Mobility Models as a Key-Performance Factor for Wireless Networks

Abstract

Nodes in wireless networks can be static or dynamic. Static nodes are stationary and their positions are fixed and their distribution may follow a particular topology. On the other hand, dynamic nodes are considered mobile and their positions changed over time due to following a specific pattern of movement, which also lead to the concept of the topology being irrelated. Simulating such networks needs a lot of attention and many requirements should be held. For instance, nodes in dynamic wireless networks are distributed according to a distribution that reflects the nature of the environment (e.g., Gaussian, Power-Law, Uniform distribution, etc.). Moreover, describing the movement patterns of nodes in dynamic wireless networks need to follow a particular mobility model that describes the movements of nodes in terms of speed and direction (e.g., Individual mobility model, Levy Flight Model, Cauchy Model). In addition to the aforementioned requirements, it is needed to incorporate a routing protocol that governs data spread within the simulation environment. This work designed a variety of experiments that combine the mentioned requirements and measure the performance of dynamic wireless networks under colorful settings and configurations. The metrics used in this approach are the amount of data exchanged, coverage area, and power and memory consumption. Mobility models, node deployment strategy (distributions), and routing protocols are the three factors that will be involved in the experiments. The experiments deal with four mobility models Random Way Point Mobility Model (RWPM), Street Random Way Point Mobility Model (SRWPM), Manhattan Mobility Model (MM), and Levy Flight Mobility Model (LFM). Three distribution models will be used in the experiments Power-Law Distribution, Chi-Squared Distribution, and Normal Distribution. The third factor that will be involved in the routing protocol, is Probabilistic Flooding Routing Protocol. Different experiments are benchmarked using these metrics. The results of this work will reveal facts about dynamic network simulations and provide some recommendations to network developers and architects.