balancing requires: A set of route candidates A set of route metrics  Issues: Congestion Single route metric  Proposed mechanisms : Relative distance estimation Quality of service metrics

30. 30 Relative Distance Estimation SRCDST Src Offset Dst Offset RD_Offset d x R … Offset= mobility x elapsed time

31. 31 Relative Distance Estimation SRCDST Src Offset Path_offset RD_Offset d x R … Offset= mobility x elapsed time

32. 32 Query Localization Technique dst d=1 d=2 d=3 d=k src x x if (rd(x,dst) > rd(src,d)) Drop PREQ; else if (rd(x,dst)<TTL) TTL=rd(x,dst); … PREQ

33. 33 Quality of Service Support [7] 2nd Class 3rd Class Delay Throughput QoS Service Best-Effort 1st Class h.(r-b)/c QoC c/(2h.(r-b)) 1/s Legend h : hop count r : buffer size b : buffer occupancy c : nodes’ throughput s : stability Application QoS Network QoC

34. 34 Simulation Model [Johansson, Perkins, Broach]  Traffic model: CBR 512 byte/packet 4 packets/second Source 10, 20, 30  Performance Metrics: Packet delivery fraction Avg. E2E delay Routing overhead  Movement model: Random way point [Yoon] 50 nodes 1500mx300m 0-20 m/s (or 1-20m/s) 900 simulated seconds Pause time=0, 30, 60, 150, 300, 600, 900 10 scenarios for each pause time

35. 35 Packet Delivery Fraction 10 sources 20 sources 30 sources The effect of mobility and traffic load is not uniform Congestion stems from the lack of load balancing in the protocols

36. 36 Avg. E2E Delay 10 sources 20 sources30 sources The effect of traffic load and mobility on the delay performance is non-uniform Load balancing : weight of QoC Relative distance estimation

37. 37 Avg. E2E Delay 10 sources20 sources30 sources Delay performance has significant improved QoS metrics is the key factor for load balancing effect

38. 38 Packet Delivery Fraction 10 sources20 sources30 sources QoS metrics has no significant effect on PDF Route discovery and route maintenance are the key factors for improving PDF Deal with Traffic load/pattern and mobility model/rate

39. 39 Summary Overall Performance Low traffic load Medium traffic load High traffic load Low Mobility HARP +TM Medium Mobility HARP +TM Similar HARP +TM High Mobility HARP +TM Similar HARP +TM

40. 40 Architecture Application HARP DDR Network  Topology Management With respect to Quality of Network  Route Determination With respect to Application requirements AN ARCHITECTURE THAT SEPARATES [1]: QoS Classes QoS: Delay TPut BE Network Quality QoC: Power, buffer Stability

41. 41 Conclusion Topology management improves routing Load balancing HARP  QoS metrics and RDE DDR  QoC metrics Control flooding overhead Forest  redundancy & collision Relative distance estimation  scope Scalability and delay performance  zone abstraction

42. 42 Conclusion Routing requires: Adaptive topology management Load balancing Congestion avoidance mechanisms Neighboring information Factors affecting routing performance Network size, mobility rate and model, traffic load and pattern, network density Traffic locality vs. network size

43. 43 Future Work  Optimal criteria of forest Construction  Introduce an adaptive routing mechanisms  Effect of the factors affecting routing performance

44. 44 Publications [1] N. Nikaein and C. Bonnet, “An Architecture for Improving Routing and Network Performance in Mobile Ad Hoc Network”, Kluwer/ACM MONET, 2003. [2] N. Nikaein, H. Labiod, and C. Bonnet, “DDR-- Distributed Dunamic Routing Algorithm for Mobile Ad Hoc Networks”, MobiHoc, 2000. [3] N. Nikaein, S. Wu, C. Bonnet and H. Labiod, “Designing Routing Protocol for Mobile Ad Hoc Networks”, DNAC, 2000. [4] N. Nikaein and C. Bonnet, “ Improving Routing and Network Performance in MANET using Quality of Nodes”, WiOpt, 2003

45. 45 Publications [5] Navid Nikaein, C. Bonnet and Neda Nikaein, “HARP- Hybrid Ad Hoc Routing Protocol ”, IST, 2001. [6] Navid Nikaein, C. Bonnet, N. Akhtar and R. Tafazolli, “HARP- v2 Hybrid Ad Hoc Routing Protocol ”, To be submitted, 2003. [7] N. Nikaein and C. Bonnet, “A Glance at Quality of Service models in Mobile Ad Hoc Networks”,DNAC, 2002. [8] N. Nikaein, C. Bonnet, Y. Moret and I. A. Rai, “LQoS- Layered Quality of Service Model for Routing in Mobile Ad Hoc Networks”, SCI, 2002. [9] N. Nikaein and C. Bonnet, “ALM-- Adaptive Location Management Model Incorporating Fuzzy Logic For Mobile Ad Hoc Networks ”, Med-Hoc-Net, 2002.

46. 46 References [Wu] DDR-Based Multicast Protocol with Dynamic Core (DMPDC), PFHSN 2002, Eurecom Institut. [Osafune] Performance Comparison of Ad Hoc Network Routing Protocol, IEICE 2002, Hitachi Co. [Rastogi] LQoS support for reactive ad hoc routing protocol, Tech. Report, Indian institute of Technology.

47. 47 Packet Delivery Fraction 10 sources20 sources30 sources The effect of mobility rate and traffic load on PDF Fluctuation Congestion Fluctuation

48. 48 Routing Overhead (pkt) 10 sources 20 sources30 sources Reaction to mobility (main cause of link failure) Caching (DSR), route request (AODV), periodical link state (OLSR), and beaconing and PREQ (HARP+TM)

49. 49 Routing Overhead (bytes) 10 sources 20 sources30 sources Reaction to mobility (main cause of link failure) Caching (DSR), route request (AODV), beaconing and PREQ (HARP+TM)

50. 50 Forest Construction  A forest is constructed by connecting each node to its preferred neighbor and vice versa.  Node k:  PN = f then B = (ZID, k, 4, 1, f)  Node f:  PN = y then B = (ZID, f, 5, 1, y) Periodical Beacon INTRA-ZONE TABLE OF NODES k AND f NID Learned_PN f NID Learned_PN y k a b f c s y k d t x c g

51. 51 Intra-Zone Clustering  Node k:  Learned_PN = c: d  B = (ZID, k, 4, 0, Learned_PN )  Learned_PN = a: b: q: y  B = (ZID, k, 4, 0, Learned_PN )  Node f:  Learned_PN = a: b: q: y: k  B = (ZID, f, 5, 0, Learned_PN )  Learned_PN = c: d: x: t  B = (ZID, f, 5, 0, Learned_PN ) NID Learned_PN f a, b, q, y, t, x c - d - NID Learned_PN y x, t k c, d b, a, q - INTRA-ZONE TABLE OF NODES k AND f a b f c s y k d t x c g

52. 52 Inter-Zone Clustering  Either a node can succeed to add some nodes to its intra-zone table.  Otherwise, it puts the remaining nodes in its inter-zone table. NID NZID Z_Stability r z4 ++ g z5 ++ INTER-ZONE TABLE OF NODE d

53. 53 Zone Naming  Select q highest ID # in intra-zone table, where  Compute a hash function on each selected ID # separately.  Concatenate all the hashed ID #.  Node k (for n=12 & d=3) :  q = 4  selected nodes: y, x, t, q  h(y)|h(x)|h(t)|h(q)  Z 2 = y’x’t’q’

54. 54 Correctness Theorem: For any graph G (i.e. networks topology), let G’ be the graph obtained by connecting each node to its PN. Then G’ is a forest. Label(x) = Max {deg(w)|NID} w  PN x  deg(x i-2 )  deg(x i ), then x i  PN xi-1.  deg(x i-2 ) = deg(x i ) & NID(x i-2 )  NID(x i ).

55. 55 Problem Definition ? Trade-off between routing overhead and delay while maximizing network utilization Trade-off between load balancing and resource conservation Separation between topology management and route determination What are the design elements of routing and how they must interact ?

56. 56 Zone Behaviors As the number of zone increases, the overhead within a zone decreases but the overhead between zones increases Whatever the network density is, the zone diameter is bounded to 8 What is the optimal number of zones to achieve the minimum overall overhead ? M opt <  N Number of Zones Size of Zones

57. 57 Zone Behaviors Whatever the network density is, the zone diameter is bounded to 8 The average ratio of tree-path to shortest path is no longer than 2 Low Complexity O(N) Zone Diameter Tree Path / Shortest Path

58. 58 Stability Level  Node mobility a basic characteristic of ad hoc networks.  A point of difference from wired networks.  Critical to capture this node mobility in the routing protocol in order to determine stable, reliable routes.  We propose: N to : neighbors at time to. N t1 : neighbors at time t1.

59. 59 Stability Level  0  stab(x)  1  stab (x) = 1  high stability, stab (x) = 0  low stability  Numerator: nodes that have remained in the neighborhood of x.  High stability if none (few) of its neighbors change, low stability if many (all) of its neighbors change.

60. 60 Stability  Example: Stability also reflects connectivity. A large degree node, in general, more stable. t0t0 t1t1 3/4 0 1/2 1 1 1 1

61. 61 A path of lower hop count has higher delay, because of high buffering delays. The other path of a possibly higher hop count, but lower delay. (also load balancing). Example

62. 62 Service differentiation Outgoing traffic Class I Class II Class III 60% 30% 10%

63. 63 Status Concept  A node broadcasts is QoS state itself.  This QoS state reflects node’s ability to act as a router. If selfish, then ceases to act as a router, doesn’t take part in routing protocol.  Since propagated by the node itself, the possibility of lying. (malicious behavior) pretend to be in selfish mode, to avoid being chosen for forwarding.

64. 64 Status Concept  A selfish node does not serve the network (does not route), hence, fair that it receives poor services from the network.  Status: the fraction of time a node has been in unselfish mode to the network.  The status of node y at node x: N u [y] : number of y’s beacons in unselfish mode. N [y] : total number of y’s beacons received.

65. 65 Status Concept  How to prevent malicious behavior?  Give a status to each node, and give incentive to a node to act as a router.  If a node acts as a router, then it is not in selfish mode.  Prefer packets generated by unselfish nodes while routing.  Hence, a node that has remained selfish, receives poor services from the network.

66. 66 Quality of Service Definition  To provide a set of service requirements to the applications while Routing through the network, e.g. end-to-end delay. Even Internet today, with high-speed high-quality fixed communication links, is unable to deliver guaranteed end-to-end services.

67. 67 QoS Routing Issues  State of communication path should be considered in QoS routing: Resource availability and its stability Cause longer path than shortest path Trade-off between shortest path and optimal path

68. 68 Assumption  Fully symmetric environment All nodes have identical capabilities  Capabilities: Transmission range, Battery life, processing capacity, buffer capacity  Each node periodically sends a Beacon  All nodes participate in protocol operation and packet forwarding [Michardi, Molva, Crowcroft]

69. 69 Motivation  The low-capacity time-varying resources make maintaining accurate link state and topology information very difficult  Even Internet today, with high-speed high-quality fixed communication links, is unable to deliver guaranteed end-to-end service

70. 70 Quality of Service Definition  To provide a set of service requirements to the applications while Routing through the network e.g. end-to-end delay Even Internet today, with high-speed high-quality fixed communication links, is unable to deliver guaranteed end-to-end services.

71. 71 Related Work- Quality of Service  Two examples in the wired Network: IntServ: per-flow end-to-end guarantee DiffServ: Per-class service differentiation  An example in Manet: FQMM: Flexible QoS Model for Manet  IntServ is used for high priority classes  DiffServ is used for low priority classes They require accurate link state and topology information

72. 72 DiffServ & IntServ & FQMM  Require accurate link state and topology information  The low-capacity time-varying resources make maintaining accurate link state and topology information very difficult Quality of service that an application requires depends strictly to the quality of network

73. 73 Network Metrics  Hop count  resource conservation  Buffer Level  Stability Level Load balancing Trade-off between load balancing & resource conservation Compute during path discovery using concave function Reflect the quality of the communication paths Map this quality to application metrics

74. 74 Conclusion- Architecture Routing Topology management Route determination Quality of connectivity (QoC)Quality of Service (QoS) Pro-Network Pro-Application