A lightweight bias correction algorithm for wireless sensor networks

Sensor nodes are usually left unattended for long periods of time which makes them prone to failures due to either lack of energy or due to the systematic errors. This poses a great problem as the data from the network becomes progressively useless. To address this critical problem, an early detection and correction algorithm of anomalous data is studied based on the fact that the measurements of one sensor can be related with the measurements of its neighbours. The result of fusion is computed by using average value of a cluster of sensors. The simulation results showed that using this algorithm will result in increasing the effective life span of the sensor network.