Collecting data in sensor networks using homesick lévy walk

Abstract

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. Homesick Lévy walk is a family of random walks whose the distance of a walk is chosen from the power law distribution. It also comes back to a starting point in a certain probability. Thus, it is attractive for collecting data in large-scale sensor networks. In this paper, we propose an algorithm for Homesick Lévy walk and analyze the behavior of the algorithm on grid graphs.