A new random walk for efficient data collection in sensor networks

Motivated by the problem of efficiently collecting data from wireless sensor networks via a mobile sink, we present an accelerated random walk on Random Geometric Graphs. Random walks in wireless sensor networks can serve as fully local, very simple strategies for sink motion that significantly reduce energy dissipation but introduce higher latency in the data collection process. While in most cases random walks are studied on graphs like G_n,p and Grid, we define and experimentally evaluate our newly proposed random walk on the Random Geometric Graphs model, that more accurately abstracts spatial proximity in a wireless sensor network. We call this new random walk the γ-stretched random walk, and compare it to two known random walks; its basic idea is to favour visiting distant neighbours of the current node towards reducing node overlap. We also define a new performance metric called Proximity Cover Time which, along with other metrics such as visit overlap statistics and proximity variation, we use to evaluate the performance properties and features of the various walks.