# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 3 Realistic Scenarios and Modelling Issues
### Trace-Based Models  
As categorised by [PERSON] and colleagues (2012), all the mobility models previously mentioned may be called synthetic, because they incorporate randomly generated elements and operate in artificial worlds of regular size, shape, and organisation. This is a great contrast with an irregular and non-uniform real world. However, this issue could be addressed via the use of historical data for real movements or evidence-based traces to develop the models. Examples of trace data repositories are the Community Resource for Archiving Wireless Data at Dartmouth (CRAWDAD, 2008 [136]) and the Mobility Library (MobiLib) website ([PERSON], nd [137]). CRAWDAD contains a range of trace data from different sources to provide a large collection of valid trace data to the MANET research (e.g., vehicle trace data used by [PERSON] et al. (2012), [138]). CRAWDAD maintains a library on Citeulike (Citeulike, nd, [139]), with over 1000 published articles that have utilised this open source resource. MobiLib has a similar collection of traces (mostly via links to the originators) and publications. Public transport systems are a common source of trace data, because taxi cabs and buses routinely log data through logging equipment, which are essentially GPS trace recorders. A comprehensive dataset for taxis in Shanghai is used by [PERSON] et al. (2012) to derive parameters, such as turn probability and speed on given roads. These parameters are then utilised to derive a mobility model to generate synthetic movement sets. Subsequently, a comparison between the following is made: synthetic data and real data; derived mobility model and RWP mobility. The complexity of obtaining trace data and real-world experiments data is highlighted by [PERSON] et al. (2012) [140]. They report on a real-world experiment based on an emergency (earthquake) training exercise used to generate a trace file. It is observed that the nodes mobility was considerably different from commonly used models.  
Campus settings are a popular source of trace data, presumably due to readily available test subjects and high levels of equipment use (i.e., mobile phones) by students. [PERSON] et al. (2012) [141] split students' daily activities into submodels, which cover areas such as eating, learning, and transport. The activities identified within each submodel are necessarily restricted. However, the trace data used to build the model actually came from conference participants rather than from students studying in a university. Thus, this reduces the relevance of the model. A review of some publicly available user mobility data sets (which are trace-based) is presented by [PERSON] and [PERSON] (2012) [142]. Herein, a sample of CRAWDAD data sets is discussed, and a range of desirable characteristics for such data sets presented are data quality, granularity, size in terms of the number of nodes, and the total length of time covered by the data set and currency.  
Detailed discussion on mobility models is conducted by [PERSON] and colleagues (2012), which include comparisons between synthetic and trace-based models, on how traces could be obtained and how different data collection methods affected the resultant models. Their research discusses \"human mobility\", which refers to people moving on foot and yields a \"human walk\" mobility pattern, with no reference to vehicular models at all. They conclude that there is still significant mobility model-related research to be done and that real traces are a necessary tool in the evaluation of MANET solutions.