We collected mobility traces of 84,208 avatars spanning 22 regions over two months in Second Life, a popular networked virtual environment. We analyzed the traces to characterize the dynamics of the avatars mobility and behavior, both temporally and spatially. We discuss the implications of the our findings to the design o f peer-to-peer networked virtual environments, interest management, mobility modeling of avatars, server load balancing and zone partitioning, client-side caching, and prefetching.
Most recently, La and Pietro have independently conducted a similar study on mobility in Second Life @cite_17 . Their study, however, focuses on metrics relevant to mobile communications, such as graph theoretic properties of line-of-sight networks formed by the avatars, travel length and time of avatars, and contact opportunities among avatars. Their goal is to use the mobility traces of avatars to model human mobility for applications related to wireless and delay-tolerant networks. On the other hand, we focus on metrics that are of interest to systems design of NVEs.
@cite_17
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In this work we present a measurement study of user mobility in Second Life. We first discuss different techniques to collect user traces and then focus on results obtained using a crawler that we built. Tempted by the question whether our methodology could provide similar results to those obtained in real-world experiments, we study the statistical distribution of user contacts and show that from a qualitative point of view user mobility in Second Life presents similar traits to those of real humans. We further push our analysis to line of sight networks that emerge from user interaction and show that they are highly clustered. Lastly, we focus on the spatial properties of user movements and observe that users in Second Life revolve around several point of interests traveling in general short distances. Besides our findings, the traces collected in this work can be very useful for trace-driven simulations of communication schemes in delay tolerant networks and their performance evaluation.