Fine grained mobility characterization steady and transient state behaviors
Download
1 / 37

Fine-Grained Mobility Characterization: Steady and Transient State Behaviors - PowerPoint PPT Presentation


  • 100 Views
  • Uploaded on

Fine-Grained Mobility Characterization: Steady and Transient State Behaviors. Wei Gao and Guohong Cao Dept. of Computer Science and Engineering Pennsylvania State University. Outline. Introduction Node mobility formulation Characterizing node mobility behaviors Performance evaluation

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Fine-Grained Mobility Characterization: Steady and Transient State Behaviors' - zaza


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Fine grained mobility characterization steady and transient state behaviors

Fine-Grained Mobility Characterization: Steady and Transient State Behaviors

Wei Gao and Guohong Cao

Dept. of Computer Science and Engineering

Pennsylvania State University


Outline
Outline State Behaviors

  • Introduction

  • Node mobility formulation

  • Characterizing node mobility behaviors

  • Performance evaluation

  • Summary & future work


Mobility characterization
Mobility Characterization State Behaviors

  • Node mobility pattern

    • Needs to be characterized from node mobility observations

    • Predict node mobility in the future


Mobility characterization1
Mobility Characterization State Behaviors

  • Improve the performance of mobile computing

    • Forecast disconnection among mobile nodes

    • Avoid unreliable links for routing

    • Actively pre-fetch data before network partition


Coarse grained mobility characterization
Coarse-Grained Mobility Characterization State Behaviors

  • Mobility observation: association to wireless Access Points (APs)

  • Mobility pattern: transitions among APs

  • Rough prediction on node movement in the future

Characterized node mobility

Node movement


Our focus
Our Focus State Behaviors

  • Fine-grained mobility characterization

    • Mobility observation: geographical node movement

    • Accurate mobility prediction

Characterized node mobility


Major contributions
Major Contributions State Behaviors

  • Formulate node mobility at a fine-grained level based on Hidden Markov Model (HMM)

  • Mobility characterization based on the HMM formulation

    • Mobility prediction at both steady-state and transient-state time scales

    • Temporal and spatial mobility inter-dependency


Hidden markov model
Hidden Markov Model State Behaviors

  • Discrete state space

  • State transition probability matrix

  • Initial state distribution

  • Observation probability distributions

    • Each state is “hidden” behind an observation PDF

    • For a state sequence , a HMM has an occurrence probability for each observation sequence


Why hmm
Why HMM? State Behaviors

  • Discrete state space in a Markov process

    • Explicit correspondence to coarse-grained mobility observations

      • Each state corresponds to an AP

    • No explicit correspondence to fine-grained mobility observations

      • Node moves continuously

  • Solution: bridge the gap through the observation PDFs in HMM


Outline1
Outline State Behaviors

  • Introduction

  • Node mobility formulation

  • Characterizing node mobility behaviors

  • Performance evaluation

  • Summary & future work


Mobility observation
Mobility Observation State Behaviors

  • Each node periodically observes its own mobility

    • Each node is able to continuously locate itself

    • Hand-held GPS devices or triangulation localization

  • Mobility observation: velocity vector

    • Including both the moving speed and direction

Observation period

Node locations


Mobility stage
Mobility Stage State Behaviors

  • Each stage corresponds to a range of the direction of node velocity vectors

    • A sector-shaped area

  • Uniform initialization

    • i-th stage:

    • : average of the first few

      mobility observations


Mobility stage1
Mobility Stage State Behaviors

  • Association of mobility stages to HMM states

    • Assume observation probability distribution as Gaussian

    • Set the mean vector to observation PDF

  • Mobility stage allocation is adjusted based on mobility observations

    • HMM parameter re-estimation


Hmm parameter re estimation
HMM Parameter Re-estimation State Behaviors

  • HMM parameters are iteratively re-estimated based on recent mobility observations to capture the up-to-date mobility pattern

  • Expectation-Maximization (EM) algorithm

    • For a set of mobility observations , re-estimation for the HMM is to maximize

    • Parameters to be re-estimated:

    • Computational complexity:

  • Being affected by various empirical parameters

Covariance matrix of observation PDF

Mean vector of observation PDF

Initial state probability

State transition probability


Weighted mobility observations
Weighted Mobility Observations State Behaviors

  • Mobility observations in a training set should not be considered as equal

    • Mobility observations in past may be different from the current node mobility

    • More recent mobility observations should have larger weights during parameter re-estimation


Weighted mobility observations1
Weighted Mobility Observations State Behaviors

  • For a training set , the weight of is proportional to t, and controlled by a constant factor and a smoothing factor as

P=0.3

P=0.5

P=0.7

P=0.9


Outline2
Outline State Behaviors

  • Introduction

  • Node mobility formulation

  • Characterizing node mobility behaviors

  • Performance evaluation

  • Summary & future work


Mobility prediction
Mobility Prediction State Behaviors

  • Steady-state and transient-state time scales

    • Human mobility exhibits zig-zag movement pattern

    • Transient-state moving directions may vary

    • The cumulative moving direction remains unchanged


Mobility prediction1
Mobility Prediction State Behaviors

  • Steady-state prediction

    • The average direction over all the mobility stages

  • Transient-state prediction

    • For the recent mobility observations , find the best state sequence which maximizes

    • The distribution of the next mobility observation

Stationary distribution of the HMM


Node mobility inter dependency
Node Mobility Inter-Dependency State Behaviors

  • Temporal Mobility Dependency (TMD)

    • Current node mobility depends on the past history

  • Spatial Mobility Dependency (SMD)

    • The movement of a node relates to others

  • Important in many mobile applications


Temporal mobility dependency tmd
Temporal Mobility Dependency (TMD) State Behaviors

  • The TMD of node j at time t with HMM defined as

    • : Kullback-Leibler distance measure between HMMs

    • Discrete approximation:

      • For the k-th mobility observation period


Spatial mobility dependency smd
Spatial Mobility Dependency (SMD) State Behaviors

  • The SMD between two nodes i and j is defined as

  • The SMD among a set S of nodes is defined as


Outline3
Outline State Behaviors

  • Introduction

  • Node mobility formulation

  • Characterizing node mobility behaviors

  • Performance evaluation

  • Summary & future work


Trace based evaluation
Trace-based Evaluation State Behaviors

  • NCSU human mobility trace

    • Records the movement trajectory of human beings during a long period of time


Accuracy of steady state mobility prediction
Accuracy of Steady-State Mobility Prediction State Behaviors

  • Comparisons:

    • Auto-Regressive (AR) process

    • Order-2 Markov prediction

linear regression

coarse-grained

50%

70%


Simulations
Simulations State Behaviors

  • Performance evaluation in large-scale networks

    • 50 mobile nodes in a area

  • Various mobility models

    • Random Way Point (RWP)

    • Gauss-Markov (GM)

      • Temporal correlation of node mobility is controlled by

    • Reference Point Group Mobility (RPGM)

      • Spatial correlation of node mobility is controlled by the average number (n) of nodes per group


Accuracy of transient state mobility prediction
Accuracy of Transient-State Mobility Prediction State Behaviors

  • Prediction error is lower than 20% for node mobility with less randomness


Mobility inter dependency
Mobility Inter-Dependency State Behaviors

  • The temporal and spatial mobility dependencies can be accurately characterized


Summary
Summary State Behaviors

  • HMM-based mobility formulation to bridge the gap between discrete Markov states and continuous mobility observations

  • Fine-grained mobility characterization

    • Steady-state and transient-state mobility prediction

    • Temporal and spatial mobility inter-dependency

  • Future work

    • Extension to multi-hop neighbors of mobile nodes

    • Correlation with existing mobility models?


  • Thank you
    Thank you! State Behaviors

    http://mcn.cse.psu.edu

    • The paper and slides are also available at:

      http://www.cse.psu.edu/~wxg139


    Hmm parameter re estimation1
    HMM Parameter Re-estimation State Behaviors

    • Parameters to be re-estimated:

    Back


    Impact of empirical parameters
    Impact of Empirical Parameters State Behaviors

    • T: period of mobility observation

      • Inversely proportional to the average node moving speed

    • L: size of training set of mobility observations

      • Larger L increases the accuracy of parameter re-estimation

      • May not capture the up-to-date mobility pattern

    • N: number of states in the HMM

      • Possible overfitting if N is too large

      • Regularization methods

    Back


    The value of p
    The Value of State BehaviorsP

    • P is adaptively adjusted according to the current node moving velocity

    • To ensure that ,

      • where , and Vmaxis the maximum node

        speed in past

    Back


    Accuracy of mobility prediction
    Accuracy of Mobility Prediction State Behaviors

    • Mainly depends on the randomness of node mobility

      • Transient-state prediction is sensitive to the frequent change of node moving direction

      • Steady-state prediction is more reliable

    • Error of node localization

      • System error

        • Eliminated when velocity vector is used as mobility observation

      • Random error

        • HMM parameters are re-estimated in an accumulative manner over multiple mobility observations

    Back


    Kl distance measure between hmms
    KL Distance Measure between HMMs State Behaviors

    • KL distance between two probabilistic distributions and

    • KL distance between two HMMs and

    Stationary distribution

    Back


    Application of mobility inter dependency
    Application of Mobility Inter-Dependency State Behaviors

    • Being used as network decision metrics

      • Mobility-aware routing: build routes between nodes with higher SMD

      • Data forwarding in DTNs: a current relay which has high TMD is also a good relay choice in the future


    Application of mobility inter dependency1
    Application of Mobility Inter-Dependency State Behaviors

    • Mobility-aware clustering

      • Nodes with higher SMD with its neighbors are better choices for clusterhead

    Back


    ad