Remote Industrial Sensor Network Monitoring Using M2M Based Ethical Sniffers
Diagnosing the deployed network efficiency and anomaly detection, which is an important research issue in traditional networking systems, has not been carefully addressed in industrial wireless sensor networks. Although recent wireless systems for industrial automation such as ISA100.11a employ device management protocols, these protocols generate and report a large amount of status information from individual sensor nodes. Also, these protocols do not capture influences on network performance from external sources such as malicious nodes or interference from other networks. The authors propose a Latent Network Diagnosis System (LaNDS) for industrial sensor networks. LaNDS employs a packet sniffing method for efficiently evaluating network performance and instantly identifying degradation causes of networking performance.