近來，室內定位技術相關應用如：醫療照顧、工廠人員導引以及賣場管理等，被熱烈的提出並且予以討論，這些應用可以利用無線感測網路(wireless sensor network)來加以實現，如此吸引了許多研究者投入探討無線感測網路中的室內定位技術。一般所使用的定位技術包含兩種：一種是幾何三角定位法(geometrical triangulation)，另一則是地區特徵指紋比對法(location fingerprinting)；在室內的環境當中，地區特徵指紋比對法明顯擁有較優於幾何三角定位法的準確度，但是此方法必須建立一個十分龐大的比對資料庫(dataset)，並且會面臨到運算複雜度過高的問題。一般說來，幾何三角定位法不適用應用於室內環境，原因在於室內環境太過複雜以及這項方法並無法收集到足夠有關於目標物的位置資訊(一般這些資訊是指著目標物所傳送過來的訊號強度)。
本篇論文當中，我們提出了一個適用於無線感測網路的定位技術，其設計上的考量是基於一般常用的室內通道模型(indoor channel model)。在我們提出的這項技術當中，我們是將九個感測器每隔一段固定距離平均佈於一個方形區域當中，八的位於邊上而一個位於正中心。每一個感測器都會收到來自於目標物所傳送的訊號並知道所收到的訊號強度，之後基於我們所設計的方法，簡單的估計出目標物至每個感測器的距離，我們可以先利用外圍八個感測器所估出來的距離資訊，經由運算後得出六組估計座標，再移除兩組離均值(outlier)後，對剩餘之四組加以平均以減輕通道的衰減效應(effect of fading)並提升準確度。此外，我們以一種有效率的方式去使用位於中心位置的第九個感測器以及它所收集到的資訊，如此可以更進一步的增加定位的準確度。
地區指紋比對法在收到目標物之訊號強度後，會去和龐大的資料庫(database)做比對來得出估計座標，如此將十分耗費運算量；而我們所提出的定位方法只需利用上述簡單的運算程序，即可利用很低的運算量來完成定位。當我們使用之前設計方法所依據的通道模型時，於許多不同的模擬條件之下，我們所提出的方法都明顯有優於一般兩種定位方法的表現；甚至在一個考慮萊利衰減(Rayleigh fading)以及遮蔽效應(shadowing)的通道模型之下，我們提出之方法依然有令人滿意的表現。此外，當室內環境改變時，地區指紋比對法所建的資料庫資訊可能會失去可用性。如此表現出了我們這項利用感測網路的方法，是擁有最適合的條件於應用在各種不同的環境之下。

Recently, the proliferation of indoor location aware applications, such as healthcare, industrial guidance, and shopping mall management, has attracted enormous research interests in indoor positioning techniques for wireless sensor networks. Conventional positioning approaches include geometrical triangulation and location fingerprinting; for indoor applications, fingerprinting performs better than geometrical triangulation, but the former has to build up a huge dataset and involves much more computational complexity.
In this thesis, we propose a positioning method based on propagation modeling for indoor wireless sensor networks. We use nine sensor nodes uniformly deployed in a grid (eight on the edge and one at the center) to estimate the location of a target. Each sensor measures the power of the signal transmitted from the target, and estimates the distance based on a propagation model. We can obtain six estimated (x, y) coordinates of the target by measuring the distance between the target and each of the eight sensors on the edge of the grid. After this, we remove two outliers of the six x-axis values and the six y-axis values, respectively, and then average the remaining four components in each axis to form a location estimate of the target. Based on the ninth sensor at the center of the grid, we generate another location estimate of the target. By properly combining these two estimates, we can have a more accurate positioning result. As compared to the location fingerprinting method, the proposed positioning scheme can provide better performance in location determination and tracking with less computational complexity in general. Computer simulation results are given to demonstrate the effectiveness of the proposed approach.

[1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A survey on sensor networks,” IEEE Commun. Mag., vol. 40, pp. 102 – 114, Aug. 2002.
[2] A. OGAWA, T. YAMAZATO, and T. OHTSUKI, “Information and Signal Processing for Sensor Networks,” IEICE Trans. fundamentals, vol..E87-A, no.10, Oct. 2004.
[3] GPS tutorial, RoyalTek Company Ltd. Available:
http://www.royaltek.com/content/category/6/18/33/ .
[4] S. Y. Seidel and T. S. Rappaport, “914 MHz Path Loss Prediction Models for Indoor Wireless Communications in Multifloored Build,” IEEE Trans. Antennas Propagat vol. 40, pp. 207 – 217, Feb. 1992.
[5] S. Phaiboon, “An Empirically Based Path Loss Model for Indoor Wireless Channels in Laboratory Building,” in Proc. 2002 IEEE TENCON’02 (IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering) vol. 2, Oct. 2002, pp. 1020 – 1023.
[6] J. Kwon, B. Dundar, and P. Varaiya, “Hybrid Algorithm for Indoor Positioning Using Wireless LAN,” in Proc. 2004 IEEE 60th Veh. Technol. Conf. (VTC 2004), vol. 7, Los Angeles, CA, Sept. 2004, pp. 4625 – 4629.
[7] P. Prasithsangaree, P. Krishnamurthy, and P. Chrysanthis, “On indoor Position Location with Wireless LANs,” in Proc. 2002 IEEE Int. Symp. Personal Indoor and Mobile Radio Commun. (PIMRC’02), vol. 2, Lisbon, Portugal, Sept. 2002, pp. 720-724.
[8] Ekahau Positioning Engine, Ekahau Inc. Available:
http://www.ekahau.com/products/positioningengine/.
[9] G. L. , Principles of Mobile Communication. Norwell, MA: Kluwer, 1998.