practical mobility models & mobility based routing joy ghosh lander cse@buffalo
TRANSCRIPT
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Practical Mobility Models & Mobility Based Routing
Joy Ghosh
LANDER
cse@buffalo
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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
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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.
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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
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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!
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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.
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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.
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Examples of scenario based mobility Manhattan model Freeway model City Area, Area Zone,
Street Unit METMOD, NATMOD,
INTMOD
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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
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City 2: Relatives
City 1: Home Town
Sociological Orbits
Home
Work
Outdoors
Porch
Kitchen
YARd
Cafeteria
Cubicle
Restroom
Mall / Plaza
Restaurant
City 3: Friends
Level 0 Orbit Area
Level 1 Orbit Path
Level 2 Orbit Path
Level 3 Orbit Path
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ORBIT mobility framework
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Simplified ORBIT
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Our example models – RWP & RW
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Our example models – Rand
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Our example models – Uni & Restr
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Our example models - Ovly
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Analysis – Mobility metrics
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Analysis – Connectivity graph metrics
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Variation in Hub size w.r.t. DSR &
LAR
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Results – Variation in LAO Timeout
* lower LAO timeout higher avg. node velocity in MAO
* higher LAO timeout higher avg. node population in Hubs
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Variation in LAO Timeout w.r.t. DSR &
LAR
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Results – Variation in Number of Hub
* larger number of Hubs longer Hub lists increased Hub overlaps
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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
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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
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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
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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
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Random Walk (fixed time)
Next Return
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Random Walk (fixed distance)
ReturnBack
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Random Waypoint
Return
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Random Direction
Return
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Boundless simulation area
ReturnNext
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Boundless simulation area mobility
ReturnBack
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Gauss-Markov (α: randomness factor)
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Probabilistic Random Walk
Return
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City Section
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Column mobility
Return
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Nomadic Community
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Pursue mobility
Return
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Reference Point Group Mobility (RPGM)
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