Sensor network deployment is becoming more commonplace in environmental, business and military applications. For example, the sensor nodes in a wireless sensor network can be used collaboratively to collect data for the purpose of observing, detecting and tracking scientific phenomena. Sensor systems have significant potential for aiding scientific discoveries by instrumenting the real world.
Keywords: Autoregressive hidden Markov models environment sensing filtering corrupted nodes sensor network clustering anomaly detection Simulations using both synthetic and real datasets show greater than 90% accuracy in identifying healthy nodes with ten nodes datasets and as high as 97% accuracy with 500 or more nodes datasets. Our approach is a simple, decentralized model to identify compromised nodes at a low computational cost. The existing algorithms are centralized and computation intensive. For each node, we train an AR-HMM based on the sensor's readings, and subsequently the B matrices of the trained AR-HMMs are clustered together into two groups: healthy and compromised (both self-healing and corrupted), which permits us to identify the group of healthy sensors. A different AR-HMM ( A, B, π) is used to describe each of the three types of nodes. We assume that sensors are healthy, self-healing and corrupted whereas each node submits a number of readings. We propose a method based on autoregressive hidden Markov models (AR-HMM) for filtering out compromised nodes from a sensor network.
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