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1 Network Wide Broadcast in Multi-Hop Wireless Networks Some slides adapted from Broadcast Storm author’s presentation.

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Presentation on theme: "1 Network Wide Broadcast in Multi-Hop Wireless Networks Some slides adapted from Broadcast Storm author’s presentation."— Presentation transcript:

1 1 Network Wide Broadcast in Multi-Hop Wireless Networks Some slides adapted from Broadcast Storm author’s presentation

2 2 Network Wide Broadcast Problem  Network Wide Broadcast (NWB):  send a message to all nodes in the network  May not have topology information  NWB used for  Routing  Paging  Group communication  Try to take advantage of MAC level broadcast  One transmission covers multiple receivers  Don’t have to know who is in range with you

3 3 Preliminaries  The broadcast is spontaneous.  no synchronization  no prior global topology knowledge – we relax this later  The broadcast is unreliable.  no acknowledgement of any kind Why? Think about it not to cause more contention 100% reliability is unnecessary for some application  Performance Metrics?  Overhead—does it make sense? How about normalized overhead?  Robustness—how to measure that? (coverage?)

4 4 Saving Rebroadcasts source Can cover all—but extra Latency and lower reliability Flooding—high overhead, But shortest paths? 5 forwarding nodes 4 hop time source 6 forwarding nodes 3 hop time

5 5 NWB by Flooding  A straight-forward approach  A host rebroadcasts the message on receiving a broadcast message for the first time.  Broadcast storm problem:  redundant rebroadcasts  contention problem  collision problem  But maybe the redundancy is useful to improve robustness? Next time we examine this angle

6 6 Analysis of Redundancy  Additional Coverage provided by a rebroadcast.  The max. additional coverage is 61%.  The coverage is 41% in average.  The expected additional coverage EAC(k)/  r 2 after a host heard a broadcast message k times.

7 7 Analysis on Contention  When a host broadcasts, its neighbors are likely to contend with each other for the medium.  A ==> B, C, D  B, C, D could seriously contend with each other. Analysis on Contention A D C B

8 8 Broadcast Losses  Higher Possibility of Collision and broadcast loss:  Rebroadcasts are likely to start at the same time. Backoff window runs out if medium is quiet for a while.  hidden terminal problem  Wireless propagation losses  No Acknowledgements and retransmits—losses are fatal we get only one shot  What are the implications?

9 9 Broadcast Storm Problem Summary  Redundancy  Contention  Collision and losses  How to derive an efficient scheme for broadcasting in a MANET?

10 10 Possible Broadcast Solutions  Probabilistic Scheme  Counter-Based Scheme  Distance-Based Scheme  Location-Based Scheme  Cluster-Based Scheme

11 11 Serious Redundancy  Optimal broadcasting vs. Flooding (a) optimal = 2 steps (b) optimal = 2 steps  Severity of Redundant Coverage.

12 12 Probabilistic Scheme  Rebroadcast by “Tossing a Die”  A host always rebroadcasts with a certain probability P.  When P = 1, this is flooding.  A smaller P will reduce the storm effect.  This approach is also known as gossiping in distributed systems  It has well known and analyzed properties

13 13 Simulation Parameters  no of hosts = 100  transmission radius = 500 meters  packet size = 280 bytes  transmission rate = 1 M bits/sec  broadcast arrival rate: 1 per sec. to the whole map  map: (1 unit = 500 meters)  1x1, 3x3, 5x5, 7x7, 10x10  roaming pattern: random walk  speed: 0~10 km/hr in a 1x1 map, 0~30 km/hr in a 3x3 map, etc.  IEEE

14 14 Performance of Probabilistic Scheme RE = REachability (in lines) SRB = Saved ReBroadcast (in bars) Latency

15 15 Observation  Reachability:  In smaller maps, a low P is sufficient to achieve high reachability.  A larger P is needed in a larger map.  Saved Rebroadcast:  linear with respect to P  Latency:  Interestingly, in smaller areas, broadcast tends to complete in a slower speed.

16 16 Counter-Based Scheme  If a host has received a broadcast packet > C times,  then do not rebroadcast.  Examples: Addition Coverage  1 time => 41%  2 times => 19%  3 times => 9%  4 times => 5%  > 4 times, very little extra area

17 17 Performance of Counter-Based Scheme  We vary C = 2, 3,..., 6 to observe the performance.  A larger C means more rebroadcast.

18 18 Observation  Reachability:  C >= 3 can offer a reachability close to flooding.  Saved Rebroadcast:  In denser area, there is more saving. In sparser area, there is less saving.  Latency:  Higher latency is smaller area.

19 19 Distance-Based Scheme  Calculate the distance to the sending host.  d min = Min{the distance to each sending host}  If d min < D (a threshold), then do not rebroadcast.  How to find distance:  signal strength  GPS devices

20 20 Performance of the Distance-Based Scheme  We vary D = 147, 72, 37, 20, 11 to observe the effect.  Smaller D means more rebroadcasting.

21 21 Observation  Why choosing D=147?  addition coverage = 0.187, equal to that of C=2  Reachability:  All look good, close to flooding.  Saved Rebradcast:  not much  Latency:  smaller area has higher latency

22 22 Location-Based Scheme  From GPS to obtain the sender’s location.  Let (x 1, y 1 ), (x 2, y 2 ), (x 3, y 3 ),..., (x k, y k ) be locations of senders.  We can accurately calculate the additional coverage of this rebroadcast.

23 23 Difficulty  Involve complicated math to calculate the extra coverage.  A lot of calculus!  Approximation:  grid simulation S1S1 A S2S2 S3S3

24 24 Performance of the Location-Based Scheme  We vary A (addition coverage) from 0.1 to  Smaller A means more rebroadcast.

25 25 Observation  Why choosing A=0.187?  This is additional coverage offered by C=2.  Best performance over all the above schemes!

26 26 Modified Location-Based Schemes  Polygon Test:  If a node is within the polygon formed by the locations of senders, then DO NOT rebroadcast. (Fig. (a))  Otherwise, rebroadcast. (Fig. (b))  If a host is within the convex, the maximum additional coverage is well below 22%. (Fig. (c))

27 27 A Short Summary  Main Concern:  Extra coverage of a rebroadcast  Different levels of accuracy:  probabilistic, counter, distance, location, polygon  Probabilistic not sensitive to the importance of a retransmission  Maybe ok in dense networks  Others are sensitive, with increasing accuracy of estimate (and increasing amount of work needed)  Counter-Based Scheme < Distance-Based Scheme < Location-Based

28 28 Cluster-Based Scheme  Cluster formation algorithm  Each host has a unique ID  A host with a local minimal ID will elect itself as a cluster head.  This head host together with its neighbor will form a cluster.  These neighbor hosts are called member of the cluster.

29 29 Cluster-Based Scheme  Cluster formation protocol:  A head’s rebroadcast can cover all other hosts in that cluster if its transmission experience no collision.  Gateway hosts should take the reponsibility to propogate the broadcast msg to hosts in other clusters.  There is no need for a non-gateway member to rebroadcast the msg.

30 30 Follow up work by the same authors (ICDCS 2001)  In the above solutions, the thresholds used are ALL FIXED.  NOT sensitive to the current status of the network.  Example: In the counter-based scheme, we may need different threshold C depending on the density of the network.  Why not make the threshold adaptive to the network status?  They explore this idea– it works fine

31 31 Brief Taxonomy of Approaches  Topology Ambivalent (or flooding based)  No knowledge of neighbors assumed  Starting point is flooding  These are the broadcast storm solutions we’ve seen so far  Another approach is possible – Topology aware  General approach: use network topology information to intelligently decide who should rebroadcast Cluster based approach is an example  Centralized solution: figure out the minimum set of rebroadcasting nodes to cover all (Minimum Connected Dominating Set MCDS) Centralized– unrealistic Does not consider that rebroadcasts are unreliable

32 32 Minimum Forwarding Set Problem  Define:  Given a source A  let D and P be the sets of k and k+1 hop neighbors of A  Find a minimum-size subset F of D such that every node in P is within the coverage area of at least one node from F  In general graph:  NP-complete: reduce “Set Cover” to it  Approximation ratio: logn  In unit disk graph:  Unknown  Approximation ratio: constant

33 33 Minimum Broadcasting Set Problem  Define:  Given a source A  Find a spanning tree T such that the number of internal nodes is minimum  In general graph:  NP-hard: hard to “Minimum Connected Dominating Set”  Approximation ratio: log  (  is the maximum node degree)  In unit disk graph:  NP-hard  Approximation ratio: constant

34 34 Hiearchical: Domination-set-based School bus routing Next few slides from Prof. Jie Wu at FAU

35 35 Graph-theoretic Definition A set in G(V, E) is dominating if all the nodes in the system are either in the set or neighbors of nodes in the set.

36 36 Five-Queen Problem (1850’s)

37 37 Desirable Features  Simple and quick  Connected dominating set Figure 6: A simple ad hoc wireless network of five wireless mobile hosts.

38 38 Existing Approaches  Graph theory community:  Bounds on the domination number (Haynes, Hedetniemi, and Slater, 1998).  Special classes of graph for which the domination problem can be solved in polynomial time.

39 39 Existing Approaches (Cont’d.)  Ad hoc wireless network community:  Global: MCDS (Sivakumar, Das, and Bharghavan, 1998).  Quasi-global: spanning-tree-based (Wan, Alzoubi, and Frieder, 2002).  Quasi-local: cluster-based (Lin and Gerla, 1999).  Local: marking process (Wu and Li, 1999).

40 40 MCDS (Sivakumar, Das, and Bharghavan, UIUC)  All nodes are initially colored white.  The node with the maximum node degree is selected as the root and colored black. All the neighbors of the root are colored gray.  Select a gray node that has the maximum white neighbors. The gray node is colored black and its white neighbors are marked gray.  Repeat step (3) until there is no more white node.

41 41 MCDS (Cont’d.) black nodes = CDS (connected dominating set) Figure 7: MCDS as an approximation of CDS

42 42 Spanning-tree-based (Wan, Alzoubi, and Frieder, IIT)  A spanning tree rooted at v (selected through an election process) is first constructed.  Nodes are labeled according to a topological sorting order of the tree.

43 43 Spanning-tree-based (Cont’d.)  Nodes are marked based on their positions in the order starting from root v.  All nodes are white initially.  V is marked black and all nodes are labeled black unless there is a black neighbor.  Each black node (except root v) selects a neighbor with the largest label but smaller than its own label and mark it gray.

44 44 Spanning-tree-based (Cont’d.) black nodes = DS black nodes + gray nodes = CDS Figure 8: selecting CDS in a spanning tree

45 45 Cluster-based (Lee and Gerla, UCLA)  All nodes are initially white.  When a white node finds itself having the lowest id among all its white neighbors, it becomes a cluster head and colors itself black.  All its neighbors join in the cluster and change their colors to gray.

46 46 Cluster-based (Cont’d.)  Repeat steps (1) and (2) until there is no white node left.  Special gray nodes: gray nodes that have two neighbors in different clusters.

47 47 Cluster-based (Cont’d.) black nodes = DS black nodes + special gray nodes = CDS Figure 9: sequential propagation in the cluster-based approach.

48 48 Localized Algorithms  Processors (hosts) only interact with others in a restricted vicinity.  Each processor performs exceedingly simple tasks (such as maintaining and propagating information markers).  Collectively these processors achieve a desired global objective.  There is no sequential propagation of information.

49 49 Marking Process (Wu and Li, 1999)  A node is marked true if it has two unconnected neighbors.  A set of marked nodes (gateways nodes) V’ form a connected dominating set.

50 50 Marking Process (Cont’d.) Figure 10: A sample ad hoc wireless network

51 51 Dominating Set Reduction  Reduce the size of the dominating set.  Role of gateway/non-gateway is rotated.

52 52 Dominating Set Reduction (Cont’d.) N [v] = N (v) U {v} is a closed neighbor set of v  Rule 1: If N [v]  N [u] in G and id(v) < id(u), then unmark v.  Rule 2: If N (v)  N (u) U N (w) in G and id(v) = min{id(v), id(u), id(w)}, then unmark v.

53 53 Dominating Set Reduction (Cont’d.) Figure 12: two sample examples

54 54 Example Figure 13: (a) Dominating set from the marking process (b) Dominating set after dominating set reduction

55 55 Topology Aware Approaches  Neighborhood information  How to decide forwarding nodes  Dynamically, or Neighborhood base Scalable Broadcast Algorithm (SBA), Flooding with Self pruning  Statically, or Set cover base Multipoint relaying, Dominant pruning, Ad hoc Broadcast Protocol (AHBP)  MCDS base

56 56 Scalable Broadcast Algorithm (SBA)  Information:  Hello message (2-hop)  Forwarding node decision:  Node v j who receives the packet from v i checks whether the set N(v j )- N(v i )-{v i } is empty  Node v j schedules the packet for delivery with a RAD (Random Assessment Delay)  Dynamically adjust the RAD to (nodes with the most neighbors usually broadcast before the others)

57 57 Self pruning  Information:  Hello message (1-hop)  Piggyback adjacent node list in broadcast packets (2-hop)  Store adjacent node list in cache  Forwarding node decision:  Node v j who receives the packet from v i checks whether the set N(v j )-N(v i )-{v i } is empty vivi vjvj

58 58 Multipoint relaying  Information:  Hello message (2-hop)  Forwarding node decision:  The sending node A selects forwarding nodes from it’s adjacent nodes  A select a minimum node set F  N(A) such that:  A node set U = N(N(A)) – N(A)  Piggyback forward list in “Hello” packets

59 59 Dominant pruning  Information:  Hello message (2-hop)  Forwarding node decision:  The sending node selects forwarding nodes from it’s adjacent nodes  Node v j who receives the packet from v i, v j select a minimum node set F  N(v j ) - N(v i ) such that:  A node set U = N(N(v j )) – N(v i ) – N(v j )  Piggyback forward list in broadcast packets

60 60 Dominant pruning vivi vjvj N(N(v j )) B(v i,v j ) U N(v i ) N(v j )

61 61 The drawback of present set cover based protocols 1 … i-1 i i+1 i+2 v i-1 vivi s When a node v i received the broadcast packet from node v i-1, it will select some forwarding nodes from N(v i )-N(v i-1 ) to cover all nodes in U. However, some nodes in U are not i+2 level nodes, and some nodes in N(v i )-N(v j ) are not i+1 level nodes.

62 62 The drawback of present set cover based protocols 2 … i-1 i i+1 i+2 v i-1 v i1 s When we will select some level i+1 nodes to cover all level i+2 nodes, the number of forwarding nodes selected by distributed algorithm can not be bounded to some ratio of the optimal solution ? v i2

63 63 Comparison 350x350 r:100

64 64 Robustness of Network Wide Broadcasts (NWBs)

65 65 NWB Algorithms--Recap  Optimized NWB algorithms cut down on the number of broadcasts – Redundancy control  Topology Aware vs. Topology Ambivalent  Aware: Collect neighbor information, construct a virtual backbone (CDS Approaches)  Ambivalent: Start with flooding, cut down on some broadcasts (based on local criteria)  Tradeoffs: Aware can reduce redundancy, has additional neighbor-tracking overhead

66 66 NWB Algorithms - 2  Static vs. Dynamic (Topology-Aware)  Static – Forwarding nodes pre-selected by previous hop  Dynamic – Nodes locally decide whether to rebroadcast (calculates whether some neighbors are uncovered)  Tradeoffs – Static algorithms are more optimal (lower overhead), dynamic algorithms adapt to losses better

67 67 NWB Robustness Problem  Optimizing NWBs cuts down on redundancy  Key broadcasts can be lost, isolating sections of the network  Metrics of interest:  Coverage – Percentage of reachable network covered  Normalized Overhead – Cost associated with the NWB operation

68 68 Simulation Information  NS-2, MAC protocol  20 scenarios of 30 nodes (randomly distributed) – 20 random seeds for each scenario  Static scenarios (some tests done with probabilistic random walk)  Propagation models  Two Ray Ground  Shadowing – Idealized & harsh signal fading model

69 69 Characterization of NWB Robustness - Flooding Coverage

70 70 NWB Robustness  Protocols studied:  Flooding  Location-Based Algorithm – from broadcast storm paper Topology ambivalent  Ad Hoc Broadcast Protocol (AHBP) Static CDS  Scalable Broadcast Algorithm (SBA) Dynamic CDS  Double-Covered Broadcast (DCB) – Static CDS with double coverage (Infocom 2004) Only topology-aware algorithm with robustness control

71 71 NWB Robustness Trends  Sparse networks are more prone to losses  Topology-aware protocols are not as robust when losses occur  Topology-aware protocols have lower overhead  Static CDS approaches are not as robust as dynamic approaches

72 72 Coverage of NWB Protocols

73 73 NWB Coverage (Mobility)

74 74 Robustness Control  Broadcast Storm  optimize to reduce redundancy  Losses  figure out when to introduce redundancy to compensate  Robustness control  An effective NWB must include both aspects  Core issue is MAC level broadcast is unreliable  Robustness vs. Reliability

75 75 Solution Space for Robustness Control  Loss-sensitive vs. fixed redundancy  Loss-sensitive implicit or explicit loss detection?  Fixed redundancy: State-sensitive or blind redundancy?  Network level vs. MAC level implementation  Network level easier to implement and deploy +can take wider range of measures in space or time -But must build on what exist at MAC level

76 76 Explicit Feedback  Receivers directly receive feedback information regarding the NWB success  Granularity of feedback: Every packet or periodically?  Number of reports: Every node, some nodes, or a single node?

77 77 Implicit Feedback  Predict losses based on observed behavior  No explicit feedback  Difference between a reception and a loss is the subsequent rebroadcasts  Observe overheard rebroadcasts  Can do better with some state information on expected rebroadcast behavior

78 78 MAC Primitive by Pei and Gerla (circa 1999)  Not directly focused at NWB—rather, they just want to make MAC broadcast more reliable  Idea, Broadcast, but require acknowledgements  No ACKs? Rebroadcast  One ACK? Good, at least one person received it  ACK explosion? There will be noise on the channel due to ACK collisions Optimistically guess that it is ACKs  Ensures at least one receiver  What is this approach in terms of the solution space?  How will it work?  Requires

79 79 Another MAC Primitive--Directed Broadcast  Problem—many potential receivers; difficult to carry out ACK/retransmit with multiple partners  Directed Broadcast: pick a partner to ACK  Partner responds with ACK  Others receiver but do not ACK  “Public Unicast”  Also does not ensure that all receive  But minor modifications to MAC; no new physical abilities needed  Reduces load on network (not all need to ACK)  Allows use of RTS/CTS if desired

80 80 Directed Broadcast  Gives control of who is guaranteed to receive  this can be exploited by topology-aware NWB algorithms E.g., CDS algorithms can pick a CDS member as partner Works very well for these algorithms  But, is more difficult to use by topology-ambivalent algorithms  You need to know at least one partner  What type of solution is it?

81 81 Receiver Driven Sequence Numbers (Gerla)  NWB originator uses sequence numbers for its broadcasts  Each receiver keeps the last few broadcasts  Big drawback  When a receiver receives a packet from one of its neighbors, and it discovers a “hole” in the sequence numbers, it asks for a rebroadcast of the missing packet  Explicit feedback, NAK based  Can it ensure full reliability?  Delay bounded?

82 82 Variations/Other possibilities  Stagger ACKs to allow multiple responders  Why?  Tricky, especially when responders not known  Variation of this exploited for Anycast—will discuss later  Directed Broadcast with multiple partners  Repeat the broadcast to a subset of nodes  If all nodes, it becomes unicast  Can pick all members of the CDS for example  All of these (schemes + these variations) are MAC based, explicit feedback with packet level granularity

83 83 Other Robustness Control  Hyper-gossipping/Hyper-flooding  Start with flooding or gossiping, and with a probability retransmit again  Network-level, fixed redundancy, not- state aware  Only Gossiping and flooding supported, not the more advanced redundancy control mechanisms But maybe the other schemes can be supported too?  Double-Covered Broadcast (DCB)–Construct a CDS that is doubly covered Each node is part of the CDS or is covered by 2 CDS members Tradeoff vs. a traditional CDS? Network-level, fixed redundancy, not-state aware

84 84 Selective Additional Rebroadcast (SAR)

85 85 SAR  Areas of concern are situations where loss is likely and the network has low redundancy  Can we use implicit feedback to speculate whether a packet has been lost or not?  Can we use state information to improve our guess?  Network-level, implicit feedback, possibly state aware  Basic idea:  Implicit loss detection  Retransmit if loss is suspected  Can be layered on top of any “redundancy control” NWB approach

86 86 Loss Prediction Approaches - 1  Probabilistic  A node that broadcasts will perform a second broadcast based on some probability p Does not use any feedback, resulting in poorer results Blindly adds robustness  Counter-based  A node that broadcasts waits to hear if the packet was sent by n neighbors Uses implicit feedback Not blind, but not adaptive

87 87 Loss Prediction Approaches - 2  State-Aware (Adaptive)  Disable SAR if past broadcasts have not been helpful  Neighbor-knowledge protocols can ensure rebroadcasting nodes heard the broadcast  Observe MAC utilization

88 88 SAR Approaches Comparison  Probabilistic – Fixed level of overhead, some gain in coverage, some rebroadcasts are wasteful, some needed rebroadcasts were missed  Counter-Based – Gain in coverage, not fully- optimized in terms of overhead (leaf nodes tend to rebroadcast)  Adaptive – Results at or above counter-based, lower overhead. Adjusts to network conditions

89 89 SAR Results

90 90 SAR Overhead

91 91 Link Quality Sensitive CDS

92 92 Signal Fading  Chance of packet reception is based on a number of factors  Transmit power  Distance between nodes  Obstacles in propagation path

93 93 Packet Reception

94 94 Link Quality Aware CDS  Like DCB, this is a fixed-redundancy & topology- aware approach  Sensitive to link states One good link may be better than two poor links  Build CDS that keeps weights on each path  Attempt to cover all nodes with some probability P

95 95 Example AB C

96 96 LQ CDS Coverage

97 97 LQ CDS Overhead

98 98 LQ-CDS  Work in progress; obviously needs a lot of work  Problems: 1. Shadowing model is too harsh  Most links have extremely poor quality—network effectively very poorly connected  Should consider a less harsh model or denser scenarios where there are at least some ok links to work with 2. Does not consider that the probability of coverage is conditional on all the sources being covered  Heuristic Solution: increase retransmit probability with number of hops from source  Track the cumulative probability


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