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Fast anomaly detection and localization is critical to ensure effective functioning of wireless sensor networks. The low bandwidth and power constraints in wireless sensor networks are the main challenges for achieving this task, especially for large scale networks. In this paper, we propose a trust-assisted framework for detecting and localizing network anomalies in a hierarchical sensor network. The proposed method makes inference based on end-to-end measurements collected by a set of measurement nodes. Network heterogeneity is exploited for better bandwidth and energy efficiency. The trustworthiness of network links is utilized to design an efficient two-phase probing strategy that can achieve a flexible tradeoff between inference accuracy and probing overhead. We performed experiments with different network settings and demonstrated the effectiveness of our proposed algorithms.