IMPORTANT A framework to systematically analyze the Impact

IMPORTANT: A framework to systematically analyze the "Impact of Mobility on Performance Of Rou. Ting in Ad-hoc Ne. Tworks" Fan Bai*, Narayanan Sadagopan+, Ahmed Helmy* * Department of Electrical Engineering + Department of Computer Science University of Southern California {fbai, helmy}@ceng. usc. edu, narayans@cs. usc. edu Apr 2, 2003 INFOCOM 2003

Outline • • • Motivation and Contributions Mobility Models and Metrics Experiments and Observation Relationship between Mobility and Performance Building Blocks Approach Conclusion and Future work Apr 2, 2003 INFOCOM 2003 2

MANET • Mobile Ad hoc Network (MANET) is a collection of wireless mobile nodes forming a network without using any existing infrastructure • Mobility and traffic are two significant factors affecting protocol performance. In current simulation, – Mobility Pattern: usually, uniformly and randomly chosen destinations (random waypoint model) – Traffic Pattern: usually, uniformly and randomly chosen communicating nodes • Impact of mobility on ad hoc routing protocols is expected to be significant Apr 2, 2003 INFOCOM 2003 3

Motivation • Randomized models (including random waypoint) do not capture – Spatial dependence (correlation) of movement among nodes – Existence of barriers or obstacles constraining mobility • A systematic framework is needed to investigate the impact of various mobility models on the performance of different routing protocols for MANETs • This study attempts to answer – Whether? Especially, to what degree does mobility affect routing protocol performance? – If the answer to 1 is yes, why? – If the answer to 1 is yes, how? Apr 2, 2003 INFOCOM 2003 4

Framework Overview Mobility Models Connectivity Graph Random Waypoint Group Mobility Freeway Mobility Manhattan Mobility Metrics Relative Speed Spatial Dependence Apr 2, 2003 Routing Protocol Performance DSR AODV DSDV Building Block Analysis Connectivity Metrics Performance Metrics Flooding Caching Link Duration Error Detection Error Handling INFOCOM 2003 Error Notification Throughput Overhead 5

Framework Components • Whether? and How much? – Rich set of mobility models that capture characteristics of different type of movement – Protocol independent metrics such as mobility metrics and connectivity graph metrics to capture the above characteristics • Why? – Analysis process to relate performance with a specific characteristic of mobility • How? – Systematic process to study the performance of protocol mechanistic building blocks across various mobility characteristics Apr 2, 2003 INFOCOM 2003 6

Mobility Metrics • Relative Speed (mobility metric I) – The magnitude of relative speed of two nodes, average over all neighborhood pairs and all time • Spatial Dependence (mobility metric II) – The value of extent of similarity of the velocities of two nodes that are not too far apart, average over all neighborhood pairs and all time For example, RWP model, Vmax=30 m/s, RS=12. 6 m/s, Dspatial=0. 03 Apr 2, 2003 INFOCOM 2003 7

Connectivity graph metric • Average link duration (connectivity metric I) – The value of link duration, average over all nodes pairs Performance Metrics • Throughput(performance metric I): delivery ratio • Overhead(performance metric II): number of routing control packets sent Apr 2, 2003 INFOCOM 2003 8

Parameterized Mobility Models • Random Waypoint Model (RWP) – Each node chooses a random destination and moves towards it with a random velocity chosen from [0, Vmax]. After reaching the destination, the node stops for a duration defined by the “pause time” parameter. This procedure is repeated until simulation ends – Parameters: Pause time T, max velocity Vmax • Reference Point Group Model (RPGM) – Each group has a logical center (group leader) that determines the group’s motion behavior – Each nodes within group has a speed and direction that is derived by randomly deviating from that of the group leader – Parameters: Angle Deviation Ratio(ADR) and Speed Deviation Ratio(SDR), number of groups, max velocity Vmax. In our study, ADR=SDR=0. 1 – In our study, we use two scenarios: Single Group (SG) and Multiple Group (MG) Apr 2, 2003 INFOCOM 2003 9

Parameterized Mobility Models • Freeway Model (FW) – Each mobile node is restricted to its lane on the freeway – The velocity of mobile node is temporally dependent on its previous velocity – If two mobile nodes on the same freeway lane are within the Safety Distance (SD), the velocity of the following node cannot exceed the velocity of preceding node – Parameter: Map layout, Vmax Map for FW • Manhattan Model (MH) – Similar to Freeway model, but it allows node to make turns at each corner of street – Parameter: Map layout, Vmax Map for MH Apr 2, 2003 INFOCOM 2003 10

Mobility Models Summary Spatial Dependence Application Geographic Restriction Random Waypoint Model General No No Group Mobility Model Battlefield Yes No Freeway Mobility Model Metropolitan Traffic Yes Manhattan Mobility Model Urban Traffic No Yes Apr 2, 2003 INFOCOM 2003 11

Experiment I: Analysis of mobility characteristics • Simulation done by our mobility generator and analyzer: • • • Number of nodes(N) = 40, Simulation Time(T) = 900 sec Area = 1000 m x 1000 m Vmax set to 1, 5, 10, 20, 30, 40, 50, 60 m/sec across simulations RWP, pause time T=0 SG/MG, ADR=0. 1, SDR=0. 1 FW/MH, map layout in the previous slide Apr 2, 2003 INFOCOM 2003 12

Mobility metrics • Objective: – validate whether proposed mobility models span the mobility space we explore • Relative speed – For same Vmax, MH/FW is higher than RWP, which is higher than SG/MG Relative Speed • Spatial dependence – For SG/MG, strong degree of spatial dependence – For RWP/FW/MH, no obvious spatial dependence is observed Spatial Dependence Apr 2, 2003 INFOCOM 2003 13

Connectivity graph metric • Link duration – For same Vmax, SG/MG is higher than RWP, which is higher than FW, which is higher than MH • Summary – Freeway and Manhattan model exhibits a high relative speed – Spatial Dependence for group mobility is high, while it is low for random waypoint and other models – Link Duration for group mobility is higher than Freeway, Manhattan and random waypoint Apr 2, 2003 INFOCOM 2003 Link duration 14

Experiment II: Protocol Performance across Mobility Models Simulations done in ns-2: • Same set of mobility trace file used in experiment 1 • Traffic pattern consists of source-destination pairs chosen at random • 20 source, 30 connections, CBR traffic • Data rate is 4 packets/sec (low data rate to avoid congestion) • For each mobility trace file, we vary traffic patterns and run the simulation for 3 times Apr 2, 2003 INFOCOM 2003 15

Results and Observations • Performance of routing protocols may vary drastically across mobility patterns – Eg : DSR Throughput Routing Overhead • There is a difference of 40% for throughput and an order of magnitude difference for routing overhead across mobility models! Apr 2, 2003 INFOCOM 2003 16

Which Protocol Has the Highest Throughput ? • We observe that using different mobility models may alter the ranking of protocols in terms of the throughput! Manhattan : AODV or DSR? Random Waypoint : DSR? Apr 2, 2003 INFOCOM 2003 17

Which Protocol Has the Lowest Overhead ? • We observe that using different mobility models may alter the ranking of protocols in terms of the routing overhead! RPGM(single group) : DSR? Manhattan : DSDV? • Recall: Whether mobility impacts protocol performance? • Conclusion: Mobility DOES matter, significantly, in evaluation of protocol performance and in comparison of various protocols! Apr 2, 2003 INFOCOM 2003 18

Putting the Pieces Together • Recall: If mobility affects protocol performance, why? • We observe a very clear trend between mobility metric, connectivity and performance – With similar average spatial dependency • Relative Speed increases Link Duration decreases Routing Overhead increases and throughput decreases – With similar average relative speed • Spatial Dependence increase Link Duration increases Throughput increases and routing overhead decreases • Conclusion: Mobility Metrics influence Connectivity Metrics which in turn influence protocol performance metrics ! Apr 2, 2003 INFOCOM 2003 19

Putting the Pieces Together Throughput Relative Velocity Link Duration Spatial Dependence Apr 2, 2003 Overhead INFOCOM 2003 20

Mechanistic Building Blocks • Recall: How mobility affects the protocol performance? • Idea: – The protocol is decomposed into its constituent mechanistic, parameterized building block, each building block is to implement a well-defined functionality – Various protocols choose different parameter settings for the same building block. For a specific mobility scenario, the building block with different parameters behaves differently, which in turns affect the overall performance of the protocol • We are interested in the contribution of building blocks to the overall performance in the face of mobility • Case study: – Reactive protocols like DSR and AODV Apr 2, 2003 INFOCOM 2003 21

Building Block Diagram for reactive protocols Route Setup Flooding Range of Flooding Caching Add Route Cache Num of Entry Caching Style Expiration Timer Route Reply Localized/Non-localized method Route Invalidate Route Maintenance Error Detection Error Handling Notify Detection Method Apr 2, 2003 Error Notification Notify Handling Mode INFOCOM 2003 Recipient 22

Examples • Caching – DSR uses aggressive caching, AODV does not – Evaluation: Ratio of number of route replies from cache to total number of route reply aggressive caching is useful ? How about cache validity? AODV DSR • Error Handling – DSR uses localized salvaging, it only happens 2%~8% across various mobility model salvaging barely has an effect ! Apr 2, 2003 INFOCOM 2003 23

Conclusions • Defined protocol independent metrics to capture a few mobility characteristics of interest and proposed a rich set of mobility models • Evaluated protocols over mobility models that span the above mobility characteristics • Performance trends and comparison results vary widely with the choice of mobility • Establish the logical relationship between mobility and protocol performance • Propose a method to analyze the interplay between building block and mobility • Mobility patterns are IMPORTANT Apr 2, 2003 INFOCOM 2003 24

Future Work • Investigate more protocol independent metrics. e. g. , path duration[1] • Establish the general framework to evaluate the design choice based on building block methodology[2] • Investigate the effect of other parameters. e. g. , node density • Investigate other mobility models and other routing protocols, e. g. ZRP, GPSR & expansion model • Integrate the mobility tool with ns-2 [3] [1] N. Sadagopan, F. Bai, B. Krishnamachari, A. Helmy, “PATHS: analysis of PATH duration Statistics and their impact on reactive MANET routing protocols” Mobi. Hoc 2003. [2] F. Bai, N. Sadagopan, A. Helmy, “BRICS: A Building-block approach for analyzing Rout. Ing proto. Cols in ad hoc network. S- a case study of reactive routing protocols”, USC-CS-TR-02 -775, in submission. [3] http: //www-scf. usc. edu/~fbai/mobility. html Apr 2, 2003 INFOCOM 2003 25

Thanks! Apr 2, 2003 INFOCOM 2003 26

Related Work • Random Waypoint based evaluation – Mobility model: only Random Waypoint model – [1] concluded that reactive protocols like DSR and AODV would perform better than proactive protocols such as DSDV under high mobility rate, while DSDV would perform quite well under low mobility rate – [2] observed that DSR would outperform AODV in less demanding situations, but AODV would outperform DSR at heavy traffic and high mobility scenario – Consistent with our observations [1]J. Broch, D. A. Maltz, D. B. Johnson et al, “A performance comparison of multi-hop wireless ad hoc network routing protocols”, MOBICOM 1998. [2]S. R. Das, C. E. Perkins, E. M. Royer, “Performance Comparison of two on-demand routing protocols for ad hoc network”, INFOCOM 2000. Apr 2, 2003 INFOCOM 2003 27

Related Work • Scenario based evaluation – [3] proposed models for ‘realistic’ scenarios like conference, disaster relief and event coverage – Conclusion about reactive and proactive protocol is similar to [1] – [4] introduced the Reference Point Group Model(RPGM), it is observed that AODV, DSDV and HSR would perform worse with random waypoint model than with RPGM – [5] proposed a generic mobility framework, Mobility Vector Model, from which all ‘realistic’ mobility patterns like MPGM can be derived [3] P. Johansson, T. Larsson, N. Hedman et al, “Scenario-based performance analysis of routing protocols for mobile ad-hoc network”, MOBICOM 1999. [4] X. Hong, M. Gerla et al, “A group mobility model for ad hoc wireless network”, ACM/IEEE MSWi. M 1999. [5] X. Hong, T. Kwon, M. Gerla et al, “A mobility framework for ad hoc wireless networks”, ACM MDM 2001. Apr 2, 2003 INFOCOM 2003 28

Link Duration • Re-run the single group mobility model for three times Apr 2, 2003 INFOCOM 2003 29

Linear Correlation between Average Path Duration and Protocol Performance • The reciprocal of average path duration is analytically shown to have a linear relationship with the throughput and overhead • For DSR – Pearson Correlation between 1/PD and throughput is – 0. 9165, -0. 9597 and – 0. 9132 for RW, FW and MH, respectively – Pearson Correlation between 1/PD and overhead is 0. 9753, 0. 9812 and 0. 9978 for RW, FW and MH, respectively • Relationship between LD and PD? [1] N. Sadagopan, F. Bai, B. Krishnamachari, A. Helmy, “PATHS: analysis of PATH duration Statistics and their impact on reactive MANET routing protocols” Mobi. Hoc 2003. Apr 2, 2003 INFOCOM 2003 30