Impact of Regularization Factor of a 3-D Gaussian-Markov Mobility Model on the Performance of Routing Protocols in FANETs

Accurate mobility models are crucial to suitably model different types of FANETs’ movements, including both rapid and sporadic changes in directions and movement in straight lines. Due to its capability to model the mobility of flying ad-hoc nodes in three dimensions, Gaussian-Markov mobility model has been examined in this work. Our analysis covers two parameters with the greatest impact on the performance of the network; the regularization factor which determines the impact of memory when updating the variables and the time step, which expresses the frequency of updating the parameters. Two commonly known protocols, OLSR and AODV from proactive and reactive types, respectively, have been used to examine the network performance in terms of mean transmission delay and packet delivery ratio (PDR) while the Gaussian-Markov is being applied on the nodes. It is shown that as the regularization factor increases and nodes move more randomly, the performance of both types of protocols degrades in terms of both delay and PDR. For small values of alpha (alpha < 0.6 ), the delay remains almost unchanged in both protocols when the variables are updated frequently, e.g. ts = 0.1 s. The PDR is also shown to decrease as the nodes travel in faster speeds (30 m/s) for all values of . The reactive protocol delivers better network performance when more flying nodes are deployed as relay nodes; however, the proactive protocol fails to achieve the same results due to the redundant control messages passed through the network.