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Practical Mobility Models & Mobility Based Routing. Joy Ghosh LANDER cse@buffalo. Outline. Impact of mobility on protocol performance Pros & Cons of Random Waypoint model Entity, Group & Scenario based models Our proposed ORBIT mobility framework Our proposed Orbit Based Routing schemes

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outline
Outline
  • Impact of mobility on protocol performance
  • Pros & Cons of Random Waypoint model
  • Entity, Group & Scenario based models
  • Our proposed ORBIT mobility framework
  • Our proposed Orbit Based Routing schemes
  • Future direction
  • Conclusion
impact of mobility on protocol performance
Impact of mobility on protocol performance
  • F. Bai, N. Sadagopan, and A. Helmy, “Important: a framework to systematically analyze the impact of mobility on performance of routing protocols for adhoc networks”, Proceedings of IEEE INFOCOM '03, vol. 2, pp. 825-835, March 2003.
random waypoint mobility model
Random Waypoint mobility model
  • Parameters
    • Pause time = p
    • Max velocity =vmax
    • Min velocity = vmin
  • Description
    • Pick a random point within terrain
    • Select a velocity vi such that vmin≤ vi≤vmax
    • Move linearly with velocity vi towards the chosen point
    • On reaching the destination, pause for specified time p
    • Repeat the steps above for entire simulation
random waypoint mobility model1
Random Waypoint mobility model
  • Pros
    • Simple to implement
    • Easy theoretical analysis
  • Cons
    • Highly impractical in real world networks
    • Average speed decay problem
      • Long journeys at low speeds
      • Solution – use non-zero min speed!
examples of entity based mobility
Examples of entity based mobility
  • Random Walk Mobility Model (including its many derivatives)
    • A simple mobility model based on random directions and speeds.
  • Random Waypoint Mobility Model
    • A model that includes pause times between changes in destination and speed.
  • Random Direction Mobility Model
    • A model that forces MNs to travel to the edge of the simulation area before changing direction and speed.
  • A Boundless Simulation Area Mobility Model
    • A model that converts a 2D rectangular simulation area into a torus-shaped simulation area.
  • Gauss-Markov Mobility Model
    • A model that uses one tuning parameter to vary the degree of randomness in the mobility pattern.
  • A Probabilistic Version of the Random Walk Mobility Model
    • A model that utilizes a set of probabilities to determine the next MN position.
  • City Section Mobility Model
    • A simulation area that represents streets within a city.
examples of group based mobility
Examples of group based mobility
  • Exponential Correlated Random Mobility Model
    • A group mobility model that uses a motion function to create movements.
  • Column Mobility Model
    • A group mobility model where the set of MNs form a line and are uniformly moving forward in a particular direction.
  • Nomadic Community Mobility Model
    • A group mobility model where a set of MNs move together from one location to another.
  • Pursue Mobility Model
    • A group mobility model where a set of MNs follow a given target.
  • Reference Point Group Mobility Model
    • A group mobility model where group movements are based upon the path traveled by a logical center.
examples of scenario based mobility
Examples of scenario based mobility
  • Manhattan model
  • Freeway model
  • City Area, Area Zone, Street Unit
  • METMOD, NATMOD, INTMOD
outline1
Outline
  • Impact of mobility on protocol performance
  • Pros & Cons of Random Waypoint model
  • Entity, Group & Scenario based models
  • Our proposed ORBIT mobility framework
  • Our proposed Orbit Based Routing schemes
  • Future direction
  • Conclusion
sociological orbits
Sociological Orbits

City 1: Home Town

City 2: Relatives

Home

Porch

Y

A

R

d

Kitchen

Outdoors

Mall / Plaza

Restaurant

City 3: Friends

Work

Cubicle

Rest

room

Cafeteria

Level 2 Orbit Path

Level 0 Orbit Area

Level 1 Orbit Path

Level 3 Orbit Path

outline2
Outline
  • Impact of mobility on protocol performance
  • Pros & Cons of Random Waypoint model
  • Entity, Group & Scenario based models
  • Our proposed ORBIT mobility framework
  • Our proposed Orbit Based Routing schemes
  • Future direction
  • Conclusion
orbit based routing basics
Orbit Based Routing - Basics
  • Each node is assumed to know their own coordinates and the coordinates of the Hubs in the terrain
  • Get acquainted with neighbors
  • Share (own)/ Cache (other’s) Hub list information
  • Build a distributed database of Hub lists
  • Query acquaintances, and acquaintances of acquaintances, and so on for unknown MNs
orbit based routing basics1
Orbit Based Routing - Basics
  • The traversal from one node to its acquaintance is referred to as a “logical hop”
  • Each logical hop may be comprised of multiple physical hops determined by greedy geographic forwarding
information query response
Information Query & Response
  • No Hub list information exists for destination
    • A subset of acquaintances is chosen (as explained later) and a query packet is sent to the Hub list of each of these acquaintances (as also explained later)
    • If an acquaintance has no information, it can forward the query packet to a subset of its own acquaintances – unless the logical hop of the packet has exceeded a specified threshold
    • Intermediate nodes can respond if appropriate
subset of acquaintances to query
Subset of acquaintances to query
  • Problem
    • Lots of acquaintances  lot of query overhead
  • Solution
    • Query a subset such that all the Hubs that a node learns of from its acquaintances are covered
  • Let H1, H2, …, Hn be the Hub lists of acquaintances A1, A2, …, An
  • Let H = {H1, H2, …, Hn} // collection of all sets of Hubs
  • Let C be the collection of all Hubs known through sets in H
  • Hence, C = U {H1, H2, …, Hn}
  • Objective is to find a minimum subset
  • This is a minimum set cover problem – NP Complete
  • We use the Quine-McCluskey optimization technique
quine mccluskey optimization
Quine-McCluskey optimization
  • Node A with Hub list Hj is a Prime acquaintance iff:
  • Let P be the set of all Prime acquaintances
  • Prime acquaintance A with Hub list Hj will be an Essential Prime acquaintance iff:
  • Example: A = {1,2}, B = {2,3,4}, C = {1,3}
    • A is a Prime acquaintance
    • B is an Essential Prime acquaintance
  • Choose all the Essential Prime acquaintances first
  • If any Hub is still uncovered, iteratively choose non-essential Prime acquaintances that cover the max number of remaining Hubs, till all Hubs are covered
packet transmission to hub lists
Packet Transmission to Hub lists
  • Key concept of OBR
    • Associate node location information with Hub lists
    • Send all types of packets to a node by transmitting to its Hub list
    • Several possible ways  different OBR Schemes
obr scheme 1 sequential
OBR Scheme 1 - Sequential
  • The packet is forwarded to the first Hub in the list that is closest to the Hub of the source
  • There on, the packet is forwarded sequentially to all the Hubs in the list
  • In case of a local maxima, the next nearest unvisited Hub is chosen
  • Failed Hubs may get multiple chances of being chosen
obr scheme 2 simulcast
OBR Scheme 2 - Simulcast
  • Multiple copies of the same packet are sent (by greedy geographic forwarding) to each of the Hubs in the list
  • Failed Hubs don’t get a 2nd chance
obr scheme 3 multicast
OBR Scheme 3 - Multicast
  • Create a Minimum Spanning Tree with the Hubs in the list
  • Multicast the packet down the MST
  • Failed Hubs “may” get a 2nd chance
  • Single Hub failure “may” cause multiple Hubs to miss the packet
obr connection maintenance
OBR – connection maintenance
  • In every data packet, source puts its current Hub information
  • While session is active, if destination changes Hub, it updates the source
  • Such data and update packets use the current Hub information to reduce delay
acquaintance based soft location management absolom
Acquaintance Based Soft Location Management (ABSoLoM)
  • Our prior work  OBR is conceptually same
  • In ABSoLoM, nodes make limited acquaintances and kept track of their exact coordinates via regular updates
  • The logical hops for a query were limited too
  • We had obtained high throughput with very low control overhead
performance analysis parameters
Performance Analysis Parameters
  • Simulations in GloMoSim
  • 100 nodes in 1000 m x 1000 m for 1000 sec
  • Radio range of 250 m
  • 150 random CBR connections
  • Each connection sends 10 packets (512 b)
  • LAO Speed (min, max) = 1 m/s, 10 m/s
  • MAO Speed (min, max) = 10 m/s, 30 m/s
results variation in hub size
Results - Variation in Hub Size

* fixed radio range & larger hub  less coverage within Hub

* fixed terrain size & larger hub  less space outside Hubs  more overlaps amongst Hubs

results variation in lao timeout
Results – Variation in LAO Timeout

* lower LAO timeout  higher avg. node velocity in MAO

* higher LAO timeout  higher avg. node population in Hubs

results variation in number of hub
Results – Variation in Number of Hub

* larger number of Hubs  longer Hub lists  increased Hub overlaps

outline3
Outline
  • Impact of mobility on protocol performance
  • Pros & Cons of Random Waypoint model
  • Entity, Group & Scenario based models
  • Our proposed ORBIT mobility framework
  • Our proposed Orbit Based Routing schemes
  • Future direction
  • Conclusion
future direction
Future direction
  • Micro level mobility aided routing
    • Mobility prediction
  • Delay Tolerant Networks
    • Packet traversal may involve both packet transmission and carrying the packet physically
    • Actually makes use of mobility in a practical way
  • Space communications
    • InterPlaNetary Internet
conclusion
Conclusion
  • Random Waypoint - of theoretical interest
  • Several mobility models – ORBIT provides a generic framework
  • OBR – first direct attempt to route based on mobility information
  • Combining packet transmission with node mobility may prove useful for DTNs
  • Applications in Space communications
references mostly for the figures
References (mostly for the figures)
  • F. Bai, N. Sadagopan, and A. Helmy, “Important: a framework to systematically analyze the impact of mobility on performance of routing protocols for adhoc networks”, Proceedings of IEEE INFOCOM '03, vol. 2, pp. 825-835, March 2003.
  • T. Camp, J. Boleng, and V. Davies, “A Survey of Mobility Models for Ad Hoc Network Research”, Wireless Communications and Mobile Computing (WCMC): Special issue on Mobile Ad Hoc Networking: Research, Trends and Applications, vol. 2, no. 5, pp. 483-502, 2002.
  • J. Ghosh, S. J. Philip, and C. Qiao, “Acquaintance Based Soft Location Management (ABSLM) in MANET”, Proceedings of IEEE Wireless Communications and Networking Conference (WCNC) '04, March 2004.
  • J. Ghosh, S. J. Philip, and C. Qiao, “ORBIT Mobility Framework and Orbit Based Routing (OBR) Protocol for MANET”, CSE Dept. TR # 2004-08, State University of New York at Buffalo, 2004 (July)
  • I.F. Akyildiz, O.B. Akan, C. Chen, J. Fang, W. Su, “InterPlaNetary Internet: state-of-the-art and research challenges” – Elsevier Computer Networks Journal (to appear)
  • S. Jain, K. Fall, R. Patra, “Routing in a Delay Tolerant Network” – Proceedings of ACM SIGCOMM ’04, August, 2004