Here we survey various computational models for Wireless Sensor Networks (WSNs). The population protocol model (PP) considers networks of tiny mobile finite-state artifacts that can sense the environment and communicate in pairs to perform a computation. The mediated population protocol model (MPP) enhances the previous model by allowing the communication links to have a constant size buffer, providing more computational power. The graph decision MPP model (GDM) is a special case of MPP that focuses on the MPP's ability to decide graph properties of the network. Another direction towards enhancing the PP is followed by the PALOMA model in which the artifacts are no longer finite-state automata but Turing Machines of logarithmic memory in the population size. A different approach to modeling WSNs is the static synchronous sensor field model (SSSF) which describes devices communicating through a fixed communication graph and interacting with their environment via input and output data streams. In this survey, we present the computational capabilities of each model and provide directions for further research.