Several widely-used radio localization systems, such as GPS and cellular localization, rely on time-of-flight measurements of data-bearing signals to determine inter-radio distances. For such measurements to be meaningful, accurate synchronization is required. Synchronization becomes more important in emerging applications for large cooperative wireless networks, and has led to active research in the area of synchronization and localization. State-of-the-art solutions either adopt a two-step, first synchronize then localize paradigm, or perform centralized, simultaneous localization and synchronization that impose stringent constraints on the network topology. In this talk, we introduce a framework for distributed simultaneous localization and synchronization that overcomes these limitations. The framework consists of a Bayesian factor graph formulation for cooperative simultaneous localization and synchronization and is suited for wireless networks with mobile nodes and time-varying clock parameters. Building on this
factor graph, a distributed belief propagation algorithm is developed that allows for real-time operation and is suitable for a time-varying network connectivity. While numerical results indicate a similar localization accuracy as achieved in perfectly synchronized networks, demonstrator implementations validate the robustness of the algorithm in practice.