Random walks play an important role in computer science, spreading a wide range of topics in theory and practice, including networking, distributed systems, and optimization.
Lévy walk is a family of random walks whose the distance of a walk is chosen from the power law distribution. There are lots of works of Lévy walk in the context of target detection in swarm robotics, analyzing human walk patterns, and modeling the behavior of animal foraging in recent years. According to these results, it is known as an efficient method to search in a two-dimensional plane. However, all these works assume a continuous plane, so far.
In this research project, we show the comparison of Lévy walk with various scaling parameters on the message dissemination problem. Our simulation results indicate that the smaller scaling parameter of Lévy walk diffuses a message efficiently compared to the larger one.