Overview

1. SHORT: Self-Healing and Optimizing Routing Techniques for Ad Hoc NetworksChao Gui and Prasant MohapatraUniversity of California

2. Overview • Background • SHORT Introduction • Shortest Path Routing • Shortcutting Established Routes • PA-SHORT • Algorithm, Limitations, Implementation • Energy Aware Routing • EA-SHORT • Performance • Conclusion

3. Background • Mobile Ad-hoc Network Routing Challenges: • Dynamic network topology • Low transmission power • Low available bandwidth • Reactive routing protocols lack knowledge about network topology and node movement • Routes may result in less than optimal paths • Path Length • Energy Consumption • Unbalanced Path Loads

4. Background • Suboptimal routing • Increases end-to-end delay • Accelerates energy consumption of nodes • Decreases bandwidth • Reduces lifetime of routes • Routing protocols must adjust to a changing mobile network without: • Incurring too much control message overhead • Consuming too much energy from nodes in the network

5. Introducing SHORT • SHORT can optimize any routing algorithm that provides a reachable path between source and destination. • SHORT includes two classes of algorithms • Path-Aware (PA-SHORT) • Energy-Aware (EA-SHORT) • Nodes neighboring a routing path monitor the route and try to optimize sub-paths: • Reducing hops • Reducing overall energy consumption • Preserving power of low-energy nodes • Goal: Optimize performance and energy conservation without incurring significant overhead.

6. Shortest Path Based Routing • Mobile nodes cause network topology to change in unpredictable ways. • Most routing techniques perform initial path discovery but do not initiate new path discovery process until a link fails. • Some routing optimization techniques actually cause non-optimality in large networks • ER (Expanding Ring) Search • Local Repair • Simulations on large (10,000 node) networks show average path lengths increasing to more than double the optimal length with these techniques

7. Shortest Path Degradation Most optimal path:8 hop path from A to I A G C E F H B I D J

8. Shortest Path Degradation C No longer optimal I B H D F A E J G Most optimal path:5 hop path from A to I

9. Shortcutting Routing Paths • In a given routing path, an (n,k) reduction implies that n routing hops can be reduced to k routing hops, where k < n. • Hops are edges of the network graph. • Intuitively, the higher the difference between n and k, the greater the performance gain will be. Original Path A-B-C A-B-C-D Reduced Path A-C A-E-D Reduction (2,1) (3,2)

10. Shortcut Examples Gui and Mohaptra

11. Frequency of Shortcut Scenarios • To provide significant performance gain, the shortcut scenarios must occur frequently. • Simulations were performed to determine the probability of adjacency of nodes. • Given a path v0, v1, … vn+1 of n+2 nodes in the simulated network, what is the probability that nodes v0 and vn+1 are adjacent? • We denote this probability by P[n], and term it the probability of adjacency. • A (2,1) shortcut is equivalent to P[1]. • A (3,2) shortcut is equivalent to P[3].

12. Frequency of Shortcut Scenarios • P[1]is shown to derive as 58.6% • For greater values of n, computer simulations were performed repeatedly. • Simulation assumption: For the given set of n + 1 nodes along the n step path, the position of the next-hop node is uniformly distributed within the unit circle. • In a mobile ad-hoc network where routing paths are setup by a routing protocol, it is more probable that the next-hop node is toward the direction of the destination node.

13. Frequency of Shortcut Scenarios • Further experiments were performed using the ns-2 simulator. • 100 node network simulated within a field of 2000m x 600m with random waypoint mobility. • Traffic simulated between source node S and destination node D using AODV for routing. • To ensure significant distance between S and D, position and movement of these nodes is restricted.

14. Frequency of Shortcut Scenarios • More frequent mobility and faster mobility within denser networks results in highest shortcut availability. • About 40% of packets encountered (n,2) shortcuts. • About 10% of packets encountered (n,1) shortcuts. • Thus, a routing scheme that utilizes both shortcuts will converge to a shortest path much faster than one that uses only (n,1) shortcuts. Gui and Mohaptra

15. Path Aware (PA)-SHORT • PA-SHORT monitors an established routing path, identifies shortcuts, and shortens the path according to those shortcuts. • Two subclasses of PA-SHORT • PA-SHORT-DV works with distance vector algorithms such as AODV. • PA-SHORT-SR works with source routing protocols such as DSR.

16. PA-SHORT: Basic Idea • Packet forwarding is broadcast wirelessly, so all nodes within range can hear the packet. • In a typical wireless broadcast: • Each node checks the packet header to see if they are the next hop for the packet. • The node that is the next hop captures the packet and processes it for the next hop. • Other nodes generally disregard the packet. • So neighboring nodes get to check all packets that they can overhear.

17. PA-SHORT: Header Information • To support PA-SHORT each packet header must carry the following field: • Hop Count field: HC • HC is initially zero at the source node and is incremented by one at every hop. • Other important information in the header: • Source address: SA • Destination address: DA

18. PA-SHORT: Hop Comparison Array • Hop Comparison Array • Stored at each node, consists of: • Source Address: SA • Destination Address: DA • Hop Count: HC • Neighbor Address: NA • NA is the neighbor address from which the overheard packet was broadcast • Prior to the first transmission, the HCA of node SAk contains <SAk, DAk, 0, SAk >

19. PA-SHORT: Algorithm Process the packet’s final destination. Write to HCA if the packet is a new flow. Process the packet’snext hop. Search for shortcuts. “Have I seen this same packet at least 2 hops ago?” Modify routing tables and clean up the hop comparison array. Gui and Mohaptra

20. PA-SHORT: Example C <A to I, HC=2> B I <A to I, HC=5> <A to I, HC=1> H D <A to I, HC=7> <A to I, HC=3> F A <A to I, HC=6> <A to I, HC=0> E J G <A to I, HC=4> Suppose node A wants to send a message to node I. Best viewed in slideshow mode.

21. PA-SHORT: Overhead • Overhead is minimal • Each node requires only a small amount of space to store the hop comparison array. • Only a small amount of processing required to detect shortcuts. • Only one extra message is necessary to inform a neighboring node to update its routing table.

22. Limitation: Source (2,1) Shortcut • A (2,1) shortcut involving the source node will not be detected. Gui and Mohaptra

23. Limitation: Destination Shortcut • Shortcuts involving the destination node will not be detected. Gui and Mohaptra

24. PA-SHORT: Implementation • Ephemeral Shortcuts • When a fast-moving node forms a shortcut, and causes routing table changes, it may only remain in the effective shortcut position range for a short period of time. • Avoidance requires position and speed data. • Multiple Shortcuts • More than one node lies within the position range of a certain shortcut, and all report the shortcut to the relevant nodes, causing multiple route changes. • Can be avoided by using a stable period in which new or updated routing entries cannot change.

25. Energy Aware Routing • Mobile devices typically have limited power. • Wireless network interfaces are a major source of energy consumption. • Energy aware routing aims at continuously monitoring changing conditions in link quality, contention rate, and tries to heal and optimize the path by diverting it to a better path, when one is available. • Route packets through nodes with sufficient power supplies, avoid nodes that are low on power, and balance traffic.

26. Energy Aware Load Balancing • A heavily-used routing path will leave other nodes relatively free from traffic load. • Energy of nodes on this path will drain early. • An energy-aware routing scheme will divert traffic to an alternate path. • EA-SHORT: Basic Idea • Listen to packets broadcast from neighbor nodes. • Monitor energy levels of neighboring nodes. • Nodes with higher energy levels volunteer to forward packets for nodes with lower energy levels.

27. Alternative Sub-Paths • In a dense network, alternate sub-paths of optimal path length may occur frequently. • Utilization of these sub-paths will help balance out the energy levels of nodes in the network. Gui and Mohaptra

28. EA-SHORT: Packet Information • To support EA-SHORT, we add two fields to each packet, P. • Hop Counter: hc(P) • Residual energy level: lvl(P) • Other packet information of interest: • Source: s(P) • Destination: d(P) • Sender: nid(P) • Sequence Number: seq(P) • Source/Dest. Pair: s-d(P)

29. EA-SHORT: Eavesdropping • Each node maintains an “overhear table” • Each entry represents a traffic flow in the network, identified by the Source/Destination pair field. • An overhear table entry is represented by e. • Source/Destination Pair: s-d(e) • Sequence Number: seq(e) • Overhear List: ovlist(e) • Overhear List Entry: hc, lvl, nid

30. EA-SHORT: Algorithm Check Overheard Packetsand Add If From a New Flow For Familiar Flows:Ignore Earlier Sequences,Reset on Later Sequences, Record Matching Sequences Identify a 2-Step Alternate Sub-path and Activate New Sub-path Gui and Mohaptra

31. EA-SHORT: Algorithm Identify Upstream Node of a 3-Step Alternate Sub-path Identify Downstream Node of a 3-Step Alternate Sub-path Listen for Cooperating 3-Step Path Nodes and Activate New Sub-path Gui and Mohaptra

32. EA-SHORT: Example Consider a high-traffic path A-B-C-D. 5 3 4 8 6 7 3 5 4 7 8 6 3 7 6 8 4 5 6 7 3 4 5 8 A B C D E F G 3 4 5 6 7 8 7 3 4 8 6 5 3 4 5 6 7 8 Let’s enable the EA-SHORT algorithm. Best viewed in slideshow mode.

33. EA-SHORT: Example <S to D, seq=2, hc=1, lvl=6> <S to D, seq=2, hc=2, lvl=6> <S to D, seq=2, hc=3, lvl=6> 3 4 5 6 5 6 3 4 3 4 5 6 6 7 8 9 A B C D E F G 3 4 5 6 7 8 8 7 6 4 3 5 3 4 5 6 7 8 Waiting: B Waiting: C SHORT: B Best viewed in slideshow mode.

34. EA-SHORT: Example <S to T, seq=4, hc=1, lvl=4> <S to T, seq=4, hc=4, lvl=4> 1 2 3 4 2 3 4 5 1 4 2 3 5 6 7 8 A B C D <S to T, seq=4, hc=2, lvl=7> <S to T, seq=4, hc=3, lvl=7> <S to T, seq=4, hc=5, lvl=8> E F G 3 3 4 5 6 7 3 7 6 4 5 2 6 8 3 4 7 5 Alternate Path Found Waiting: C Best viewed in slideshow mode.

35. PA-SHORT: Performance • AODV and DSR path optimality suffer as mobility increases. • SHORT algorithm enhances path optimality significantly. • AODV and DSR delivery rate suffer as mobility increases. • SHORT algorithm enhances delivery rate significantly. Gui and Mohaptra

36. PA-SHORT: Route Overhead • SHORT algorithms have less routing overhead and fewer route requests. • Performance gain of AODV-SHORT is greater than that DSR-SHORT in both measures. • Again, SHORT performance gains are most significant in high-mobility scenarios. Gui and Mohaptra

37. EA-SHORT: Performance 5 sourcenodes 5 sourcenodes 5 sinknodes 5 sinknodes • Simulated network field designed to create high-traffic nodes. • The network capacity timeline depicts a scenario with 200 seconds pause time, 10m/s movement and 6 packets per sec. • AODV interruptions begin at 1210 seconds and are data flow is not sustainable when resumed. High Traffic Nodes AODV Failure Zone Gui and Mohaptra

38. Conclusion • SHORT improves routing optimality through: • Continuous monitoring of routing paths • Gradually redirecting toward a more optimal path • SHORT exploits available information about: • Relative local topology • Link quality • Simulations show that SHORT achieves: • Higher delivery rates • Lower routing overhead • Longer network lifetimes

39. Slides prepared by James Bernsen