The recent significant advancements in microchips, microelectromechanical systems, and microsensing technologies have given birth to a modern networking paradigm, namely, Shared Sensor Networks. Tiny sensor nodes serve as an essential infrastructure to build ap- plications such as the smart home, built infrastructure monitoring, building occupancy moni- toring, traffic monitoring, and environment monitoring as constituents of the above-mentioned networking paradigm. Sensor nodes are, however, susceptible to in situ faults and attacks. Sim- ilarly, sensor readings are vulnerable to in transit errors and attacks. An adequately designed anomaly detection system, as a second line of defense, can timely detect and report anomalies to a user. However, state-of-the-art anomaly detection schemes are not able to accurately iden- tify the source of anomalies. Their sole focus is merely on the detection of anomalies. The identification of the source of anomalies is imperative for their effective mitigation. This study has introduced a novel mobile agent-based in situ verification service which can identify the source of anomalies after their detection. A mobile-agent enabled anomaly detection and verification system, with its internal structure and algorithmic specifications, is presented for Wireless Sensor Networks (WSNs). The proposed system enables mobile agents to use information which is obtained through a Coordinated Resource Management (CRM) mechanism to perform an in situ diagnosis of sensor nodes. The CRM mechanism enables sensor nodes to share their resource status with corresponding cluster leaders or a base station for network resource management. In addition to traditional network resource management, our hypothesis is to employ the CRM-based information to detect several types of anomalies which are caused by erroneous values of sensor readings and battery status. One of the objectives of the proposed anomaly detection and verification system is to maximize the use of the received CRM-based observations for the anomaly detection purpose. The statistical association among different features of interest is, therefore, exploited to detect different natures of anomalies which are caused by denial of sleep attacks, sensor node faults, and resource exhaustion attacks. The proposed system further uses the information received from CRM-based observations to verify the source of anomalies using mobile agents.