This paper focuses on resource-aware and cost-effective indoor-localization at room-level using RFID technology. In addition to the tracking information of people wearing active RFID tags, we also include information about their proximity contacts. We present an evaluation using real-world data collected during a conference: We complement state-of-the-art machine learning approaches with strategies utilizing the proximity data in order to improve a core localization technique further.