Utilizing PCFGs for Modeling and Learning Service Compositions in Sensor Networks

Service composition in sensor networks combines elementary services with a specific functionality to create a service with higher level functionality. The previous efforts in automating composition were sending full information about all services across the entire sensor network, creating a security risk and imposing significant communication overhead. Furthermore, learning based composition or error detection methods do not consider global information, leading to inefficiencies in the generated composition graphs. In this paper, we propose a probabilistic context-free grammar (PCFG) based modeling technique to construct service compositions. The successful compositions created for the given application are treated as statements belonging to an efficient composition PCFG of this application. The given set of such compositions is used to derive this PCFG automatically. Future composition could be then easily constructed with the help of such PCFG. We present our methodology for achieving such modeling and provide examples of its use to demonstrate its advantage over previous work. We also evaluate the resulting improvements in performance of compositions and in the costs of their creation.