Android ssd a robust rf location fingerprint addressing mobile devices’ heterogeneity
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ECway Technologies provides IEEE projects and software developments with offices in multiple cities in Tamil Nadu, India. They can be contacted via phone or online. The document discusses a robust RF localization fingerprint approach called Signal Strength Difference (SSD) that addresses the heterogeneity of mobile devices. SSD is proposed as an alternative to Received Signal Strength (RSS) due to RSS varying significantly across different hardware. Experiments show SSD provides more robust localization when the testing device differs from the training device. SSD-based algorithms outperform other approaches that aim to mitigate the effects of mobile node hardware variations.
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Android ssd a robust rf location fingerprint addressing mobile devices’ heterogeneity
- 1. ECWAY TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS OUR OFFICES @ CHENNAI / TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE CELL: +91 98949 17187, +91 875487 2111 / 3111 / 4111 / 5111 / 6111 VISIT: www.ecwayprojects.com MAIL TO: ecwaytechnologies@gmail.com 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 AccessPoint (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.