CSE 620
Advanced Networks Concepts
Location Management in Mobile Networks
Ramesh Ravipati
Veeraiah P. Gorantla
Department of Computer Science and Engineering
State University of New York at Buffalo
Contents
•
Introduction
•
Location Management Techniques
•
Location Prediction Schemes
- Traveling Demand Model
- Mobility Prediction through Neuro-Fuzzy Theory
Introduction
Problem:
How do we keep track of the location of the Mobile Host (MH)s in a mobile
network?
Why ?
To provide better communication services in a mobile environment and
provide better performance for such networks.
We try to keep track of the Mobile Hosts through location management
techniques. A number of techniques are being used for this purpose. This is
to locate the user in a mobile network rapidly so as to improve the
performance of the communications network.
Location Management
• Attempt to keep track of the mobile hosts in real time, as the hosts
move through the network.
• The location records have to be updated frequently so as to keep the
location management in real-time.
Location Prediction
• Attempt to predict the location of the mobile host at a future instant so
as to provide services to the user without break or interruption.
• Good mobility models have to be used for accurate predictions.
Location Management Techniques
• Updating
- Time-based updating
- Distance-based updating
- Movement-based updating
• Paging
- Random updating
- Sequential updating
• Caching location records (centralized)
• Distributed location strategies
Advantages
• Simple to implement
• Intuitively easy to understand
Disadvantages
• Limited resources of bandwidth, power etc.
• Brings down the performance of communications network
due to constant updates to the location records
Location Prediction Schemes
• We attempt to predict the future location of a mobile host or user
• The future location of a host can be predicted from the present location
based on given specified conditions
• To predict the future location of a user, a mobility model has to be
utilized
• This mobility model simulates the movement of the hosts through the
network
• The mobility model to be selected has to be realistic enough to model
the behavior of the hosts
Mobility Models
• Random Walk
- The direction and velocity of the hosts are random variables
• Fluid Flow
- The users can move in a direction uniformly distributed between
[0,2Π] with a given average velocity
• Rule based Models
- The movement of the users is defined by a given specified set of
rules
In this presentation, we present two location prediction
techniques in mobile environments:
• User Mobility Prediction by Traveling Demand Model
- Uses a rule-based mobility model (traveling demand model)
• Mobility Prediction Scheme based on Neuro-Fuzzy Theory
- Uses neuro-fuzzy theory to create a mobility model
User Mobility Prediction by Traveling Demand Model
• The key point : Users have regular mobility patterns in their motion
behavior
• Users can move through several cells in their mobility patterns
• Hence user mobility should not be parameterized independently for each
cell
• The characteristics of the mobility behavior have to be determined over
the entire network (or a relevant part of the network)
Traveling Demand Model
In this model, the individual hosts make choices or decisions
pertaining to the question of their mobility :
1.
Given the present geographical location i, a time period t, and an
activity A, the host first decides whether to travel or not
2.
Given the choice made at the first level of decision, the host then
chooses a location j for the conduct of the given activity
3.
Given the outcomes of the first two decisions, the host then decides the
transportation mode m to use, among the various alternative modes
available between i and j
4.
Given the outcomes of all the preceding decisions, the traveler chooses
a route r among those available for the trip
Traveler Decision Process Structure
Formulating the Model
The event that mobile users move from one location to another follows
the exponential distribution given as
ƒ(t) = (1/D’) • exp(-t/D’)
where D’ is the mean duration time at an arbitrary location
The mean duration time at an arbitrary location can be given by
D(ρ) = - D’ • log(1 - ρ)
where ρ Є [0,1] is the probability that mobile users stay in an arbitrary
location and is selected using a uniform distribution
Given the Φ destinations by which mobile users pass, the following
parameter is required to obtain a mean duration time at i th destination
( i Є{1,2,3,….., Φ)
Di is the mean duration time at i th destination
In a real mobile environment, however,an equivalent mean duration time
is not observed
To approximate real-world behaviors sufficiently closely, every
destination should have a different mean duration time.
We now consider the traveling mode which users decide between two
destinations
• Mobility modeling should include changes in both the movement
velocity and direction of mobile users
A movement direction, θij, between two destination,Di and Dj , is calculated
as
θij = θ’ + (2П/m) • k
where θ’ is the angle for the major direction
m is the number of movement directions in [0,2П]
k Є {1,2,3,…..,m-1}
Let us consider the movement velocity of mobile users
Mobile users have the following velocity between two destinations,
Di and Dj
vij = vij’ + v
where vij is the actual velocity of the mobile user
vij’ is the mean velocity between the two locations
and v is the variation in the mean velocity
Pij = probability vector for the movement between two locations Di and Dj
Rij = route probability vector for choosing among k routes between
Di and Dj
Example of Movement Direction
Simulation
Simulation Factors
region size
Sx x Sy
cell radius
Cr
initial location
D0
number of destinations
Φ
mean duration time at each destination
D i’
number of directions
m
Simulation Factors (contd…)
direction probability vector between two destinations
mean velocity and velocity variation between two destinations
Pij
vij’ , v
number of routes
k
route probability vector
Rij
Simulation Environment
We assume 5 locations D0 to D4 in the network
Sx = 900 , Sy = 850
Cr = 1.0 km
Hence we get a cell network approximately 25 x 28
Mean velocity of the mobile user = 10 km/h
We assume
Unit of time is hour
Unit of velocity is km/h
Trajectory of Mobile Users for Various Change of Velocity
Trajectory of Mobile Users for m = 6
Trajectory of Mobile Users for m = 12
Simulation Results
Features of the Traveling Demand Model
• We can see from the simulation that mobile users follow a
regular pattern
• By these patterns, if the present position of the user is
known, we can find the future location at a given instant
• Such location prediction strategies improve the performance
of the communication network by decreasing call-blocking
probability and call-dropping
Mobility Prediction Scheme based on Neuro-Fuzzy Theory
• The key point : Use of neuro-fuzzy theory to estimate the future position of
a mobile user
• The system autonomously learns about the motion behavior of the user
• Locations are obtained by approximate reasoning of the neuro-fuzzy
inference system
• High accuracy for prediction of various user patterns
• Also can be used with considerable accuracy to users whose history has
not been learned by the system
Movement paths of mobile users
Components of a mobility prediction scheme
1.
Pre-processing component
- user mobility data generation and classification
2.
The Inference system
- mobility learning and neuro-fuzzy inference
3.
Mobility Prediction component
- location prediction analysis
Pre-processing
Inference System
Location Prediction
As in the previous model, we consider the following factors
•
Given the present geographical location i, a time period t, and an
activity A, the host first decides whether to travel or not
•
Given the choice made at the first level of decision, the host then
chooses a location j for the conduct of the given activity
•
Given the outcomes of the first two decisions, the host then decides
the transportation mode m to use, among the various alternative
modes available between i and j
•
Given the outcomes of all the preceding decisions, the traveler
chooses a route r among those available for the trip
Once we have a history of movements of the users, we can then use
the inference system to predict the future locations of the users
The Inference System consists of 4 parts
1.
Neuro-fuzzy Model
- simplified fuzzy inference model
2. Fuzzy Rulebase
- set of rules in which mobility history of users is represented as
linguistic values
3.
Mobility data Clustering
- grouping of data into clusters for easy access and retrieval
4.
Mobility Learning
- procedures for learning mobility patterns using neural networks
Motivation for Neuro-fuzzy theory
• Existing methods are based on direct observation in the current mobile
situation
• Such methods have poor prediction performance, especially if the
current location of the user is previously unknown
• This approach to mobility prediction attempts to derive a future location
based on the knowledge of previously known mobile information
• The intelligent prediction scheme promises a more appropriate approach
Control flow for simulation of the user mobility prediction scheme
Neuro-fuzzy logic
• Fuzzy logic provides an inference morphology that enables
approximate human reasoning capabilities to be applied to knowledgebased systems
• Fuzzy systems are suitable for uncertain or approximate reasoning,
especially for the system with a mathematical model that is difficult to
derive
• Fuzzy logic allows decision making with estimated values under
incomplete or uncertain information
• Fuzzy Logic is a departure from classical two-valued sets and logic,
that uses "soft" linguistic (e.g. large, hot, tall) system variables and a
continuous range of truth values in the interval [0,1], rather than strict
binary (True or False) decisions and assignments
Mobility Prediction Scheme
Expresses user mobility in terms of direction and velocity of the mobile
user
• It predicts a future location based on the movement factors of past and
current locations of a mobile user
Let Skt be the state k at time t of the movement of the user
Skt is defined as Skt = (vkt,θkt)
such that k < K and t Є T
where K is the number of states
T is the set of time
vkt is the velocity of the user at time t in state k
θkt is the direction of the user at time t in state k
Using the movement states defined above, the movement function
which maps current and past movement states into a future
movement state can be
where NFS is the neuro-fuzzy inference system
h is the number of current and past movement states
The current and past movement states represent location history, by
which movement patterns can be determined.
• To construct a fuzzy rulebase, we use cluster-based techniques to
automatically generate fuzzy rules according to the degree of similarity
of movement patterns
• Similar states get grouped into clusters
• Each cluster has a cluster center which is used as the reference value
• The similarity(distance) between the present state of a user and a
cluster is given as
Di = | Skt – Ri |
where Ri is the center of the cluster i.
• We define a value r such that if Di <= r the similarity is high,
otherwise it is low
• Each cluster is associated with an element in the fuzzy rulebase
• Based on the above criteria we build our fuzzy rulebase
• But unnecessary fuzzy rules should be eliminated from the
rulebase according to an appropriate mobile situation
• It is necessary for a criterion to determine the elimination of an
unnecessary fuzzy rule from the fuzzy rulebase.
where agei is the age of the i th fuzzy rule
α is the parameter which controls the age of the fuzzy rule
β is the parameter determining the elimination of the fuzzy rule
The age of a fuzzy rule is updated as follows:
• If the distance between a current movement state vector and a
cluster center is less than the radius, the age of a fuzzy rule
becomes zero.
• On the other hand, if the distance is larger than the radius, the
age of a fuzzy rule is increased by one.
• According to the value of β fuzzy rules are subdivided into two
groups: young fuzzy rules and old fuzzy rules
• Since the young rules suitably reflect the latest movement state, it
is desirable that they be used as fuzzy rules
•
The old rules should be eliminated from the rulebase
Learning Model
Learning Model (cont…)
This process is to be run iteratively from Step 2 to Step 5
This process will enable the learning model to recognize the movement
patterns in the location history of the mobile users by clustering the movement
states
Simulation Environment
Parameters
• The number of movement data (days)
• The number of current and past movement states (h)
• The change period of movement states (ζ)
• Radius (r)
• Learning rate (η)
• The number of learning iterations (m)
• The control parameter for the age of the fuzzy rule (α)
• The remove parameter for the unnecessary fuzzy rules (β)
Conclusions
• In this presentation, we discussed the mobility prediction schemes based
on two criteria
• The main issue addressed is the prediction of user mobility in
order to improve QoS for mobile applications
• It was motivated from the fact that the location history, plays an
important role in user mobility
• The predicted location information by the scheme could be used
for supporting the pre-connection of services and the pre-allocation
of resources
• This provides an efficient solution for the service delay problem
that occurs in a hand-off.
• Mobility prediction on existing mobile networks will play a role as a
coordinator for smooth communication
References
[1] Joon-Min Gil, Chan Yeol Park, Youn-Hee Han, Chong-Sun Hwang “User Mobility
Simulation by Traveling Demand Model in Mobile Environments”, Nov 2000
[2] Joon-Min Gil “Simulation of a Mobility Prediction Scheme Based on Neuro-Fuzzy
Theory in Mobile Computing”, July 2000
[3] John Scourias,Thomas Kunz “An activity-based mobility model and location management
simulation framework”, Oct 1999
[4] Kuochen Wang, Jung Huey “A cost effective distributed location management strategy for
wireless networks”, Sep 1999
[5] Sanjoy K. Sen, Amiya Bhattacharya, Sajal K. Das “A selective location update strategy for
PCS users”, Jun 1999
[6] Atif A. Siddiqi, Thomas Kunz “The Peril of Evaluating Location Management Proposals
through Simulations”, May 1999
[7] Guang Wan, Eric Lin “Cost reduction in location management using semi-realtime
movement information”, Feb 1998
[8] Derek Lam, Yingwei Cui, Donald C. Cox, Jennifer Widom “A Location Management
Technique To Support Lifelong Numbering in Personal Communications Services”, Oct 1997