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randomway.docx2.3.1 Random Based Mobility Models
In this class of model, the nodes drift haphazardly in a free manner without placing any limits. Every node arbitrarily selects the end point, the velocity, and path to reach the destined address, without being dependent on the other nodes. One of the well-known mobility models under this class is the random waypoint model, widely used in the simulation analysis. The random walk model and the random direction model are the different forms of the random waypoint model [10].
2.3.1.1 Random Waypoint Model
For the simulation analysis of this research, we have used the random waypoint model. This has been a standard model to measure the performance of mobile ad hoc routing protocols primarily because it is easy to use and well accessible. This model is implemented in the following manner. When the simulation starts, each node chooses a location in simulation field randomly as its destination. Then the node travels toward this destination with a constant speed selected uniformly and arbitrarily from [0, Vmax]. The argument Vmax is maximum speed with a mobile node can as the node reaches its destination; it halts for some period called pause time. If the pause time is zero, then the nodes move freely without interruption. After this period, the node once again selects an arbitrary destination in the simulation filed and proceeds toward it. This entire process is reiterated repeatedly until the simulation ends [10].
2.3.1.2 Limitations of the Random Waypoint Model
The Random Waypoint Model and its variants are designed to mimic the movement of mobile nodes in a simplified way. Because of their simplicity of implementation and analysis, they are widely accepted. However, they may not adequately capture certain mobility characteristics of some realistic scenarios, including temporal dependency, spatial dependency, and geographic restriction.
2.3.1.2.1 Temporal Dependency of Velocity
In the Random Waypoint Model, the velocity of a mobile node is a memoryless random process, that is, the velocity at the current epoch is independent of the previous epoch. Thus, some extreme mobility behavior, such as sudden stopping, sudden accelerating, and sharp turning, may frequently occur in the trace generated by the Random Waypoint Model. However, in many real-life scenarios, the speed of vehicles and pedestrians will accelerate incrementally. In addition, the direction change is also smooth.
2.3.1.2.2 Spatial Dependency of Velocity
In the Random Waypoint Model, the mobile node is considered an entity that moves independently of other nodes. This kind of mobility model is classified as an entity mobility model. However, in some scenarios, including battlefield communication and museum touring, the movement pattern of a mobile node may be influenced by a certain specific “leader” node in its neighborhood. Hence, the mobility of various nodes is indeed correlated.
2.3.1.2.3 Geographic Restrictions of Movement
In the Random Waypoint Model, the mobile nodes can move freely within the simulation field without any restrictions. However, in many realistic cases, especially the applications used in urban areas, the movement of a mobile node may be bounded by obstacles, buildings, streets, or freeways.
The Random Waypoint Model and its variants fail to represent some mobility characteristics likely to exist in mobile ad hoc networks. Thus, several other mobility models were proposed.
2.3.2 Temporal Dependency Models
The mobility of a node may be constrained and limited by the physical laws of acceleration, velocity, and rate of change of direction. Hence, the current velocity of a mobile node may depend on its previous velocity. Thus, the velocities of single node at different time slots are “correlated.”
This mobility characteristic is called the temporal dependency of velocity. The memoryless nature of the Random Waypoint Model render inadequate to capture this temporal dependency behavior. As a result, various mobility models considering temporal dependency are proposed.
2.3.3 Spatial Dependency Models
In the Random Waypoint Model, a mobile node moves independently of other nodes, that is, the location, speed, and movement direction of a mobile node are not affected by other nodes in the neighborhood. As previously mentioned, these models do not capture many realistic scenarios of mobility. For example, in the case of a vehicle on a freeway attempting to avoid a collision, the vehicle’s speed cannot exceed that of the vehicle ahead of it. Moreover, in some targeted ad hoc networks applications including disaster relief and battlefields, team collaboration among users exists and the users are likely to follow the team leader. Therefore, the mobility of a mobile node could be influenced by other neighboring nodes. Because the velocities of different nodes are “correlated” in space, we call this characteristic the Spatial Dependency of Velocity.
2.3.4 Geographic Restriction Model
Another limitation of the Random Waypoint Model is the unconstraint motion of the mobile node. Mobile nodes, in the Random Waypoint Model, are allowed to move freely and randomly anywhere in the simulation field. However, in most real-life applications, we observe that a node’s movement is subject to its environment.
In particular, the motions of vehicles are bounded to the freeways or local streets in the urban area, and on campus the pedestrians may be blocked by the buildings and other obstacles. Therefore, the nodes may move in a pseudorandom way on predefined pathways in the simulation field. Some recent works address this characteristic and integrate the paths and obstacles into mobility models. This kind of mobility model is called a mobility model with geographic restriction. The Manhattan Mobility Model is a popular geographic restriction model which we have used in our simulation. The following section describes the Manhattan Mobility Model in detail.
One simple way to integrate geographic constraints into the mobility model is to restrict the node movement to the pathways in the map. The map is predefined in the simulation field and utilizes a random graph to model the map of the city. This graph can be either randomly generated or carefully defined based on a certain map of a real city. The vertices of the graph represent the buildings of the city, and the edges model the streets and freeways between those buildings.
