Scalable interest management using interest group based filtering for large networked virtual environments

Abstract

As networked virtual environment(NVE)scales in terms of users and network latency, a key aspect to consider is scalability for interactive performance because a large number of objects likely impose heavy burden especially on the network and computational resources. To improve the scalability, various relevance-filtering mechanisms have been proposed. However the existing filtering mechanisms do not scale well in terms of interactive performance as the number of users increases and crowds in a specific place. In this thesis, we propose a new scalable filtering scheme that reduces the number of messages by dynamically grouping users based on their interests and distance. Within a group, members communicate with each other with high fidelity. However, a representative sends up-to-dated group information of members with low transmission frequency when they are not of immediate interest but are still within the interest area. The representative is elected from members of the group in distributed manner. The proposed scheme enhances the interactive performance scalability of large-scale DVE systems as much as 18% compared with the existing approach.