隨著Internet Of Things(IOT)技術快速的發展，位置感知技術被大量應於日常生活中各種智慧化的服務，因此位置感知技術扮著關鍵技術的角色。本論文著重於室內環境的位置感知技術。透過佈建在環境中之感測網路平台，位置感知技術可以隨時將感測到的資料，透過環境所建置的任何網路傳送到伺服器供人和其他程序使用，藉由進一步的處理與分析，讓使用者可以在不同的環境下，透過不同的網路與通訊環境，隨時隨地享受個人化及貼心的應用與服務。以室內定位來說由於使用Global Position System(GPS)時有Line-Of-Sight(LOS)的限制，所以不適用於室內環境定位。本論文以成本低不用任何額外硬體Received Signal Strength Indicator(RSSI)的定位方式為基礎，利用接收端所接收到的RSSI值來估算與傳送端的距離，由於室內環境中的擺設非常複雜會造成無線訊號的衰減，同時也會造成RSSI定位方法估算待測物座標的誤差。所以我們提出一個雙模式的室內感知演算法，根據bind node處於靜置時的狀態或是處於動態時的tracking狀態下會自動切換成Path Loss Exponent Estimation for Indoor Wireless Sensor Positioning Algorithm與Environment-Assisted Indoor Wireless Sensor Position Tracking Algorithm。並經由實驗可以證實不論blind node處於靜置時的定位或是blind node是處於動態時的tracking狀態下都能達到定位誤差小於2公尺。在即使處在於不利於RF傳輸的環境下，定位誤差也可以控制在2.21公尺左右。由此可以證明本論文提出的演算法可以在低成本的前提下提供更精確的定位服務。

Rapid advances in theInternet of Things (IOT), have led to the pervasive use of location-aware technologies in daily life for many intelligent services, explaining why they are a major component of IOT. This thesis focuses on use of location-aware technologies in an in-door environment. Distributing sensor network platforms throughout the surrounding environment allows location-aware technologies transfer the gathered data instantly, to the server or other processes for usage via any network setup in the surroundings. By adding data processing and data analysis, users can enjoy personalized applications and services anytime anywhere, through various networks and communication environments as well as in different surrounding environments. Using Global Position System (GPS) in-doors may have Line-of-Sight (LOS) limitations at certain times, making it unsuitable for positioning of in-door surroundings. This work uses Received Signal Strength Indicator (RSSI) positioning as the basis method, owing to its low-cost and no requirement for additional hardware. Also, the distance between the receiver and transmitter can be calculated by using the RSSI value received by the receiver.Owing to complex in-door obstructions, wireless signal attenuation may occur, subsequently causing errors in the RSSI positioning method when calculating the object coordinates. Therefore, this work develops a Dual-Mode Indoor Location-Aware algorithm, which interchanges the positioning algorithm between the Path Loss Exponent Estimation for Indoor Wireless Sensor Positioning Algorithm and Environment-Assisted Indoor Wireless Sensor Position Tracking Algorithm, according to whether the blind node is static or in motion. Experimental results indicate regardless of whether blind nodes are static or in motion, the positioning deviation is less than 2 meters. Even when using surroundings that are inconducive to RF transmission, the positioning deviation can be controlled within 2.21 meters. Results of this thesis demonstrate that the proposed algorithm performs better than other positioning algorithm in terms of positioning accuracy and low cost.

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