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UDelModels Vinay Sridhara (Department of Electrical and Computer Engineering) University of Delaware

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What are we looking at ?

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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

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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

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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

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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.

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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

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Gauss-Markov Model The speed and direction of the n th step depends on the (n-1) st step S n = alpha* S n-1 + (1-alpha)*S mean +sqrt(1-alpha 2 )S xn-1 d n = alpha* d n-1 + (1-alpha)*d mean +sqrt(1-alpha 2 )d xn-1 alpha – tuning parameter S mean and d mean are mean values of speed and direction S xn-1 and d xn-1 are Random Variables from Gaussian distribution When alpha = 0 we get totally random node movement When alpha = 1 we get a perfectly linear node movement

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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

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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 Buildings Sidewalks Roads Environment

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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

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office vertices hallway vertices end of hallway vertex - adjacent to floors above and below sidewalk-to-building- connector office vertex converted into hallway vertex sidewalk-to sidewalk- connectors two buildings make up a building complex Buildings and Offices Contd.

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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

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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

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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

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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

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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

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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

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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)

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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]

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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

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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 U i = for the office vertices and U i = 2 for the sidewalk to building connector The probability of choosing a vertex is hence

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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

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Mobility Model

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Mobility Model Working

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UDMapBuilder Graphical utility for building the cities

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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

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Free Space Model Free Space propagation model is one of the most basic models P r = (P t )*C/d 2 P t is the transmitted power P r is the received power C is a constant depending on the transmission criteria like wavelength d is the distance between transmitter and receiver

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Fast Fading The fading that occurs due to small movements in the source or receiver. Pr = cos(2 ft) two rays delayed by L Pr = acos(2 ft) + bcos(2 f(t-L)) if L = 1/2f then Pr = (a-b)cos(2 ft) if L = 1/f then Pr = (a+b)cos(2 ft) Rayleigh and Riciean Models are used to model this effect

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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

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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

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Ground Reflections Occurs when there is a line of sight path between the transmitter and the receiver added attenuation distance between transmitter and receiver (meters)

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UDel Propagation Model

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Raytracing One of the most popular method for physical layer propagation modeling is Raytracing. Problems - missed area source traced rays reflective wall worst reflective wall

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Beamtracing To avoid the problem faced in raytracing we use a variant of raytracing called Beamtracing wall wall tile end point of tile virtual source first beam second beam

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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

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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 dRdR d1 O d2 O dDdD Number of floors FL 00dB 130dB 225dB 338dB 4 or more40dB PL=α O 10log(d O )+α R 10log(d R )+α D 10log(d D )+WL×n+FL(m), α O >2 α D > 2 α R < 2 WL – attenuation per wall

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Results of Raytracing - 1 Signal propagation due to source at the center of Mall

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Pronounced wave guide effect Results of Raytracing – 2

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Results of Raytracing - 3 Wave guide down the hall

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Propagation from Inside a Building Floor 1 Floor 2 Floor 3 Signal strengths in different floors due to source in floor 1

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Effects Due to Small Alleyways

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