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

Networks UC Davis Jian Li, Prasant Mohapatra 2 / 22 Agenda Introduction Our Proposal: PANDA PANDA Algorithms Performance Evaluation Conclusion

Networks UC Davis Jian Li, Prasant Mohapatra 3 / 22 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 ... Intro (1): MANET

Networks UC Davis Jian Li, Prasant Mohapatra 4 / 22 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

Networks UC Davis Jian Li, Prasant Mohapatra 5 / 22 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 S D Route Request Route Reply Broadcast Storm Problem: for example, node C may receive broadcast messages from nodes A and B almost simultaneously, which results in a collision. C A B Proposed Solution: each node applies a random delay before rebroadcasting a message or responding to a broadcast message.

Networks UC Davis Jian Li, Prasant Mohapatra 6 / 22 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 S I J K M L D 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. It happens that: Node I rebroadcast earlier than Node J.

Networks UC Davis Jian Li, Prasant Mohapatra 7 / 22 Agenda Introduction Our Proposal: PANDA PANDA Algorithms Performance Evaluation Conclusion

Networks UC Davis Jian Li, Prasant Mohapatra 8 / 22 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

Networks UC Davis Jian Li, Prasant Mohapatra 9 / 22 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

Networks UC Davis Jian Li, Prasant Mohapatra 10 / 22 Agenda Introduction Our Proposal: PANDA PANDA Algorithms Performance Evaluation Conclusion

Networks UC Davis Jian Li, Prasant Mohapatra 11 / 22 PANDA Algorithms Different Variants  PANDA-LO (Location Only)  PANDA-LV (Location & Velocity)  PANDA-TP (Transmission Power)

Networks UC Davis Jian Li, Prasant Mohapatra 12 / 22 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.

Networks UC Davis Jian Li, Prasant Mohapatra 13 / 22 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) Note: L1 > L2 > L3 and T1 > T2 > T3. This figure shows how to estimate the link lifetime.

Networks UC Davis Jian Li, Prasant Mohapatra 14 / 22 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:

Networks UC Davis Jian Li, Prasant Mohapatra 15 / 22 Similar to PANDA-LO, only consider link distance Attempt to make a big jump at each hop PANDA-TP (2) 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. smaller

Networks UC Davis Jian Li, Prasant Mohapatra 16 / 22 Agenda Introduction Our Proposal: PANDA PANDA Algorithms Performance Evaluation Conclusion

Networks UC Davis Jian Li, Prasant Mohapatra 17 / 22 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

Networks UC Davis Jian Li, Prasant Mohapatra 18 / 22 Simulation Results (1) Path optimality ratio = length of actual path / optimal path

Networks UC Davis Jian Li, Prasant Mohapatra 19 / 22 Simulation Results (2) End to end delay

Networks UC Davis Jian Li, Prasant Mohapatra 20 / 22 Simulation Results (3) Energy conserving route discovery

Networks UC Davis Jian Li, Prasant Mohapatra 21 / 22 Agenda Introduction Our Proposal: PANDA PANDA Algorithms Performance Evaluation Conclusion

Networks UC Davis Jian Li, Prasant Mohapatra 22 / 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