移動感測網路中基於灰色預測與模糊推論來進行傳輸功率控制之節能方法

Power-Saving Methods Using Grey Prediction and Fuzzy Reasoning to Transmission Power Control for Mobile Sensor Networks

李昀宸(Yun-Chen Lee)

指導教授 ： 李俊賢

國立臺北科技大學/電資學院/電機工程系研究所/碩士(2013年)

https://doi.org/10.6841/NTUT.2013.00136

摘要

移動式感測網路中，感測節點的能源消耗除了移動所耗費的能源外，其餘主要有三種模式:資料傳輸、接收以及閒置，其中就以資料傳輸消耗的能源最多，因此傳輸功率控制(transmission power control, TPC)的目的在於減少資料傳輸能耗進而增加感測節點使用的壽命。現有針對移動感測網路的TPC，必須事先離線建立路徑預測模型來進行即時調整傳輸功率，因而造成額外成本開銷以及能源損耗。
因此，本論文提出非離線預測方式來即時調整傳輸功率維持良好傳輸效能的TPC，透過基站(Base station, BS)將收到的傳輸功率、訊號強度值以及預測下一筆收到的訊號強度值，來做為模糊邏輯系統輸入，進而產生新的一筆傳輸功率，給予終端節點(End device, ED)動態調整傳輸功率指令。此方法有兩階段設定: 1) 初始階段BS透過不同大小的廣播等級，給予ED適當的傳輸功率設定，可以降低移動ED初始的傳輸能耗；2) 動態調整階段，由於ED具有移動性，為了降低傳輸能耗又能減少封包遺失，本文利用了灰色預測和模糊推論來產生新的一筆傳輸功率，經由灰色預測透過少量的數據，便能即時預測下一筆資料，非常適合不同環境的移動感測網路中。故本文提出之方法，不僅能夠即時動態調整傳輸功率，降低ED傳輸能耗，並能透過預測提升網路性能，因而延長整體網路之壽命。

關鍵字

移動感測網路 ； 傳輸功率控制 ； 灰色預測 ； 模糊推論 ； 節能

並列摘要

In mobile sensor networks, in addition to the energy consumed by the mobile, there 3 kinds of energy consumption of sensor nodes are: transmission, reception and idle. Among them, the maximum energy consumption is data transfer. Therefore, the purpose of the transmission power control (TPC) is to reduce the overhead data transmission, and increase the life of the sensor nodes. Existing sensor network for mobile transmission power control must be offline to create the path prediction model for real-time adjustment of the transmission power, and this approach leads to additional overhead costs and energy consumption.
This thesis proposes an on-line predictive approach to immediately adjust the transmission power and maintain good transmission performance of TPC. Input the values received by the base station (BS) -transmission power, the signal strength value and the prediction value of the next signal strength- into the fuzzy logic system. This produces a new transmission power value input into the end device (ED) to dynamically adjust the transmission power command. This paper has two stages of setting: 1) Initial stage, BS broadcasts through different size levels, and gives proper transmission power settings that reduce the transmission of the initial mobile ED energy. 2) Dynamic adjustment stage, since ED has mobility, this thesis makes use of the advantages of the gray prediction and fuzzy inference to produce a new transmission power such that the transmission energy and packet loss can be reduced. Grey is able to use a small amount of data to forecast in dynamic real-time. The result suits for different mobile sensor network environments. Therefore, the proposed method not only can dynamically adjust transmit power to reduce ED transmission energy consumption, but also can improve network performance by the estimating scheme. Our approach furthermore can prolong the life of the entire network.

並列關鍵字

Mobile Sensor Networks ； Transmission Power Control ； Grey Prediction ； Fuzzy Reasoning ； Power-Saving Methods

參考文獻

[10]Jin-Shyan Lee and Chun-Chieh Chuang, "Transmission power control method and system," U.S. Patent 8 218 475, July, 10, 2012.

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