不同移動速度下的效能比較

在這一小節中，我們要觀察改變 Vmax 會帶來怎樣的影響。而我們所取的平均 是在平均錯誤收斂後，也就是取 time slot 介於 11 到 20 之間的錯誤去做平均。

環境的設定:

anchor node 個數=28 ，normal node 個數=200，通訊半徑(R)=50 有合作式定位

(a)

(b)

圖 4.14 改變 Vmax 大小比較 MCL 、IMCL 、WMCL、RMCL 和 RMCL-W 的(a)平均錯 誤(b)取樣區陎積

從圖 4.14 接可以看出隨著 Vmax 變大，平均錯誤有變差的，其中還是以我們所

提出的方法有最好的改善。從圖中可以看出當 Vmax 超過 R 時，RMCL 跟 WMCL 有 縮小，原因如我們在 4.3.2 節中所提，ROC of previous node 會隨著 Vmax 靠近 R 而降低此限制。

我們進一步討論 Vmax 會使錯誤率提高的原因，有利用到 Vmax 的限制是屬於 previous constraints，其中包含了 previous of anchor node 、 previous self-assisted RIC 和 previous of cooperative normal node。

會使 previous s constraints 失效的原因是實際速度跟最大值 Vmax 有落差，

而 normal node 移動速度是在[0~Vmax]中 random 產生，我們假設以 Vmax/2 做為 界線，當移動距離小於 Vmax/2 表示移動距離太小，因為真實移動速度是 random 產生，會小於一半的機率是 1/2。所以不管 Vmax 大小，發生移動速度太小的機 率都是 1/2，所以機率是一樣的，一樣的機率成上不同的 Vmax 所得到的不同的 期望值，也就可以解釋當 Vmax 越大時，平均誤差位大。

我們又去檢查 Vmax 跟接收 nchor node 的關係，如 4.15 圖所示:

圖 4.15 改變 Vmax 計算 normal node 平均接收到的 anchor node

我們可以看出隨著 Vmax 的提升，每個 normal node 接收到的平均 anchor node 有下降，所以我們認為這是影響平均錯誤的原因之一。

第五章

結論及未來展望

在 range-free 的條件下，能夠使用的資訊有限，所以如何掌握更多目前跟過 去的資訊變成為我們的研究動機，從第四節電腦模擬中可以知道我們提出的方法 可以在合作式方法下提供更精確的定位。利用現在或是過去的 ROC 和 RIC 多設限 制，從 CDF 圖中我們可以發現合作式定位提供一個改善的空間，weighted sample 則是使其效果更好，而我們提出的方法則是提供了另外一個改善的空間，

weighted sample 一樣可以使定位效果更精進。從另一角度觀察，我們所提的方 法可以取代 weighted sample 的部分，如此一來便可以達到相同的定位效果，但 是降低了運算複雜度。

non-causal 的系統下，可利用的資訊又更多。因為 non-causal 這方陎的資訊是 以往沒有利用的，估計所帶來的效果不會和以往的限制有所衝突或是重複，所以 改善效果會更明顯，我們認為此種方法也可能成為往後的發展方向。

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