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© James P.G. SterbenzITTCMobile Wireless Networking
The University of Kansas EECS 882Mobility and Location Management
© 2004–2011 James P.G. Sterbenz03 October 2011
James P.G. Sterbenz
Department of Electrical Engineering & Computer ScienceInformation Technology & Telecommunications Research Center
The University of Kansas
[email protected]
http://www.ittc.ku.edu/~jpgs/courses/mwnets
rev. 11.0
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03 October 2011 KU EECS 882 – Mobile Wireless Nets – Mobility Location MWN-LM-2
© James P.G. SterbenzITTC
Mobile Wireless NetworkingMobility and Location Management
LM.1 Mobility modelsLM.2 Location management
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03 October 2011 KU EECS 882 – Mobile Wireless Nets – Mobility Location MWN-LM-3
© James P.G. SterbenzITTC
Mobile Wireless Networking Cube Model
• Mobility directly affects L1→L3
physicalMAC
link
networktransport
sessionapplication
L1
L7L5L4L3
L2L2–
data plane control plane
plane
management
socialL8
virtual linkL2.5
mobility
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© James P.G. SterbenzITTC
Mobile Wireless NetworkingLM.1 Mobility Models
LM.1 Mobility modelsLM.1.1 Entity mobility modelsLM.1.2 Group mobility models
LM.2 Location management
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© James P.G. SterbenzITTC
Mobility ModelsOverview and Motivation
• Mobility model : model of mobility in a real systemMotivation?
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© James P.G. SterbenzITTC
Mobility ModelsOverview and Motivation
• Mobility model : model of mobility in a real system• Understand the behaviour of deployed systems• Predict the behaviour of proposed systems
– before deployment
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Mobility ModelsClassification
• Degree of abstraction• Number of nodes• Topology• Memory
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© James P.G. SterbenzITTC
Mobility ModelsClassification: Degree of Abstraction
• Trace-driven : constructed from measurements– useful for well-understood pre-existing scenarios
• particularly when location measurements are available
– following the trajectory of a real mobile node• important to understand if representative of general scenario
– may be approximate• time sample• geographic resolution
• Synthetic mobility model• Analytical model
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Mobility ModelsClassification: Degree of Abstraction
• Trace-driven• Synthetic mobility model : abstract model
– intended to approximate the real behaviour of a node– necessary for new scenarios– important to understand applicability to real scenarios
• Analytical model
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© James P.G. SterbenzITTC
Mobility ModelsClassification: Degree of Abstraction
• Trace-driven• Synthetic mobility model• Analytical model
– mathematical model of mobility– typically applied to satellite and spacecraft orbits
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© James P.G. SterbenzITTC
Mobility ModelsClassification: Degree of Abstraction
• Synthetic vs. trace driven choice– dictated by availability of traces– synthetic model may be less computationally intense– trade fidelity of abstraction vs. representativeness of trace
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Mobility ModelsClassification: Number of Nodes
• Entity mobility model : model for a single node– example: individual person walking through a city
• Group mobility model : model for a group of nodes– each of which uses an entity model within the group– example: group of people walking through a city
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© James P.G. SterbenzITTC
Mobility ModelsClassification: Topology
• Unconstrained: arbitrary node movement– within a defined geographic area– e.g. individual walking in a field
• Constrained: node movement patterns constrained– may be constrained to simplify synthetic model
• e.g. Manhattan grid
– may be constrained to represent real topology or geography• e.g. automobile moving within a road system
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© James P.G. SterbenzITTC
Mobility ModelsClassification: Memory
• Memoryless: past history not used in model– current move or trajectory independent of past– e.g. Brownian motion
• Memory: past history used in model– current move or trajectory based on past movement– e.g. vehicle driving on a road
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Mobile Wireless NetworkingLM.1.1 Entity Mobility Models
LM.1 Mobility modelsLM.1.1 Entity mobility modelsLM.1.2 Group mobility models
LM.2 Location management
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03 October 2011 KU EECS 882 – Mobile Wireless Nets – Mobility Location MWN-LM-16
© James P.G. SterbenzITTC
Entity Mobility ModelsExample Scenarios
• Entity mobility model: a single node• Examples of entity movement:
– individual person walking through a city– individual soldier on special operations mission– individual vehicle trajectory– solitary animal movements
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Entity Mobility ModelsExample Models
• Memoryless– random walk– random waypoint
• Memory– Gauss-Markov– Probabilistic random walk
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03 October 2011 KU EECS 882 – Mobile Wireless Nets – Mobility Location MWN-LM-18
© James P.G. SterbenzITTC
Entity Mobility ModelsExample Models: Random Walk
• Random walk: random choice of distance & direction– modelled after Brownian motion [Einstein 1926]
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Entity Models: Random WalkAlgorithm
• Random walk: random choice of distance & direction– choose starting position
(x0 , y0 )
1
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© James P.G. SterbenzITTC
Entity Models: Random WalkAlgorithm
• Random walk: random choice of distance & direction– choose starting position
(x0 , y0 )– choose random direction
θ1 = [0,2π )
2
θi
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© James P.G. SterbenzITTC
Entity Models: Random WalkAlgorithm
• Random walk: random choice of distance & direction– choose starting position
(x0 , y0 )– choose random direction
θ1 = [0,2π )– choose random speed
s1 = [smin, smax]
3
θi si
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• Random walk: random choice of distance & direction– choose starting position
(x0 , y0 )– choose random direction
θ1 = [0,2π )– choose random speed
s1 = [smin, smax]– travel at constant speed s1
for constant time tor distance d
Entity Models: Random WalkAlgorithm
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© James P.G. SterbenzITTC
• Random walk: random choice of distance & direction– choose starting position
(x1 , y1 )– choose random direction
θ2 = [0,2π )– choose random speed
s2 = [smin, smax]– travel at constant speed s2
for constant time tor distance d
– immediately repeat fromcurrent position to i=2
Entity Models: Random WalkAlgorithm
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© James P.G. SterbenzITTC
• Random walk: random choice of distance & direction– choose starting position
(x2 , y2 )– choose random direction
θ3 = [0,2π )– choose random speed
s3 = [smin, smax]– travel at constant speed s3
for constant time tor distance d
– immediately repeat fromcurrent position to i=3
Entity Models: Random WalkAlgorithm
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• Random walk: random choice of distance & direction– choose starting position
(xi , yi )– choose random direction
θi+1 = [0,2π )– choose random speed
si+1 = [smin, smax]– travel at constant speed si+1
for constant time tor distance d
– immediately repeat fromcurrent position i to i+1 …
Entity Models: Random WalkAlgorithm
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Entity Models: Random WalkExample Path
• Random walk: random choice of distance & direction
[Camp Boleng Davis 2002 Fig. 1]
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Advantages?
Entity Models: Random WalkAdvantages
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• Advantages– simple memoryless model
Entity Models: Random WalkAdvantages
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• Advantages– simple memoryless model
• Problems– nodes walk randomly around origin; never stray far
• Pr[moving away from origin] = Pr[moving toward origin]
Disadvantages?
Entity Models: Random WalkAdvantages and Disadvantages
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• Advantages– simple memoryless model
• Problems– nodes walk randomly around orgin; never stay far
• Pr[moving away from origin] = Pr[moving toward origin]
• Disadvantages– most real network-node scenarios not Brownian– rapid, random, disruptive turns in path
Entity Models: Random WalkAdvantages and Disadvantages
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Entity Mobility ModelsExample Models: Random Waypoint
• Random waypoint: move between waypoints[Johnson-Maltz 1996]– one of the most widely used mobility models
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Entity Models: Random WaypointAlgorithm
• Random waypoint: move between waypoints– choose starting position
(x0 , y0 )
1
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Entity Models: Random WaypointAlgorithm
• Random waypoint: move between waypoints– choose starting position
(x0 , y0 )– choose random waypoint
(x1 , y1 )
2
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Entity Models: Random WaypointAlgorithm
• Random waypoint: move between waypoints– choose starting position
(x0 , y0 )– choose random waypoint
(x1 , y1 )– choose random speed
s1 = [smin, smax]
3
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• Random waypoint: move between waypoints– choose starting position
(x0 , y0 )– choose random waypoint
(x1 , y1 )– choose random speed
s1 = [smin, smax]– travel to waypoint
at constant speed s1
Entity Models: Random WaypointAlgorithm
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• Random waypoint: move between waypoints– choose starting position
(x0 , y0 )– choose random waypoint
(x1 , y1 )– choose random speed
s1 = [smin, smax]– travel to waypoint
at constant speed s1– pause for a specified time
p1
Entity Models: Random WaypointAlgorithm
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• Random waypoint: move between waypoints– choose starting position
(x1 , y1 )– choose random waypoint
(x2 , y2 )– choose random speed
s2 = [smin, smax]– travel to waypoint
at constant speed s2– pause for a specified time
p2– repeat from current position
to i=2
Entity Models: Random WaypointAlgorithm
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• Random waypoint: move between waypoints– choose starting position
(x2 , y2 )– choose random waypoint
(x3 , y3 )– choose random speed
s3 = [smin, smax]– travel to waypoint
at constant speed s3– pause for a specified time
p3– repeat from current position
to i=3
Entity Models: Random WaypointAlgorithm
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03 October 2011 KU EECS 882 – Mobile Wireless Nets – Mobility Location MWN-LM-39
© James P.G. SterbenzITTC
• Random waypoint: move between waypoints– choose starting position
(xi , yi )– choose random waypoint
(xi+1 , yi+1 )– choose random speed
si+1 = [smin, smax]– travel to waypoint
at constant speed si+1
– pause for a specified timepi+1
– repeat from current positioni to i+1 …
Entity Models: Random WaypointAlgorithm
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Entity Models: Random WaypointExample Path
• Random waypoint: move between waypoints
[Camp Boleng Davis 2002 Fig. 3]
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Advantages?
Entity Models: Random WaypointAdvantages
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• Advantages– relatively simple memoryless model– more realistic than random walk for many scenarios
• Problem: average node velocity decreases over time– due to steps over long distance with very low speed– takes some time to stabilise– problem reduced by large enough choice of smin
Disadvantages?
Entity Models: Random WaypointAdvantages and Disadvantages
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03 October 2011 KU EECS 882 – Mobile Wireless Nets – Mobility Location MWN-LM-43
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• Advantages– relatively simple memoryless model– more realistic than random walk for many scenarios
• Problem: average node velocity decreases over time• Disadvantages
– still has sharp sudden turns– may not realistically represent real mobility patterns– but may be good enough to model
• mobility effects including need for rerouting and handoffs• disconnectivity when nodes out of range
Entity Models: Random WaypointAdvantages and Disadvantages
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Entity Mobility ModelsExample Models: Gauss-Markov
• Gauss-Markov: gradually vary trajectory over time[Garcia-Luna-Aceves Madrga 1999]
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Entity Models: Gauss-MarkovAlgorithm
• Gauss-Markov: gradually vary trajectory over time– choose starting position (x0 , y0 )– choose mean direction
and initial random direction θ0 = [0,2π )– choose mean speed
and initial random speed s0 = [smin, smax]
1
θi si
s
θ
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© James P.G. SterbenzITTC
• Gauss-Markov: gradually vary trajectory over time– choose starting position (x0 , y0 )– choose mean direction
and initial random direction θ0 = [0,2π )– choose mean speed
and initial random speed s0 = [smin, smax] – travel at constant speed s0
until next timestep tn
Entity Models: Gauss-MarkovAlgorithm
2
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• Gauss-Markov: gradually vary trajectory over time– at each timestep n
– new speed
new direction– tuning parameter 0≤α≤1
α=0: Brownianα=1: linear
– memory from last timestep
Entity Models: Gauss-MarkovAlgorithm
3
1
21 (1 ) (1 )
nn n xs s s sα α α−−= + − + −
1
21 (1 ) (1 )
nn n xθ αθ α θ α θ−−= + − + −
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© James P.G. SterbenzITTC
• Gauss-Markov: gradually vary trajectory over time– at each timestep n
– new speed
new direction– tuning parameter 0≤α≤1
α=0: Brownianα=1: linear
– memory from last timestep
Entity Models: Gauss-MarkovAlgorithm
4
1
21 (1 ) (1 )
nn n xs s s sα α α−−= + − + −
1
21 (1 ) (1 )
nn n xθ αθ α θ α θ−−= + − + −
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© James P.G. SterbenzITTC
• Gauss-Markov: gradually vary trajectory over time– at each timestep n
– new speed
new direction– tuning parameter 0≤α≤1
α=0: Brownianα=1: linear
– memory from last timestep
Entity Models: Gauss-MarkovAlgorithm
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1
21 (1 ) (1 )
nn n xs s s sα α α−−= + − + −
1
21 (1 ) (1 )
nn n xθ αθ α θ α θ−−= + − + −
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Entity Models: Gauss-MarkovExample Path
• Gauss-Markov: gradually vary trajectory over time
[Camp Boleng Davis 2002 Fig. 10]
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Advantages?
Entity Models: Gauss-MarkovAdvantages
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• Advantages– no sudden turns– more realistic than random waypoint for some scenarios
Disadvantages?
Entity Models: Gauss-MarkovAdvantages and Disadvantages
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• Advantages– no sudden turns– more realistic than random waypoint for some scenarios
• Disadvantages– more computationally intensive than random waypoint– more parameters to understand and tune– doesn’t model pausing at waypoints
Entity Models: Gauss-MarkovAdvantages and Disadvantages
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Entity Mobility ModelsExample Models: City Section
• City section: constrained to paths (streets)[Davies 2000]
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Entity Models: City SectionAlgorithm Overview
• Motion constrained to street paths– streets assigned speed limits
• Nodes choose random destination– shortest time computed– nodes maintain safe spacing
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Mobile Wireless NetworkingLM.1.2 Group Mobility Models
LM.1 Mobility modelsLM.1.1 Entity mobility modelsLM.1.2 Group mobility models
LM.2 Location management
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Group Mobility ModelsExample Scenarios
• Group mobility model: a coördinated set of nodes• Examples of group movement:
– group of people walking through a city– military unit– search-and-rescue team– caravan of vehicles trajectory– herd, flock, or school of animals
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Group Mobility ModelsGroup and Entity Sub-Models
• Group mobility model consists of two sub-models– mobility model for group– mobility model for entities within group– may use same or different models for each
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Entity Mobility ModelsExample Models: Column
• Column: nodes follow reference point along a line[Sanchez Manzoni Anejos 2001]
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Entity Mobility ModelsExample Models: Nomadic Community
• Nomadic: nodes move with respect to reference point[Sanchez Manzoni Anejos 2001]– entity model for nodes with respect to reference point– entity model for reference points
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Entity Mobility ModelsExample Models: Pursue
• Pursue: nodes follow reference point[Sanchez Manzoni Anejos 2001]
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Entity Mobility ModelsExample Models: Reference Point
• RPGM (reference point group model):nodes move with respect to center of group[Hong Gerla Pei Chiang 1999]
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Mobile Wireless NetworkingLM.2 Location Management
LM.1 Mobility modelsLM.2 Location management
LM.2.1 Taxonomy and strategiesLM.2.2 Internet and PSTN location management
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Location ManagementIntroduction
• Fixed networks: static nodes– address related to topology (e.g. IP LPM) or– address bound to device (e.g. Ethernet)
• Mobile networks: modes moveproblem?
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Location ManagementIntroduction
• Mobile networks: nodes move– problem:
how to find the destination node for communication?
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Location ManagementIntroduction and Definition
• Mobile networks: nodes move• Location management tracks movements of MNs
– also {location|mobility}{management|tracking}– maintains binding between node identifier and location– note: “address ” is overloaded and can mean either
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Location ManagementIntroduction and Definition
• Mobile networks: nodes move• Location management tracks movements of MNs
– also {location|mobility}{management|tracking}– maintains binding between node identifier and location– note: “address ” is overloaded and can mean either
• Location database maintains these bindings– typically distributed set of agents or registries
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Location ManagementNetworks based on Geographic Location
• Location management consists of– tracking and disseminating geo-coördinates– predicting trajectories in case of high-velocity mobility
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Location ManagementMobile Ad Hoc Networks
• Mobile ad hoc networks (MANETS)– significant mobility– no dependence on infrastructure
• Location management consists of– mapping identifiers to current topological location
or– reassigning identifiers based on topological location
and providing translation mechanism to new address
• Both of these are very hard problems– especially in large geographically distributed networks
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Location ManagementLM.2.1 Taxonomy and Strategies
LM.1 Mobility modelsLM.2 Location management
LM.2.1 Taxonomy and strategiesLM.2.2 Internet and PSTN location management
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Location ManagementAlternative Strategies
• Flooding– proactive vs. reactive
• Quorum / rendezvous– explicit vs. implicit– flat vs. hierarchical
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Location ManagementFlooding
• Flooding– each node floods its location to other nodes
Advantages and disadvantages?
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Location ManagementFlooding: Advantages vs. Disadvantages
• Flooding– each node floods its location to other nodes
• Advantages– simple scheme– guarantees dissemination if connectivity exists
• Disadvantages– overhead
Optimisations?
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Location ManagementFlooding: Optimisations
• Flooding– each node floods its location to other nodes
• Advantages– simple scheme– guarantees dissemination if connectivity exists
• Disadvantages– overhead
• Optimisations– decrease accuracy of dissemination with distance– limit hop count or decreased frequency with hop count
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Location ManagementFlooding: Proactive vs. Reactive
• Proactive flooding– each node periodically floods its location to other nodes
Reactive flooding?
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Location ManagementFlooding: Proactive vs. Reactive
• Proactive flooding– each node periodically floods its location to other nodes
• Reactive flooding– nodes that don’t know location of destination flood query
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Location ManagementQuorum / Rendezvous
• Flooding– proactive vs. reactive
• Quorum / rendezvous– explicit vs. implicit– flat vs. hierarchical
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Location ManagementQuorum / Rendezvous
• Quorum-based location management– quorum of location servers capable of finding all nodes– location server nodes serve location requests– location server nodes serve update requests
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Location ManagementQuorum / Rendezvous
• Quorum-based location management– quorum of location servers capable of finding all nodes– nodes serve location requests– nodes serve update requests
• Rendezvous– rendezvous servers– handle both location and update are rendezvous
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Location ManagementQuorum: Explicit
• Explicit quorum-based location management– explicit set of servers designated as location servers– intersecting set of query quorum and update quorum
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Location ManagementQuorum: Implicit
• Implicit quorum-based location management– implicit set of servers selected by DHT lookup
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Location ManagementQuorum: Flat vs. Hierarchical
• Flat quorum-based location management– all servers serve same role
• Hierarchical quorum-based location management– hierarchy of servers– sub-grid one approach for implicit approach
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Location ManagementLM.2.2 Internet and PSTN
LM.1 Mobility modelsLM.2 Location management
LM.2.1 Taxonomy and strategiesLM.2.2 Internet and PSTN location management
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Location ManagementInternet and Mobile Cellular Telephony
• Networks with limited mobility capabilities– occasional handoffs and roaming
• e.g. mobile cellular telephony and mobile IP
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Location ManagementInternet and Mobile Cellular Telephony
• Networks with limited mobility capabilities– occasional handoffs and roaming
• e.g. mobile cellular telephony and mobile IP
• Mobile nodes– assigned to home network– move to visited network
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Location ManagementInternet and Mobile Cellular Telephony
• Networks with limited mobility capabilities– occasional handoffs and roaming
• e.g. mobile cellular telephony and mobile IP
• Mobile nodes– assigned to home network– move to visited network
• Location management: registries and mappingsInternet(agent)
Mobile PSTN(location registry)
Home Network HA HLR
Visited / Foreign Network FA VLR
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Location ManagementInternet: Mobile IP
• Location management performed by Mobile IP agents– HA (home agent): maintains binding to MN care-of address– FA (foreign agent): assigns care-of addr when MN registers
Lecture WIHome Net Visited Net
CN
IPAP
IPAP
IP IP
IP
IP
MNHA FAMN
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Location ManagementMobile Telephone Network
• Location management performed by PSTN registries– HLR (home location register): binding to VLR of MN – VLR (visitor location register): authentication, service profile
Lecture MT
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Mobility and Location Management Further Reading
• Tracy Camp, Jeff Boleng, and Vanessa Davies,“A Survey of Mobility Models for Ad Hoc Network Research”,Wireless Communications and Mobile Computing,Wiley, vol.2 iss.5, September 2002, pp.483–502
• A. Rahaman, J. Abawajy, and M. Hobbs,“Taxonomy and Survey of Location Management Systems”6th IEEE/ACIS International Conference on Computer and Information Science, July 2007, pp. 369–374
• Tracy Camp,Location Information Services in Mobile Ad Hoc Networks,Colorado School of Mines Technical Report MCS-03-15, October 2003
• Roy Friedman and Gabriel Kliot,Location Services in Ad Hoc and Hybrid Networks: A Survey,Technion Computer Science Technical Report CS-2006-10, April 2006
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Mobility and Location ManagementAcknowledgements
Some material in these foils is based on the textbook• Murthy and Manoj,
Ad Hoc Wireless Networks:Architectures and Protocols
Some material in these foils enhanced from EECS 780 foils