Mobile Wireless Networking Mobility and Location Management

Transcription

1 Mobile Wireless Networking The University of Kansas EECS 882 Mobility and James P.G. Sterbenz Department of Electrical Engineering & Computer Science Information Technology & Telecommunications Research Center The University of Kansas 03 October 2011 rev James P.G. Sterbenz Mobile Wireless Networking Mobility and LM.1 Mobility models LM.2 Location management 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-2 1

2 L8 L7 L5 L4 L3 L2.5 L2 L2 L1 Mobile Wireless Networking data plane Cube Model management control plane social application session transport network virtual link link MAC physical Mobility models affect L1 L3 in ns-3 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-3 m ob i l i t y p l a n e Mobile Wireless Networking LM.1 Mobility Models LM.1 Mobility models LM.1.1 Entity mobility models LM.1.2 Group mobility models LM.2 Location management 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-4 2

3 Mobility Models Overview and Motivation Mobility model : model of mobility in a real system Motivation? 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-5 Mobility Models Overview 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 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-6 3

4 Mobility Models Classification Degree of abstraction Number of nodes Topology Memory 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-7 Mobility Models Classification: 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 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-8 4

5 Mobility Models Classification: 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 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-9 Mobility Models Classification: Degree of Abstraction Trace-driven Synthetic mobility model Analytical model mathematical model of mobility typically applied to satellite and spacecraft orbits 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-10 5

6 Mobility Models Classification: 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 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-11 Mobility Models Classification: 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 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-12 6

7 Mobility Models Classification: 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 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-13 Mobility Models Classification: 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 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-14 7

8 Mobile Wireless Networking LM.1.1 Entity Mobility Models LM.1 Mobility models LM.1.1 Entity mobility models LM.1.2 Group mobility models LM.2 Location management 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-15 Entity Mobility Models Example 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 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-16 8

9 Entity Mobility Models Example Models Memoryless random walk random waypoint Memory Gauss-Markov Probabilistic random walk 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-17 Entity Mobility Models Example Models: Random Walk Random walk: random choice of distance & direction modelled after Brownian motion [Einstein 1926] 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-18 9

10 Entity Models: Random Walk Algorithm Random walk: random choice of distance & direction choose starting position (x 0, y 0 ) 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-19 1 Entity Models: Random Walk Algorithm Random walk: random choice of distance & direction choose starting position (x 0, y 0 ) choose random direction θ 1 = [0,2π ) θ i 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM

11 Entity Models: Random Walk Algorithm Random walk: random choice of distance & direction choose starting position (x 0, y 0 ) choose random direction θ 1 = [0,2π ) choose random speed s 1 = [s min, s max ] θ i si 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-21 3 Entity Models: Random Walk Algorithm Random walk: random choice of distance & direction choose starting position (x 0, y 0 ) choose random direction θ 1 = [0,2π ) choose random speed s 1 = [s min, s max ] travel at constant speed s 1 for constant time t or distance d 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM

12 Entity Models: Random Walk Algorithm Random walk: random choice of distance & direction choose starting position (x 1, y 1 ) choose random direction θ 2 = [0,2π ) choose random speed s 2 = [s min, s max ] travel at constant speed s 2 for constant time t or distance d immediately repeat from current position to i=2 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-23 5 Entity Models: Random Walk Algorithm Random walk: random choice of distance & direction choose starting position (x 2, y 2 ) choose random direction θ 3 = [0,2π ) choose random speed s 3 = [s min, s max ] travel at constant speed s 3 for constant time t or distance d immediately repeat from current position to i=3 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM

13 Entity Models: Random Walk Algorithm Random walk: random choice of distance & direction choose starting position (x i, y i ) choose random direction θ i+1 = [0,2π ) choose random speed s i+1 = [s min, s max ] travel at constant speed s i+1 for constant time t or distance d immediately repeat from current position i to i+1 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-25 7 Entity Models: Random Walk Example Path Random walk: random choice of distance & direction [Camp Boleng Davis 2002 Fig. 1] 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-26 13

14 Entity Models: Random Walk Advantages Advantages? 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-27 Entity Models: Random Walk Advantages Advantages simple memoryless model 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-28 14

15 Entity Models: Random Walk Advantages and Disadvantages Advantages simple memoryless model Problems nodes walk randomly around orgin; never stay far Pr[moving away from origin] = Pr[moving toward origin] Disadvantages? 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-29 Entity Models: Random Walk Advantages and Disadvantages Advantages simple memoryless model Problems nodes walk randomly around origin; never stray far Pr[moving away from origin] = Pr[moving toward origin] Disadvantages most real network-node scenarios not Brownian rapid, random, disruptive turns in path 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-30 15

16 Entity Mobility Models Example Models: Random Waypoint Random waypoint: move between waypoints [Johnson-Maltz 1996] one of the most widely used mobility models 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-31 Entity Models: Random Waypoint Algorithm Random waypoint: move between waypoints choose starting position (x 0, y 0 ) 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM

17 Entity Models: Random Waypoint Algorithm Random waypoint: move between waypoints choose starting position (x 0, y 0 ) choose random waypoint (x 1, y 1 ) 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-33 2 Entity Models: Random Waypoint Algorithm Random waypoint: move between waypoints choose starting position (x 0, y 0 ) choose random waypoint (x 1, y 1 ) choose random speed s 1 = [s min, s max ] 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM

18 Entity Models: Random Waypoint Algorithm Random waypoint: move between waypoints choose starting position (x 0, y 0 ) choose random waypoint (x 1, y 1 ) choose random speed s 1 = [s min, s max ] travel to waypoint at constant speed s 1 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-35 4 Entity Models: Random Waypoint Algorithm Random waypoint: move between waypoints choose starting position (x 0, y 0 ) choose random waypoint (x 1, y 1 ) choose random speed s 1 = [s min, s max ] travel to waypoint at constant speed s 1 pause for a specified time p 1 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM

19 Random waypoint: move between waypoints choose starting position (x 1, y 1 ) choose random waypoint (x 2, y 2 ) choose random speed s 2 = [s min, s max ] travel to waypoint at constant speed s 1 pause for a specified time p 2 repeat from current position to i=2 Entity Models: Random Waypoint Algorithm 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-37 6 Entity Models: Random Waypoint Algorithm Random waypoint: move between waypoints choose starting position (x 0, y 0 ) choose random waypoint (x 3, y 3 ) choose random speed s 3 = [s min, s max ] travel to waypoint at constant speed s 3 pause for a specified time p 3 repeat from current position to i=3 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM

20 Entity Models: Random Waypoint Algorithm Random waypoint: move between waypoints choose starting position (x i, y i ) choose random waypoint (x i+1, y i+1 ) choose random speed s i+1 = [s min, s max ] travel to waypoint at constant speed s i+1 pause for a specified time p i+1 repeat from current position i to i+1 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-39 8 Entity Models: Random Waypoint Example Path Random waypoint: move between waypoints [Camp Boleng Davis 2002 Fig. 3] 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-40 20

21 Entity Models: Random Waypoint Advantages Advantages? 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-41 Entity Models: Random Waypoint Advantages and Disadvantages 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 s min Disadvantages? 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-42 21

22 Entity Models: Random Waypoint Advantages and Disadvantages 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 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-43 Entity Mobility Models Example Models: Gauss-Markov Gauss-Markov: gradually vary trajectory over time [Garcia-Luna-Aceves Madrga 1999] 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-44 22

23 Entity Models: Gauss-Markov Algorithm Gauss-Markov: gradually vary trajectory over time choose starting position (x 0, y 0 ) choose mean direction θ and initial random direction θ 0 = [0,2π ) choose mean speed s and initial random speed s 0 = [s min, s max ] θ i si 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-45 1 Entity Models: Gauss-Markov Algorithm Gauss-Markov: gradually vary trajectory over time choose starting position (x 0, y 0 ) choose mean direction and initial random direction θ 0 = [0,2π ) choose mean speed and initial random speed s 0 = [s min, s max ] travel at constant speed s 0 until next timestep t n 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM

24 Entity Models: Gauss-Markov Algorithm Gauss-Markov: gradually vary trajectory over time at each timestep n 2 new speed sn = αsn 1 + (1 α) s + (1 α ) sx n 1 2 new direction θn = αθn 1 + (1 α) θ + (1 α ) θx n 1 tuning parameter 0 α 1 α=0: Brownian α=1: linear memory from last timestep 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-47 3 Entity Models: Gauss-Markov Algorithm Gauss-Markov: gradually vary trajectory over time at each timestep n 2 new speed sn = αsn 1 + (1 α) s + (1 α ) sx n 1 2 new direction θn = αθn 1 + (1 α) θ + (1 α ) θx n 1 tuning parameter 0 α 1 α=0: Brownian α=1: linear memory from last timestep 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM

25 Entity Models: Gauss-Markov Algorithm Gauss-Markov: gradually vary trajectory over time at each timestep n 2 new speed sn = αsn 1 + (1 α) s + (1 α ) sx n 1 2 new direction θn = αθn 1 + (1 α) θ + (1 α ) θx n 1 tuning parameter 0 α 1 α=0: Brownian α=1: linear memory from last timestep 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-49 5 Entity Models: Gauss-Markov Example Path Gauss-Markov: gradually vary trajectory over time [Camp Boleng Davis 2002 Fig. 10] 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-50 25

26 Entity Models: Gauss-Markov Advantages Advantages? 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-51 Entity Models: Gauss-Markov Advantages and Disadvantages Advantages no sudden turns more realistic than random waypoint for some scenarios Disadvantages? 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-52 26

27 Entity Models: Gauss-Markov Advantages and Disadvantages 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 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-53 Entity Mobility Models Example Models: City Section City section: constrained to paths (streets) [Davies 2000] 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-54 27

28 Entity Models: City Section Algorithm Overview Motion constrained to street paths streets assigned speed limits Nodes choose random destination shortest time computed nodes maintain safe spacing 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-55 Mobile Wireless Networking LM.1.2 Group Mobility Models LM.1 Mobility models LM.1.1 Entity mobility models LM.1.2 Group mobility models LM.2 Location management 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-56 28

29 Group Mobility Models Example 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 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-57 Group Mobility Models Group 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 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-58 29

30 Entity Mobility Models Example Models: Column Column: nodes follow reference point along a line [Sanchez Manzoni Anejos 2001] 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-59 Entity Mobility Models Example 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 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-60 30

31 Entity Mobility Models Example Models: Pursue Pursue: nodes follow reference point [Sanchez Manzoni Anejos 2001] 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-61 Entity Mobility Models Example Models: Reference Point RPGM (reference point group model): nodes move with respect to center of group [Hong Gerla Pei Chiang 1999] 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-62 31

32 Mobile Wireless Networking LM.2 LM.1 Mobility models LM.2 Location management LM.2.1 Taxonomy and strategies LM.2.2 Internet and PSTN location management 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-63 Introduction Fixed networks: static nodes address related to topology (e.g. IP LPM) or address bound to device (e.g. Ethernet) Mobile networks: modes move problem? 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-64 32

33 Introduction Mobile networks: nodes move problem: how to find the destination node for communication? 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-65 Introduction 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 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-66 33

34 Introduction 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 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-67 Networks based on Geographic Location Location management consists of tracking and disseminating geo-coördinates predicting trajectories in case of high-velocity mobility 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-68 34

35 Mobile 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 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-69 LM.2.1 Taxonomy and Strategies LM.1 Mobility models LM.2 Location management LM.2.1 Taxonomy and strategies LM.2.2 Internet and PSTN location management 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-70 35

36 Alternative Strategies Flooding proactive vs. reactive Quorum / rendezvous explicit vs. implicit flat vs. hierarchical 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-71 Flooding Flooding each node floods its location to other nodes Advantages and disadvantages? 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-72 36

37 Flooding: Advantages vs. Disadvantages Flooding each node floods its location to other nodes Advantages simple scheme guarantees dissemination if connectivity exists Disadvantages overhead Optimisations? 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-73 Flooding: 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 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-74 37

38 Flooding: Proactive vs. Reactive Proactive flooding each node periodically floods its location to other nodes Reactive flooding? 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-75 Flooding: 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 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-76 38

39 Quorum / Rendezvous Flooding proactive vs. reactive Quorum / rendezvous explicit vs. implicit flat vs. hierarchical 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-77 Quorum / 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 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-78 39

40 Quorum / 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 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-79 Quorum: Explicit Explicit quorum-based location management explicit set of servers designated as location servers intersecting set of query quorum and update quorum 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-80 40

41 Quorum: Implicit Implicit quorum-based location management implicit set of servers selected by DHT lookup 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-81 Quorum: 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 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-82 41

42 LM.2.2 Internet and PSTN LM.1 Mobility models LM.2 Location management LM.2.1 Taxonomy and strategies LM.2.2 Internet and PSTN location management 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-83 Internet and Mobile Cellular Telephony Networks with limited mobility capabilities occasional handoffs and roaming e.g. mobile cellular telephony and mobile IP 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-84 42

43 Internet 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 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-85 Internet 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 mappings Home Network Visited / Foreign Network Internet (agent) HA FA Mobile PSTN (location registry) HLR VLR 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-86 43

44 Internet: 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 WI Home Net Visited Net MN HA AP IP IP IP IP IP AP FA MN IP CN 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-87 Mobile 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 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-88 44

45 Mobility and 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 A. Rahaman, J. Abawajy, and M. Hobbs, Taxonomy and Survey of Systems 6th IEEE/ACIS International Conference on Computer and Information Science, July 2007, pp 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 , April October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-89 Mobility and Acknowledgements 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 03 October 2011 KU EECS 882 Mobile Wireless Nets Mobility Location MWN-LM-90 45