A Novel Mechanism for Flooding Based Route Discovery in Ad hoc Networks

1. A Novel Mechanism for Flooding Based Route Discovery in Ad hoc Networks Jian Li and Prasant Mohapatra Networks Lab, UC Davis

2. Agenda • Introduction • Our Proposal: PANDA • PANDA Algorithms • Performance Evaluation • Conclusion

3. Intro (1): MANET • What is a Mobile Ad hoc Network (MANET)? • A set of wireless devices that can move around freely • Form a dynamic topology via ad hoc wireless links • No fixed infrastructure, no central administration • Limited bandwidth, battery, etc • MANET applications • Military tactical communication • Disaster relief • Conferencing • ...

4. Intro (2): Routing in MANET • Why routing is different in MANETs? • Unpredictable moving pattern • Dynamic link availability, limited bandwidth • Fast changing topology • Other concerns: battery source, security issues, etc • Proposed routing protocols • Proactive v.s. Reactive (On-Demand) • Topology-based v.s. Position-based

5. Intro (3): Flooding based Routing • Flooding technique is often used by on demand protocols, such as Dynamic Source Routing(DSR) and Ad hoc On-demand Distance Vector(AODV) routing Route Request C D A Route Reply S B Broadcast Storm Problem: for example, node C may receive broadcast messages from nodes A and B almost simultaneously, which results in a collision. Proposed Solution: each node applies a random delay before rebroadcasting a message or responding to a broadcast message.

6. Intro(4): Random Rebroadcast Delay • Random Rebroadcast Delay (RRD) approach can solve the problem of “broadcast storm” effectively • But, the “Randomness” also introduces a new problem: “Next-hop Racing” behavior At nodes L and M, node I “wins” over node J, even if J is better than I in term of link lifetime Suppose: Node I moves much faster than Node J, so the link S-I will be broken sooner than the link S-J. K I L S D It happens that: Node I rebroadcast earlier than Node J. J M

7. Agenda • Introduction • Our Proposal: PANDA • PANDA Algorithms • Performance Evaluation • Conclusion

8. Our Proposal: PANDA (1) • PANDA • Positional Attributes based Next-hop Determination Approach • Basic idea of PANDA • Classify neighboring nodes into different classes, each of which uses a different delay range such that better candidates go first • “Good” or “Bad” candidates • Utilize geographical location, velocity, energy, etc., to determine the rebroadcast delay

9. Our Proposal: PANDA (2) • Greedy approach • selecting better link at each hop hopefully leads to better end-to-end routes • Fully distributed • an intermediate node makes local decision without any communications with its neighboring nodes • Versatile capabilities • e.g., search for a route with smallest number of hops, or with minimal transmission power consumption, etc

10. Agenda • Introduction • Our Proposal: PANDA • PANDA Algorithms • Performance Evaluation • Conclusion

11. PANDA Algorithms • Different Variants • PANDA-LO (Location Only) • PANDA-LV (Location & Velocity) • PANDA-TP (Transmission Power)

12. PANDA-LO (Location Only) • Determine rebroadcast delay according to link distance • Attempt to make a big jump at each hop At node A If |SA| > L1 delay = t1 + uniform(0, t1) else if |SA| > L2 delay = 2*t1 + uniform(0, t1) else if |SA| > L3 delay = 3*t1 + uniform(0, t1) else delay = 4*t1 + uniform(0, t1) Note: L1 > L2 > L3 and T1 > T2 > T3.

13. PANDA-LV (Location & Velocity) • Determine rebroadcast delay according to link distance and lifetime • Attempt to select more stable link with a BIG jump At node A If |SA| > L1 && Lifetime > T1 delay = t1 + uniform(0, t1) else if |SA| > L2 && Lifetime > T2 delay = 2*t1 + uniform(0, t1) else if |SA| > L3 && Lifetime > T3 delay = 3*t1 + uniform(0, t1) else delay = 4*t1 + uniform(0, t1) This figure shows how to estimate the link lifetime. Note: L1 > L2 > L3 and T1 > T2 > T3.

14. PANDA-TP (Transmission Power) • Motivation: multiple small hops can save transmission power over a big single hop Assuming the path loss is a simple function of the transmission distance: The path energy ratio:

15. PANDA-TP (2) • Similar to PANDA-LO, only consider link distance • Attempt to make a big jump at each hop smaller At node A If |SA| < L3 delay = t1 + uniform(0, t1) else if |SA| < L2 delay = 2*t1 + uniform(0, t1) else if |SA| < L1 delay = 3*t1 + uniform(0, t1) else delay = 4*t1 + uniform(0, t1) Note: L1 > L2 > L3 and T1 > T2 > T3.

16. Agenda • Introduction • Our Proposal: PANDA • PANDA Algorithms • Performance Evaluation • Conclusion

17. Simulation Setup • NS-2 simulator • simulation area 1500m x 300m • 250m transmission range • 100 nodes • 30 connections • speed (0, 20) m/sec • “random waypoint” mobility model • pause time: 0, 30, 60, 150, 300, and 500 sec • simulation time 500 sec

18. Simulation Results (1) • Path optimality ratio = length of actual path / optimal path

19. Simulation Results (2) • End to end delay

20. Simulation Results (3) • Energy conserving route discovery

21. Agenda • Introduction • Our Proposal: PANDA • PANDA Algorithms • Performance Evaluation • Conclusion

22. Conclusion • PANDA approach outperforms RRD approach • Both PANDA-LO and PANDA-LV can improve path optimality • PANDA-LV can improve end-to-end delay • PANDA-TP can discover routes with much less power consumption than RRD approach