Exploiting social information for dynamic tuning in cluster based WiFi localization

While WiFi-based indoor localization services are on the rise, existing solutions require periodic updates and therefore exhibit high power demand. In this paper, we propose a novel Social Aware Cluster Based Localization algorithm (SAC-Loc) that leverages social information between nodes in order to dynamically cluster those that exhibit similar mobility patterns. SAC-Loc deploys socially-aware algorithms that dynamically determine when to split and coalesce clusters depending on predicted network topology changes. Based on social ties between encountered nodes, it estimates cluster stability metrics in order to avoid joining crossing nodes with temporary proximity, or the unnecessary splitting of a group due to wireless scanning limitations. We analyze and evaluate our algorithms using a data driven approach based on real-world traces, in addition to an experimental implementation and deployment in our department. While state-of-the-art group localization algorithms can either be energy efficient or highly accurate, SAC-Loc provides a desirable trade-off between accuracy and energy consumption based on popular indoor localization applications.