| Summary: | 在無線感測器網路中，由於感測器電池的不可替換性，有效的能源管理是一項重要的研究議題。既然通訊及偵測都會消耗感測器的能量，減少多餘偵測範圍的重疊，及降低重覆資料(duplicate data)的影響，可有效節省能量，延長網路生命週期。於本研究中，我們提出VERA (Voronoi dEtection Range Adjustment)，利用分散式Voronoi diagram演算法劃分各感測器負責監控的區域，並利用基因演算法計算每個感測器最合適的偵測範圍以節省能量，延長網路生命週期。此外，我們亦考慮偵測能力的限制，在減少感測器偵測範圍重疊的同時，也避免某些區域的偵測能力低於門檻值。在實驗模擬的部份，我們利用模擬系統驗證所提出的方法是否能有效降低各感測器偵測範圍的重疊性，並因偵測範圍降低而導致duplicate data的減少和整個感測器網路總能量耗損的減少。末了，也將驗證本方法是否能延長無線感測器網路的生命週期和達到滿足偵測機率的最低保證。 === In the wireless sensor networks, the batteries are not replaceable, efficient power management thus becomes an important research issue. Since both communication and detection consume energy, if we can largely decrease the overlaps among detection ranges and reduce the duplicate data then we can save the energy effectively. This will thus prolong the network lifetime. In this research, we propose a Voronoi dEtection Range Adjustment (VERA) method that utilizes distributed Voronoi diagram to delimit the responsible area for each sensor, and utilize Genetic Algorithm to compute the most suitable detection range for each sensor. As we try to decrease the detection ranges, we still guarantee to meet the lower bound of the sensor detection probability.
Simulations showed that our method can decrease the redundant overlaps among detection ranges, minimize energy consumption, and prolong the lifetime of the whole network effectively.
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