目前無線感測網路定位法已發展成兩大類：range-free與range-based定位演算法。range-free為不需要任何測距技術而使用參考節點間的連結度來估算為數眾多的未知節點位置。Range-based定位則需使用一些測距技術，如TOA、TDOA、AOA和RSSI等當作測量鄰居節點距離的工具，並藉由這些測量資訊計算未知節點位置。為了增加錨節點的覆蓋率問題,一些基於測距為基礎的無線感測網路定位法使用疊代式的多邊測量可以解決錨節點不足的影響，但是卻有兩個缺點,一是某些節點仍然存在錨節點鄰居節點不足的情形,其二會有誤差累積的影響等缺點。因此，在本文中我們提出基於PSO最佳化演算法使用未知節點的最接近鄰居測距來估算未知節點位置，藉由這個方法減緩誤差累積的影響以及增加孤立節點成功定位的機會。由電腦模擬的結果中，新的定位法相較其他定位方法對於不同環境因素影響下也能有效定位。

Localization in wireless sensor networks has developed two categories: range-free and range-based localization algorithms. The range-free algorithms don't need any range techniques but use connectivity among the anchors to estimate the positions of unknown nodes. The range-based algorithms must need some range techniques such as TOA, TDOA, AOA and RSSI to measure the neighbors' distance. And use these measurements to estimate the position of the unknown nodes. In order to add the coverage of anchor nodes, some range-based localization algorithms use iterative multilateration to solve low density problem of the anchor nodes. But the iterative multilateration algorithm suffers two drawbacks: first, some nodes still don't have sufficient anchor nodes in their neighborhood; second, the use of localized unknown nodes as anchor nodes can bring the cumulative error. Therefore, we propose a PSO based localization algorithm using the distance of the closest neighbor to estimate the unknown node's location. We use this algorithm to reduce the error accumulation effects and add the probability of the orphan nodes which can successfully calculate the locations. Compared with some localization algorithms, new method can be more effective performance for different environment in our computer simulations.

目   錄

中文摘要	I
目   錄	V
圖 目 錄	VIII
表 目 錄	X
第一章 緒論	1
1.1 前言	1
1.2 章節架構	4
第二章 相關研究背景	5
2.1 測距技術	5
2.1.1 TOA和TDOA	5
2.1.2 AOA	6
2.1.3 RSSI	7
2.2 無線電波信號能量與路徑衰退模型	7
2.3 相關研究文獻	10
2.3.1 RADAR	10
2.3.2 Ecolocation	11
2.3.3 AHLoS	18
2.3.4 n-hop multilateration	21
2.3.5 Generic localized algorithm	22
2.3.6 DV-distance	22
第三章	PSO最佳化演算法	25
3.1 PSO簡介	25
3.2 PSO執行過程	27
第四章	新定位演算法之架構	29
4.1 研究動機與構想	29
4.2 新方法的運算步驟	31
4.3 使用PSO精練	35
第五章	效能評估	41
5.1 模擬參數	42
5.2 模擬結果與評估	43
5.2.1 RSSI測量次數多寡與誤差的比較	43
5.2.2 網路總節點數量與定位誤差評估	45
5.2.3 錨節點比率與定位誤差之關係	47
5.2.4 雜訊標準差σ的變化與定位誤差評估	49
5.2.5 孤立節點可成功定位的節點數比率評估	51
第六章	結論	55
參考文獻	57


圖 目 錄

圖 2.1：TDOA測距示意圖	6
圖 2.2：Ecolocation示意圖一	13
圖 2.3：Ecolocation示意圖二	14
圖 2.4：Ecolocation示意圖三	15
圖 2.5：Ecolocation流程圖	17
圖 2.6：Atomic示意圖	20
圖 2.7：iterative multilateration示意圖	20
圖 2.8：n-hop multilateration示意圖	21
圖 2.9：DV-distance示意圖	24
圖 3.1：PSO演算法流程	26
圖 4.1：理想訊號強度衰減示意圖	30
圖 4.2：MODE 2示意圖	34
圖 4.3：新定位法流程示意圖	38
圖 4.4：最接近鄰居搜尋位置示意圖	40
圖 5.1：RSSI數據取樣次數與平均定位誤差之關係	44
圖 5.2：網路總節點數量與定位誤差之關係	46
圖 5.3：錨節點比率與定位誤差	48
圖 5.4：雜訊標準差變化與定位誤差	50
圖 5.5：節點成功定位比率與定位誤差關係圖	53
圖 5.6：利用疊代所產生之誤差曲線圖	54


表 目 錄

表 5.1：實驗模擬參數	42

【1】	D. Niculescu and B. Nath, “DV based positioning in ad hoc networks,” Jour. of Telecommunication Systems, 2003   pp.267-280.
【2】	P. Bahl and V. N. Padmanabhan, “Radar: An in-building RF-based user location and tracking system,” Proc. of the IEEE Infocom 2000, pp. 775-784
【3】	K. Yedavalli, B. Krishnamachari, S. Ravula and B. Srinivasan, "Ecolocation: a sequence based technique for RF localization in wireless sensor networks," Proc. of the Information Processing in Sensor Networks, 2005, pp. 285-292.
【4】	A. Savvides, C-C. Han and Mani B. Strivastava, “Dynamic fine-grained localization in ad-hoc networks of sensors,” Proc. of the 7th Annual Int'l Conf. on Mobile Computing and Networking, 2001, pp. 166-179.
【5】	A. Avvides, H. Park and Mani B. Strivastava, “The n-hop multilateration primitive for node localization problems,” Proc. of the 1st ACM Int’l Workshop on Wireless Sensor Networks and Applications, 2002, pp.112-121
【6】	S. Meguerdichian, S. Slijepcevic, V. Karayan and M. Potkonjak, “Localized algorithms in wireless ad-hoc networks: Location discovery and sensor exposure,” Proc. of the 2nd ACM Int’l Symp. on Mobile Ad Hoc Networking & Computing, 2001, pp. 106-116.
【7】	J. Kennedy and R. C. Eberhart, “Particle Swarm Optimization,” Proc. of IEEE International Conference on Neural Networks, Vol. IV , 1995, pp.1942-1948
【8】	J. Kennedy and R. C. Eberhart, “A New Optimizer Using Particle Swarm Theory,” Proc. of the 6th Int’l Symp. on Micro Machine and Human Science, 1995, pp.39-43
【9】	Y. Shi and R.C. Eberhart, “A Modified Particle Swarm Optimizer,” Proc. of the IEEE Int’l Conf. on Evolutionary Computation, 1998, pp.69-73
【10】	Y. Shi and R. C. Eberhart, “Parameter Selection in Particle Swarm Optimization,” Proc. of the 1998 Annual Conf. on Evolutionary Programming,  1998
【11】	K. Whitehouse, C. Karlof and E. David, “A practical evaluation of radio signal strength for ranging-based localization.” ACM Culler Mobile Computing and Communications Review Vol 11 Issue 1, Jan. 2007, pp.41-52