Analysis of the Random Direction Mobility Model with a Sense-and-Avoid Protocol

Random mobility models (RMMs) capture the random movement patterns of mobile agents, and have been widely used in the evaluation and design of mobile networks. Existing RMMs (e.g. random walk and random direction) assume every mobile agent to move independently. Unmanned aerial systems (UASs), on the other hand, need to maintain a separation distance for the safety of the airspace, and hence their mobility patterns violate the independent movement assumption of existing RMMs. In this paper, for the first time in the literature per knowledge of authors, we enhance the Random Direction RMM through equipping it with a commonly used decentralized sense and avoid protocol-- sense-and-stop (S&S). We provide analytical results on critical networking statistics such as stationary node distribution and inter-vehicle distance distribution in a two-dimensional (2-D) space using the Markov type of analysis. The analysis and simulation studies lead to interesting insights such as that the commonly used S&S protocol is not effective when the randomness of UAV mobility is high, as it increases the probability for a UAS to stay within the collision distance.