3 research outputs found
Blind Sensor Calibration using Approximate Message Passing
The ubiquity of approximately sparse data has led a variety of com- munities
to great interest in compressed sensing algorithms. Although these are very
successful and well understood for linear measurements with additive noise,
applying them on real data can be problematic if imperfect sensing devices
introduce deviations from this ideal signal ac- quisition process, caused by
sensor decalibration or failure. We propose a message passing algorithm called
calibration approximate message passing (Cal-AMP) that can treat a variety of
such sensor-induced imperfections. In addition to deriving the general form of
the algorithm, we numerically investigate two particular settings. In the
first, a fraction of the sensors is faulty, giving readings unrelated to the
signal. In the second, sensors are decalibrated and each one introduces a
different multiplicative gain to the measures. Cal-AMP shares the scalability
of approximate message passing, allowing to treat big sized instances of these
problems, and ex- perimentally exhibits a phase transition between domains of
success and failure.Comment: 27 pages, 9 figure
Detecting Faulty Wireless Sensor Nodes through Stochastic Classification
In many distributed systems, the possibility to adapt the behavior of the involved resources in response to unforeseen failures is an important requirement in order to significantly reduce the costs of management. Autonomous detection of faulty entities, however, is often a challenging task, especially when no direct human intervention is possible, as is the case for many scenarios involving Wireless Sensor Networks (WSNs), which usually operate in inaccessible and hostile environments.
This paper presents an unsupervised approach for identifying faulty sensor nodes within a WSN. The proposed algorithm uses a probabilistic approach based on Markov Random Fields, requiring exclusively an analysis of the sensor readings, thus avoiding additional control overhead. In particular, abnormal behavior of a sensor node will be inferred by analyzing the spatiotemporal correlation of its data with respect to its neighborhood. The algorithm is tested on a public dataset, over which different classes of faults were artificially superimposed