Machine learning techniques identify high quality solutions of mental health problems among Facebook users.

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Wireless Sensor Network (WSN) technology has recently moved out of controlled laboratory settings to realworld deployments. Many of these deployments experience high rates of failure. Common types of failure include Node Failure, Link Failure and Node Reboot. Due to the resource-constraints of sensor nodes, existing techniques for fault detection in enterprise networks are not applicable. Previously proposed WSN fault detection algorithms either rely on periodic transmission of node status data or inferring node status based on passive information collection. The former approach significantly reduces network lifetime, while the latter achieves poor accuracy in dynamic or large networks. Herein, we propose Sequence Based Fault Detection (SBFD), a novel framework for network fault detection in WSNs. The framework exploits in-network packet tagging using the Fletcher Checksum and server-side network path analysis to efficiently deduce the path of all packets sent to the Sink. The Sink monitors the extracted packet paths to detect Persistent Path Changes which are indicative of network failures. When a failure is suspected, the Sink uses control messages to check the status of the affected nodes. SBFD was implemented in TinyOS on TelosB motes and its performance was assessed in a testbed network and in TOSSIM simulation. The method was found to achieve a fault detection accuracy of 90.7% to 95.0% for networks of 25 to 400 nodes at the cost of 0.164% to 0.239% additional control packets and a 0.5% reduction in node lifetime due to in-network packet tagging. Finally, a comparative study was conducted with existing solutions.

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