Information-theoretic modeling of false data filtering schemes in wireless sensor networks

False data filtering schemes are designed to filter out false data injected by malicious sensors; they keep the network immune to bogus event reports. Theoretic understanding of false data filtering schemes and guidelines to further improve their designs are still lacking. This article first presents an information-theoretic model of false data filtering schemes. From the information-theoretic view, we define the scheme's filtering capacity CFi as the uncertainty-reduction ratio of the target input variable, given the output. This metric not only performs better than existing metrics but also implies that only by optimizing the false negative rate and false positive rate simultaneously, can we promote a scheme's overall performance. Based on the investigation from the modeling efforts, we propose HiFi, a hybrid authentication-based false data filtering scheme. HiFi leverages the benefits of both symmetric and asymmetric cryptography and achieves a high filtering capacity, as well as low computation and communication overhead. Performance analysis demonstrates that our proposed metric is rational and useful, and that HiFi is effective and energy efficient.