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Fingerprint-based methods are widely adopted for indoor localization purpose because of their cost-effectiveness compared to other infrastructure-based positioning systems. However, the popular location fingerprint, Received Signal Strength (RSS), is observed to differ significantly across different devices' hardware even under the same wireless conditions. The authors derive analytically a robust location fingerprint definition, the Signal Strength Difference (SSD), and verify its performance experimentally using a number of different mobile devices with heterogeneous hardware. Their experiments have also considered both Wi-Fi and Bluetooth devices, as well as both access-point-based localization and mobile-node-assisted localization. They present the results of two well-known localization algorithms (K Nearest Neighbor and Bayesian Inference) when their proposed fingerprint is used, and demonstrate its robustness when the testing device differs from the training device.
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