The document proposes a robust location fingerprint called Signal Strength Difference (SSD) to address the heterogeneity of mobile devices for indoor localization. SSD is derived analytically and tested experimentally using different mobile devices with heterogeneous hardware under various wireless conditions. Experiments consider both Wi-Fi and Bluetooth devices for AP-based and MN-assisted localization. Results show that SSD-based localization algorithms like K Nearest Neighbor and Bayesian Inference perform better than other approaches that aim to mitigate effects of hardware variations. SSD provides more accurate indoor positioning compared to the commonly used Received Signal Strength fingerprint.
Android ssd a robust rf location fingerprint addressing mobile devices’ heterogeneity
1. SSD A ROBUST RF LOCATION FINGERPRINT ADDRESSING
MOBILE DEVICES’ HETEROGENEITY
ABSTRACT:
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. We 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.
Our experiments have also considered both Wi-Fi and Bluetooth devices, as well as both Access-
Point (AP)-based localization and Mobile-Node (MN)-assisted localization. We present the
results of two well-known localization algorithms (K Nearest Neighbor and Bayesian Inference)
when our proposed fingerprint is used, and demonstrate its robustness when the testing device
differs from the training device. We also compare these SSD-based localization algorithms'
performance against that of two other approaches in the literature that are designed to mitigate
the effects of mobile node hardware variations, and show that SSD-based algorithms have better
accuracy.
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