UDelModels

Vinay Sridhara(Department of Electrical and

Computer Engineering)

University of Delaware

What are we looking at ?

Impact of Environment on wireless Ad Hoc networks What are UDelModels Existing mobility models Why do we need another mobility model Existing propagation models Why do we need to use a different propagation model Are the results of simulations significantly different Summary of current work Future work Conclusions

Content Outline

UDelModels

UDelModels are a set of tools for MANET simulations.

They aim at providing a close to real approximation to the environments for wireless ad hoc networks.

Two main components of UDelModels Mobility model Propagation model

Existing Mobility Models

Entity Mobility Models

Group Mobility Models

Random Walk

City Section

Random Waypoint

Random Direction

Boundless simulation area

Gauss-Markov

Probabilistic version of Random walk

Exponentially Correlated Random waypoint

Column Model

Nomadic Model

Pursue Model

Reference point Group Mobility

Memoryless

Past speed and direction

Urban Environments

Random Walk Model The Node Chooses a Random direction from [0, 2π] and a random speed from [maxSpeed, minSpeed]and moves along that direction. The Nodes can either move for a constant distance or for time. The Node repeats the above steps till the end of simulation.

Random Waypoint Model Node is initialized at a Random Location

The node after waiting for an initial pause time, picks a random destination and starts moving towards the destination at uniformly distributed random speed

Upon reaching the destination the node pauses for a Uniformly distributed random time.

Simulation Area

Gauss-Markov ModelThe speed and direction of the nth step depends

on the (n-1)st step

Sn = alpha* Sn-1 + (1-alpha)*Smean+sqrt(1-alpha2)Sxn-1

dn = alpha* dn-1 + (1-alpha)*dmean+sqrt(1-alpha2)dxn-1

alpha – tuning parameter

Smean and dmean are mean values of speed and direction

Sxn-1 and dxn-1are Random Variables from Gaussian distribution

When alpha = 0 we get totally random node movement

When alpha = 1 we get a perfectly linear node movement

The main motivation for this new mobility model is the UDel Propagation Model UDel Mobility Model is a graph based constrained random waypoint mobility model

There are three main components to this model

1. Graphical description of the environment

2. Mobility

3. Visualization tool

UDel Mobility Model

Graphical Description The graphical description describes the environment in which the MANET will be simulated

This provides an environment with in which the Mobile nodes will be operating

BuildingsSidewalks

Roads

Environment

Buildings and Offices The Buildings are composed of office locations and the hallways

A simple and homogeneous model of the building is employed

From a graphical perspective hallways are made up of series of vertices

office vertices hallwayvertices

end of hallway vertex - adjacent to floors above and below

sidewalk-to-building-connector

office vertex converted into hallway vertex

sidewalk-tosidewalk-

connectors

two buildings make up a building complex

Buildings and Offices Contd.

Buildings and Offices Contd. All the office locations are considered to be of uniform width and length

More than one buildings can be grouped together to form a complex

Sidewalks The pedestrian mobile nodes have to move on the sidewalks

It is essential that all the buildings in the topology are reachable from each other via the sidewalks

The point where the sidewalk connects to a building is made the door of the building

The sidewalks can be defined by the user or it can be automatically generated by the program

Automatically generating the sidewalks might result in an unrealistic number of sidewalks

Mobility Three kinds of mobile nodes are defined

Multiple mobility models are utilized

Mobile Nodes

Pedestrian

Vehicular

Airborne

Graph Based Random walk

Graph Based Random waypoint

Simple Freeway Model

Random Waypoint

People

Cars & other vehicles

UAVs, Helicopters etc

Pedestrian Mobile Nodes Mobile Nodes are randomly and uniformly initialized in the office locations

The Mobile node after a random pause time picks a random office location and starts moving towards it at a random speed

Problem!

Finding the shortest path between all the office locations is computationally intensive

Pedestrian Mobile Nodes Contd.

Two tier architecture is employed

1. First Tier: Consists of buildings connected by the sidewalks

2. Second Tier: Consists of each office location connected to the doorway through the hallway

First the shortest path between all the buildings is computed

Secondly the shortest path from an office location to the doorway is computed for each building

Random Waypoint for Pedestrian mobile nodes

Each node has its own itinerary (An itinerary consists of the past and the present information )

This aspect of UDel Mobility model makes it not Memoryless

This also introduces a lot avenues for realistic modeling of the movement of nodes

Random & Constrained Why do we call it graph based constrained random waypoint mobility model ?

The Nodes pick random office location

This makes this mobility model random

These nodes move along the defined sidewalks (graphically edges connecting vertices)

This aspect makes the node movements constrained to the sidewalks (outside the buildings) or the hallways (inside the buildings)

Parameters Pause Time Distributions

- Pareto Distribution

- Exponential Distribution

- Uniform Distribution

Speed of Mobility

The speed for Mobility is random number picked uniformly from [maxSpeed, minSpeed]

Random Walk for Pedestrian Mobile Nodes Random walk mobility for UDel model is similar to the existing graph based random walk mobility model

Specifically when a node reaches a vertex it picks the next vertex from the list of neighboring vertices.

Two significant differences

- The node pauses are restricted to only office locations

- The mobility model is not entirely Memoryless

- When a node enters a hallway it does not pick all the vertices with equal probability

Realistic Mobility Itinerary information is used

A weight based probability scheme is employed

Each vertex is assigned a probability based on the past information in the itinerary

e.g. if a node has been in the office for last 10 steps then Ui = 0.0002 for the office vertices and Ui = 2 for the sidewalk to building connector

The probability of choosing a vertex is hence

NUMSTEPSii 1.84uw

jN

1j

i

wΣ

wyprobabilit

Vehicular and Airborne Nodes Mobility of the Vehicular nodes is constrained to the roads

- The nodes pick random roads and and travel at a random speed picked uniformly between [maxSpeed, minSpeed]

- When the nodes come to the end of that road they pause with the time distribution given by pareto, exponential or uniform

The Airborne nodes move in a constrained 3D space (cube) above the city

- They hover over the cities with specified number of stops before reaching the starting point

Mobility Model

Mobility Model Working

UDMapBuilderGraphical utility for building the cities

Propagation Models Good propagation models contributes as one of the most important factor towards realistic modeling of MANETs

There has been little investigation into the effects of physical layer

Even though much work on the physical layer modeling has been carried out for cellular networks, it is not relevant for MANETs.

- Difference in reception and transmission power

- Effects of second order propagation

Free Space ModelFree Space propagation model is one of the most basic models

Pr = (Pt)*C/d2

Pt is the transmitted power

Pr is the received power

C is a constant depending on the transmission criteria like wavelength

d is the distance between transmitter and receiver

Fast FadingThe fading that occurs due to small movements in the source or receiver.

Pr = cos(2ft)

two rays delayed by L

Pr = acos(2ft) + bcos(2f(t-L))

if L = 1/2f then Pr = (a-b)cos(2ft)

if L = 1/f then Pr = (a+b)cos(2ft)

Rayleigh and Riciean Models are used to model this effect

Fast Fading Contd. It is important to note that the value of L is very sensitive to carrier frequency

- Hence the problem is more alleviated in the wide band channels

Many techniques like equalization are used to utilize the signals that are not canceling out

Shadow Fading

The shadow fading also called slow fading is the effect of the shadows that are cast by the objects like buildings, mountains etc

- The signal strength decreases because of the presence of a large object between the transmitter and the receiver

- This is usually modeled as a log-normal distribution

- The effects of shadowing are deterministic and hence it is very important to have a deterministic model for this kind of fading mechanism

Ground ReflectionsOccurs when there is a line of sight path between the transmitter and the receiver

0 500 1000 15000

1

2

0 100 2000

1

2

addedattenuation

distance between transmitter and receiver (meters)

UDel Propagation Model

RaytracingOne of the most popular method for physical layer propagation modeling is Raytracing.

Problems -

missedarea

source

traced rays

reflective wallworst

reflective wall

Beamtracing

To avoid the problem faced in raytracing we use a variant of raytracing called Beamtracing

wall

wall tile

end pointof tilevirtual

source

first beam

second beam

Details

The buildings are composed of walls

These walls are divided into small tiles which are taken as reference points for reflection of rays

The terrain is divided into numerous floor tiles

The resolution fairly depends on the size of these tiles

Computation is divided into

- Preprocessing

- Beamtracing

Indoor Propagation Model

Due to the increased complexity we use a Attenuation Factor model which a fairly good approximation for Indoor propagation or for propagation from outside to inside

dR

d1 O

d2 O

dD

Number of floors

FL

0 0dB

1 30dB

2 25dB

3 38dB

4 or more 40dB

PL=αO10log(dO)+αR10log(dR)+αD10log(dD)+WL×n+FL(m),

αO >2αD > 2αR < 2

WL – attenuation per wall

Results of Raytracing - 1

Signal propagation due to source at the center of Mall

Pronounced wave guide effect

Results of Raytracing – 2

Results of Raytracing - 3

Wave guide down the hall

Propagation from Inside a Building

Floor 1 Floor 2 Floor 3

Signal strengths in different floors due to source in floor 1

Effects Due to Small Alleyways

ray_movie.swf