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研究生:呂亮宜
研究生(外文):Lu, Liang-Yi
論文名稱:物聯網系統之高目標感測器選擇之研究
論文名稱(外文):Many-Objective Sensor Selection in Internet of Things Systems
指導教授:林春成林春成引用關係
指導教授(外文):Lin, Chun-Cheng
口試委員:林文杰楊大和黃世強王昱舜
口試委員(外文):Lin, Wen-ChiehYang, Ta-HoWong, Sai-KeungWang, Yu-Shuen
口試日期:2018-06-21
學位類別:碩士
校院名稱:國立交通大學
系所名稱:工業工程與管理系所
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:中文
論文頁數:33
中文關鍵詞:物聯網感測器選擇許多目標最佳化能量效率能量平衡
外文關鍵詞:Internet of thingssensor selectionmany-objective optimizationenergy efficiencyenergy balancing
相關次數:
  • 被引用被引用:2
  • 點閱點閱:307
  • 評分評分:
  • 下載下載:69
  • 收藏至我的研究室書目清單書目收藏:1
物聯網(Internet of Things,IoT)將生活中的物件透過具有許多功能的感測器設備連結在一起,在配置物聯網系統的計畫階段,我們會考慮到物聯網系統中的感測器選擇,其分派預定義的物聯網服務到許多感測器設備上,並在能量與距離限制下最佳化一個或多個目標。感測器選擇問題最佳化一通用函數在其他應用中已被證實是個NP-hard問題,且物聯網服務的數量在實務上被認為是相當龐大的。因此,應用進化演算法(Evolutionary Algorithm,EA)來解決大規模的多目標問題是合適的。在實務上的問題中常常會牽涉到許多個相互衝突的不同目標,近年來,在許多目標最佳化問題(Many-Objective Optimization Problem,MaOPs)的研究上有比較大的突破,不同於以往的多目標最佳化問題(Multi-Objective Problem,MOPs)的研究多只考慮到少數的目標(通常為兩個或三個目標),而是傾向於處理四個以上的目標。因此,本文將把更多種目標同時地列入考慮,包含傳輸能量消耗之最佳化、所有設備上的能量平衡、能量擷取(Energy Harvesting,EH)、綠色環保和服務品質(Quality of Service,QoS),以更貼切實務情況。本文中,使用基於分解的多目標進化演算法(Many-Objective Evolutionary Algorithm Based on Decomposition,MOEA/D)來求解多目標的問題,將問題分解成許多個子問題,且只利用與它相鄰的子問題資料來同時地進行優化,此方法具有較低的計算複雜度,比過去的多目標解法更穩定,解空間的變異也較小。我們將設計一個模擬實驗環境,並使用平行座標表示法與散布圖來分析多種目標在不同應用上的價值。
The Internet of Things connects physical objects through sensor devices with multiple functionalities. At the planning stage of deploying an IoT system, we are concerned about sensor selection in the IoT system, which allocates predefined IoT services to multiple sensor devices so as to optimize one or more objectives associated with these allocations, under energy and distance constraints. The sensor selection problem that optimizes a utility function in other applications has been shown to be NP-hard, and the number of IoT services concerned is enormous in practice. Hence, it is suitable to apply evolutionary algorithms (EA) for solving the large-scale problem with multiple objec-tives. Recently, the paradigm of multiple-objective EAs (which often address only two or three objectives) has advanced to many-objective EAs (which are intended to address four or more objectives that may be in conflict with each other in many cases). There-fore, this article considers many objectives of the sensor selection problem in the IoT system, including optimization of communication energy consumption, energy balanc-ing on all devices, energy harvesting, green concerns, and QoS. The problem is re-solved by a tailored many-objective EA based on decomposition to increase computa-tional efficiency and solution quality. By simulation, the proposed EA is shown to be promising through scatter charts and parallel coordinates.
摘 要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 v
表目錄 v
第一章 緒論 1
第二章 文獻回顧 6
2.1 感測器選擇問題 6
2.2 許多目標最佳化問題 8
第三章 問題描述 11
3.1 問題定義 11
3.2 目標定義 13
3.2.1 最小化總傳輸能量消耗 13
3.2.2 平衡感測器設備之間的能量 14
3.2.3 最大化服務品質 14
3.2.4 最大化總能量擷取 15
3.2.5 最佳化綠色環保指數/最小化總污染水準 16
第四章 研究方法 18
第五章 實驗設計與分析 21
第六章 結論 31
參考文獻 32
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