Deep Validation of Spatial Temporal Features of Synthetic Mobility Models

Abstract

This paper analyzes the most relevant spatial-temporal stochastic properties of benchmark synthetic mobility models. Each pattern suffers from various mobility flaws, as will be shown by the models' validation. A set of metrics is used to describe mobility features, such as the speed decay problem, the density wave phenomenon, the spatial node distribution, and the average neighbor percentage. These metrics have already been validated for the random waypoint mobility model (RWPMM), but they have not yet been verified for other mobility patterns that are most frequently used. For this reason, this investigation attempts to deeply validate those metrics for other mobility models, namely the Manhattan Grid mobility, the Reference Point Group mobility, the Nomadic Community mobility, the Self-Similar Least Action Walk, and SMOOTH models. Moreover, we propose a novel mobility metric named the "node neighbors range". The relevance of this new metric is that it proves at once the set of outcomes of previous metrics. It offers a global view of the overall range of mobile neighbors during the experimental time. The current research aims to more rigorously understand mobility features in order to conduct a precise assessment of each mobility flaw, given that this fact further impacts the performance of the whole network. These validations aim to summarize several parameters into 18,126 different scenarios with an average of 486 validated files. An exhaustive analysis with details like those found in this paper leads to a good understanding of the accurate behaviors of mobility models by displaying the ability of every pattern to deal with certain topology changes, as well as to ensure network performances. Validation results confirm the effectiveness and robustness of our novel metric.

Performance Analysis of OLSR Protocol in Mobile Ad Hoc Networks

“…According to the leader's mobility decisions, the entire group moves at random from one place to another. This versatile pattern is used in mobile communications for military applications, conferences, and class visits to museums [14]. Figure 4 present an example of node movement using NC.…”

“…The second one is reactive routing protocol and called on-demand protocols, and the last one is a hybrid routing protocol. However, the overall performance of ad hoc networks is influenced by mobility models like the Random Waypoint (RWP) [12], Random Direction (RD) [13], Nomadic Community [14], and Reference Point Group Mobility Model (RPGM) [15]. The change in the direction and speed of nodes depend on the type of mobility models used in the network.…”

Flexible synthetic mobility modeling to discover trajectories for complex areas of mobile wireless networks

“…Henee, these mobility models assume that mobile nodes can freely travel without any obstruction. These selections produce many mobility problems for these models, as pointed out in Panisson et al (2012) and Ibadah et al (2018b). In the case of models with temporal dependeney, the devices are governed by physical laws of motion, where the current mobility decisión mainly depends on the history of movements (Cong et al 2015).…”