隨著IoT技術的發展，藍牙技術聯盟 (Bluetooth SIG) 在2017年訂定了藍牙網格網路 (Bluetooth Mesh Network) 的藍牙網路標準，使得支援此標準的藍牙裝置能組建一個藍牙的IoT網路。另一方面，室內適地性服務 (Indoor Location-Based Service) 一直是需求，包含了個人化服務、資產與人員管理、電子圍籬等應用，目前許多研究多是基於BLE Beacons 作為訊號放送端，而行動裝置作為接收端並進行座標計算，這種架構除了行動裝置的耗能問題外，座標追蹤也需透過網路傳送至伺服器當中。
本論文提出了基於 Bluetooth Mesh Network 的 IoT 燈具的無線燈光控制平台與室內定位平台架構。無線燈光控制平台方面，本論文在 iPad 上實作的符合 Bluetooth Mesh Network 的燈光開關與強度的的訊息協定，並開發一套燈光控制的 API 使遠端伺服器能透過藉由 iPad 架設的 HTTP Server 控制所有燈具。在室內定位方面，除了 iPad 上實作的符合 Bluetooth Mesh Network 的 RSSI 感測器設定與感測結果的訊息協定外，也使用以 BLE Beacons 為定位目標， Bluetooth Mesh 燈具為接收端的定位架構，並提出相對應的定位演算法。本論文定位演算法利用燈具安裝的特性，比較鄰近的燈具訊號強弱動態的決定做為參考點的感測器，並利用對數距離路徑損耗模型計算 Beacons 與感測器的距離並利用帶權重的質心定位法計算 Beacons 的座標。在精準度上，本論文採用 Kalman Filter 作為 RSSI 值的濾波與最終座標的平順化，使得最終精準度座標能達到誤差在 0.36m-1.27m 間並且標準差在 0.1m~0.47m 間。

Indoor Location-Based Service has a great demand in commercial buildings such as navigation, assets tracking, etc. In this thesis, we propose an indoor localization system based on Bluetooth mesh network. Bluetooth mesh network, which has been standardized recently, supports Bluetooth devices to establish an IoT mesh network. Based on the Bluetooth mesh network standard, we can easily install a sensor array of Bluetooth devices in a room with low effort.
In this work, we replaced our traditional lights in laboratory with Delta Bluetooth mesh lights, which can measure beacon signal strength and send the result through the Bluetooth network to a gateway (iPad). We implemented a HTTP API server on iPad so that the remote server can access the iPad to control the Bluetooth mesh lights and receive the beacon signal strength data from the Bluetooth mesh lights. The remote server calculates the beacon position as latitude and longitude based on the received sensor data and can control lights for individual beacon.
We develop an algorithm called “Reliable Sensor nodes weighted centroid Localization (RSL)” which is able to achieve an average localization error of 0.94m on this system. First, RSL scores top-6-RSSI-sensor groups by the distance between each sensor as counting the neighbor sensors. Then, RSL selects the sensor, which has at least two neighbors in the top-6-RSSI-sensors groups. Last, RSL estimates the position with WCL by using the selected sensor positions and the beacon RSSI values.

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