Bias estimation in sensor networks
Provides theoretical guarantees for bias estimation in sensor networks, which is crucial for applications like distributed control and localization.
This paper identifies conditions on network topology and number of biased sensors for correct bias estimation in sensor networks, showing that biases can always be determined in non-bipartite graphs even if all sensors are corrupted, while bipartite graphs require more than half unbiased sensors.
This paper investigates the problem of estimating biases affecting relative state measurements in a sensor network. Each sensor measures the relative states of its neighbors and this measurement is corrupted by a constant bias. We analyse under what conditions on the network topology and the maximum number of biased sensors the biases can be correctly estimated. We show that for non-bipartite graphs the biases can always be determined even when all the sensors are corrupted, while for bipartite graphs more than half of the sensors should be unbiased to ensure the correctness of the bias estimation. If the biases are heterogeneous, then the number of unbiased sensors can be reduced to two. Based on these conditions, we propose some algorithms to estimate the biases.