# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 3 Realistic Scenarios and Modelling Issues
### World Size and Simulation Time
#### 3.1.2 Movement Models  
The majority of the MANET research reported in the literature employs only one of the mobility models discussed below. Random way point or random waypoint (RWP) is the most commonly used mobility model ([PERSON], 1996, [10]; [PERSON] et al., 1998). In RWP, each node is placed randomly within the model world at the start of the simulation. For each node, a random destination is selected within the model world, and a random speed (within a given range) is used for the node's motion towards that destination. On reaching the destination, the node pauses for a random time period (within limits), selects a new destination, and then the process iterates. Seminal examples of RWP use are found in [PERSON] et al., 2001, [PERSON] et al. (2001) and [PERSON], 2004, [PERSON] et al. (2004). Its continual use is cited in [PERSON] al., 2012, [PERSON] and [PERSON], 2012, [PERSON] and [PERSON] (2012), and [PERSON] et al., 2012, [PERSON] et al. (2012). It is common to use RWP as a baseline when discussing realistic mobility models. Group and swarm mobility models have been compared with RWP ([PERSON] et al., 2004, [PERSON] et al., 2004), while [PERSON] et al. (2006)[PERSON] et al. (2006) report on a rush hour mobility model and compared it with RWP. The ns-2 (ns2, nd ([PERSON] et al., 2004)) distribution set includes a program (_setdest_) that generates ns-2 Monarch mobility files (Monarch Project, 2004, [PERSON] et al. (2013)) given a range of parameters, including node count, rectangular world size, pause time, and speed range. RWP is compared with other models in a number of publications (e.g., [PERSON] and [PERSON], 2013, [PERSON] and [PERSON], 2010), and the comparisons made are with Gauss-Markov ([PERSON] et al., 2009, [PERSON] et al., 2011), reference point group mobility (RPGM), and Manhattan using DSDV. Gauss-Markov, RPGM, and Manhattan have been discussed ([PERSON] et al., 2013, [PERSON] et al., 2012) with respect to AODV ([PERSON] et al., 2012, [PERSON] and [PERSON], 2013, [PERSON] and [PERSON], 2013, [PERSON] and [PERSON], 2012). [PERSON] and [PERSON] (2012)[PERSON] and [PERSON] (2012) explore three different routing protocols--AOMDV, optimized link state routing, and zone routing protocol--using a range of mobility models (RWP, RPGM, Manhattan, and Gauss-Markov). [PERSON] and [PERSON] (2012) have used RWP to evaluate new protocols using Gauss-Markov and random direction and also to evaluate their novel FASTRoute cluster-based routing protocol. Despite its popularity, RWP has been critiqued in literature. [PERSON] and colleagues (2002) show that the initial distribution of nodes is not representative of the way the nodes are distributed after a period of mobility. The solutions suggested are to run the model for a considerable time before ensuring that the nodes are in appropriate locations or to save a series of snapshots of the runs where the result of each run will feed forward as initial inputs to the subsequent run. The simulation runs do not always end up with a steady state but instead show a decreasing average speed over the simulation period ([PERSON] et al., 2003, [PERSON] et al. (2003)). In order to address this problem, the minimum speed is set to a non-zero value. However, neither of these approaches to the issues make the mobility produced any more realistic. Some variations on random waypoint exist. The random walk model dates back to the original work done by [PERSON] in 1926 on Brownian motion and is first implemented by [PERSON] and [PERSON] (2001) [PERSON] and [PERSON] (2001). Each node chooses a random speed and direction from pre-defined ranges at each time interval. On reaching the boundary, the node bounces back into the simulation world. The random direction model ([PERSON] et al., 2001, [PERSON] et al. (2001)) is developed to address the tendency for RWP nodes to congregate in the centre of the simulation area. This is done by selecting a direction of travel and stopping at a boundary. It then selects another direction, which is constrained by being at a boundary.