在先進讀表基礎建設網路下，會有佈建有智慧電表以及集中器。在電表與電表
之間，或是電表與集中器之間，智慧電表能透過無線的方式傳送資料。這些資料最
後將傳送到集中器上，最後上傳回電力公司。用以協助電源管理、電費管理。與過
往人工讀表不同，可以節省大量人力成本，並且可以及時知道用電資訊。使得智慧
電表比起傳統電表更顯得有優勢。
由於電表與電表之間可以互相傳送資料，因此會形成一個多跳網路。電表所產
生的資料需要傳回到集中器，在透過集中器將資料送回電力公司。因此需要決定電
表應該要被分配到哪個集中器。此外佈建集中器時需要相當的成本，我們會希望能
夠佈建越少的集中器越好，並且同時也能夠服務到所有電表。集中器佈建的位置，
以及電表該被分配連到哪個集中器均會影響到最後的結果。所以位置的選擇以及電
表所選擇連線的集中器便是相當重要的問題。
在這篇論文中，我們探討集中器應該佈建的位置以及電表所連線的集中器，以
達成能夠使用較少量的集中器便可以服務所有電表。我們提出一個基於線性規劃的
演算法。起初我們會為每個集中器找出適當連線的電表集合，並且由這些電表到該
集中器的路徑形成一顆以集中器為根結的樹。透過這些樹利用線性規劃找出佈建集
中器的位置參數，以及電表與各集中器的關係參數。最後使用這些參數找出適當的
解。在實驗中，我們的方法在大部分不同的環境以及參數設定下，能夠使用想對較
少的集中器就能夠服務電表。

Under the advanced meter reading infrastructure network, smart meters and concentrators will be built. Different from traditional manual reading, it can save a lot of human resource and can know the power consumption information in time. Make smart meters have advantages over traditional meters.
Since the meter and the meter can transmit data to each other, a multi-hop network is formed. The data generated by the meter needs to be passed back to the concentrator and sent back to the utility provider end through the concentrator. Therefore, it is necessary to decide which concentrator the meter should be assigned to. While building a concentrator requires considerable cost, we would like to be able to build fewer concentrators as much as possible and at the same time be able to serve all of the meters. The location of the concentrator deployment and which concentrator the meter is assigned to will affect the final result. Therefore, the deployment of concentrators and the assignment of meters are very important issues.
In this paper, we propose an algorithm based on linear programming. Initially, we will find a suitable meter set that it can serve for each concentrator. The paths from these meters to the concentrator form a tree rooted at the concentrator. Through these trees, the linear programming is used to find out the location parameters of the deployment of concentrator and the relationship parameters between the smart meter and each concentrator. Finally use these parameters to find the appropriate solution.
In the experiment, our algorithm can use less concentrators to service meters in most different environments and settings.

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