Abstract: A novel solution to generate an optimal spatial placement map of Radio Frequency (RF) beacons, such as Bluetooth Low Energy (BLE) beacons, which can be used for indoor localization. A described embodiment solves for both generating optimal number of beacons required in a given environment and optimizing their location. The solution also incorporates behavior of RF signal due to various environmental factors such as signal reflection and absorption due to static and dynamic obstacles. In one embodiment, the framework is built upon Genetic Algorithm (GA) for optimization of beacon placement. The experimental results achieved using one implementation of the described method reduced the number of beacons by 50% as compared to beacons placed using expert knowledge. Furthermore, the overall localization error increased by ˜0.4 m when the results using the RF/BLE map generated by the described system were compared with the RF/BLE map generated using expert knowledge.

Abstract: A novel solution to generate an optimal spatial placement map of Radio Frequency (RF) beacons, such as Bluetooth Low Energy (BLE) beacons, which can be used for indoor localization. A described embodiment solves for both generating optimal number of beacons required in a given environment and optimizing their location. The solution also incorporates behavior of RF signal due to various environmental factors such as signal reflection and absorption due to static and dynamic obstacles. In one embodiment, the framework is built upon Genetic Algorithm (GA) for optimization of beacon placement. The experimental results achieved using one implementation of the described method reduced the number of beacons by 50% as compared to beacons placed using expert knowledge. Furthermore, the overall localization error increased by ˜0.4 m when the results using the RF/BLE map generated by the described system were compared with the RF/BLE map generated using expert knowledge.