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rding to the knowledge of nodes motion and the probability of availability of future paths. When the random direction model is applied in this manner, it determines system capability and ensures support for user communication and network reliability. Thus, route stability based on quality of service is perceived by network users. Hence, the importance of random mobility models is stressed in their usefulness in the analytical approach of studying the behavior of network routes.

 

Gauss-Markov mobility model

This model uses a single tuning parameter to vary the degree of randomness in the mobility pattern. The next location of a node is determined and generated by the preceding location and velocity. In a nutshell, a mobile node is initially assigned a velocity and a direction. An update of direction and speed is applied to it constantly at fixed time intervals. The Gauss-Markov mobility model differs from the other models in that subsequent node movements are dependent on previous movements. The degree of this dependence is adapted by a distinct parameter denoted as α. This parameter varies between values of 0 and 1 (0≤α≤1). If for instance the parameter is equal to zero, it means that the new movement does not depend the preceding movements, a result that is similar to the random walk model. On the other hand, if the parameter is within the range specified earlier, then it means that intermediate levels of randomness are achieved. Finally, if α is equal to negative unity, then the entity is said to be moving in a linear mode.

 

Figure 4: Diagram of Gauss-Markov model

 

Source: Klaus & Gross

 

The above figure illustrates the travelling pattern of a mobile node using the Gauss-Markov model which begins in the center of the simulation. In the Gauss-Markov model, the frequency of link change in a network increases exponentially with the increase in the mobility of a node. The result is a relatively lower throughput especially in the commonly used multicast protocols (Meghanathan, 2010). The average speed can be specified for a mobile node in this model. In simulation, collisions with the simulation boundary are avoided by adapting the direction of a node when it approaches the boundary. When the entity reaches a certain distance towards the boundary, it is forced away from it in another direction. The current direction is modified to move automatically away from the boundary as a basis of calculation for the next step. Hence, the entity is prevented from dwelling near a boundary for prolonged periods. The expiry of the predefined time interval allows for calculation of a new direction and speed according to the current location, velocity and direction.

 

The Gauss-Markov mobility model does not exhibit the sharp stops and turns experienced in the previously discussed mobility models. This is because it adapts the direction and velocity updates based on the curren本论文由英语论文网提供整理，提供论文代写，英语论文代写，代写论文，代写英语论文，代写留学生论文，代写英文论文，留学生论文代写相关核心关键词搜索。