Aggregation of user contexts in context-based communication

A context-based communication system enables the indirect addressing and routing of messages according to the users' contexts. This provides, for example, the means to send a message to all students on campus who attend a certain class, with information about an upcoming exam. However, for a targeted forwarding of messages towards users, the routers need information about the context of connected users. Global knowledge, i.e., each router knowing about every user, is not scalable, though, because of the necessary update messages to keep this information up-to-date. To address this challenge, a router can aggregate similar contexts and only provide such an aggregated view to neighboring routers. In this paper, we present an approach to aggregate similar contexts, based on a similarity measure for user contexts. The algorithm can be adjusted according to the observed messages and user contexts in the system by specifying a similarity threshold to determine when contexts are aggregated. The aggregation of user contexts improves the scalability of our approach by significantly reducing the load of context updates by up to 30%, depending on the usage of the system. This improvement comes at the cost of a negligible increase in false positive messages due to the loss of information used for forwarding messages.