臺灣博碩士論文加值系統

物聯網(Internet of Thing，IoT)可以提供人們日常生活中許多便捷的服務，而無線感測器網路(Wireless Sensor Network，WSN)是物聯網發展的關鍵技術之一，但是無線感測器具有一些限制和挑戰，例如有限的電力、計算能力、儲存空間以及網路頻寬，尤其是有限的電力，無論是感測環境透過無線電訊號彼此之間進行通信和共享資料皆會造成電力的消耗，加上一般無線感測器常被安裝在人們無法到達的區域進行環境監測，若無線感測器能量耗盡是無法對其提供電力支援，只能遺棄這些感測器卻會導致環境災害，因此要如何找到一種方法能根據網路中每個感測器的狀態進行更靈活、更快速的程序來延長無線感測器網路的壽命成為一個重要的問題。而軟體定義網路(Software-Defined Network，SDN)將控制功能與硬體設備分開，使硬體設備僅具有封包轉發的功能，透過控制軟體根據網路狀態動態控制網路和資料封包的傳輸狀態和應用程序要求，為了提供靈活性和適應性，軟體定義網路需要一種動態方法來解決和優化路由規劃的問題。
本研究提出基於多準則之人工蜂群演算法 (Multiple-criteria Artificial Bee Colony，MABC)來優化無線感測器網路中能耗的問題，該方法中首先使用人工蜂群演算法(Artificial Bee Colony，ABC)尋找網路中數個感測器節點作為群頭組合，其中將根據當前網路中節點狀態動態選擇不同數量的節點，選擇群頭的目的是先為網路中的感測器節點進行分群，如此其他節點成員將封包傳遞給隸屬的群頭即可，接著再為群頭們尋找最佳的節能封包傳輸路徑至IoT基台，其中透過多準則決策分析方法評估路徑中多個屬性包括剩餘能量、能量消耗、路徑距離、跳躍數以及頻率做出決策評分來幫助演算法能找到更佳的解，本研究於實驗中架設以軟體定義網路為架構的無線感測器網路，基礎架構層中感測器節點無需自行計算封包傳輸路徑而是由軟體控制層進行運算，因此於網路運作中隨時將所有感測節點的資訊儲存於資料庫中，接著依據節點們的資訊執行MABC演算法計算出的最佳的節能封包傳輸路徑並通知網路中各節點，本研究進行兩種實驗模式來評估方法的可行性，其一透過單次運算群頭們最佳封包傳輸路徑比較基本的人工蜂群演算法與粒子群演算法、貪婪演算法於第一個節點能耗耗盡時網路運作回合數與平均能耗來分析方法的優劣，其二為根據網路中的狀態進行動態的最佳封包傳輸路由運算，比較基本人工蜂群演算法、動態群頭模式之人工蜂群演算法、MABC與動態群頭模式之MABC的四種方法並以三種準則網路壽命週期、平均能耗與存活節點進行評估。
從實驗結果中可以證實本研究所提出的MABC方法可較基本的人工蜂群演算法提供較為節能的封包傳遞路徑並且能使無線感測器網路較慢才死亡，也證實透過多準則分析對候選路徑多方面的評估是能為演算法提供更佳的運算方向，在實驗過程中也發現加上動態群頭策略可以更延長節點們的使用壽命，而本研究的貢獻為運用群體智慧演算法於無線感測器網路的路由運算，並提出能運用於無線感測器網路中基於多準則的人工蜂群演算法來改善演算法快速收斂的問題，以及將軟體定義網路結合無線感測器網路實驗，透過軟體定義網路的特性動態調整網路中路由策略，最後使用NS3網路模擬器模擬無線感測器網路的節點封包的運作。
本研究遇到的挑戰為雖然於實驗中可以證實所提出的方法可以用更節能方式進行封包傳輸與延長網路壽命，但對於網路中封包延遲與遞送率並沒有討論到，以及對於網路大小與IoT基地台放置的位置差異是否會影響到結果，因此未來可以再做進一步的探究，或是將多準則決策分析結合其他演算法並針對無線網路中的其他特性對路徑進行更多方面的評估。

Internet of Thing (IoT) can provide many convenient services in people’s daily lives, and Wireless Sensor Network (WSN) is one of the key technologies for the development of Internet of Things, but wireless sensors have Some limitations and challenges, such as limited power, computing power, storage space, and network bandwidth, especially limited power, whether the sensing environment communicates with each other through radio signals and shares data will cause power consumption. In addition, general wireless sensors are often installed in areas inaccessible to people for environmental monitoring. If the energy of the wireless sensors is exhausted, they will not be able to provide power support. Only abandoning these sensors will cause environmental disasters. Therefore, How to find a way to extend the life of the wireless sensor network by performing more flexible and faster procedures based on the status of each sensor in the network has become an important issue. Software-Defined Network (SDN) separates the control function from the hardware device, so that the hardware device only has the function of packet forwarding, and dynamically controls the transmission status of the network and data packets according to the network status through the control software And application requirements, in order to provide flexibility and adaptability, software-defined networks need a dynamic method to solve and optimize routing planning problems.
This research proposes multiple-criteria Artificial Bee Colony (MABC) algorithm to optimize the energy consumption of wireless sensor networks. The method first uses the Artificial Bee Colony algorithm (ABC) Find several sensor nodes in the network as the cluster head combination, which will dynamically select different numbers of nodes according to the current node status in the network. The purpose of selecting the cluster head is to first be the sensor node in the network Perform grouping, so that other node members can pass the packets to the group heads they belong to, and then find the best energy-saving packet transmission path for the group heads to the IoT base station, in which multiple attributes in the path are evaluated through a multi-criteria decision analysis method Including remaining energy, energy consumption, path distance, number of hops, and frequency to make a decision score to help the algorithm find a better solution. In this study, a wireless sensor network based on a software-defined network was set up in the experiment. The sensor nodes in the infrastructure layer do not need to calculate the packet transmission path by themselves, but are calculated by the software control layer. Therefore, the information of all the sensor nodes is stored in the database at any time during the network operation, and then executed based on the information of the nodes The MABC algorithm calculates the best energy-saving packet transmission path and informs each node in the network. This study conducts two experimental modes to evaluate the feasibility of the method. One is to compare the best packet transmission paths between all of cluster heads. We analysis the artificial bee colony algorithm, particle swarm algorithm and greedy algorithm by the number of network operation rounds and average energy consumption when the first node die.The second is based on the state of the network Perform dynamic optimal packet transmission routing calculations, compare the four methods of artificial bee colony algorithm, artificial bee colony algorithm in dynamic cluster heads, MABC and MABC in dynamic cluster heads mode, and use three criteria for network life round, the average energy consumption and alive nodes.
From the experimental results, it can be confirmed that the MABC method proposed in this research can provide a more energy-efficient packet transmission path with a more artificial bee colony algorithm and make the wireless sensor network death slower. It also proves that the multi-criteria analysis is correct The multi-faceted evaluation of candidate paths can provide a better computing direction for the algorithm. In the experiment process, it was also found that adding a dynamic cluster heads can extend the life of nodes. The contribution of this research is the use of group intelligence algorithm It is used in the routing calculation of wireless sensor networks, and proposes a multi-criteria-based artificial bee colony algorithm that can be used in wireless sensor networks to improve the problem of rapid algorithm convergence, and combines software-defined networks with wireless sensors. In the sensor network experiment, the routing strategy in the network is dynamically adjusted through the characteristics of the software-defined network, and finally the NS3 network simulator is used to simulate the operation of the node packet of the wireless sensor network.
The challenge encountered in this study is that although experiments can prove that the proposed method can use a more energy-efficient way to transmit packets and extend the life of the network, but the packets delay and packets delivery rate in the network are not discussed. Whether the difference between the network size and the location of the IoT base station will affect the results, so we can do in further research , or combine multi-criteria decision analysis with other algorithms and make more aspects of the path for other characteristics in the wireless network.

中文摘要 i
英文摘要 iii
誌謝 v
目錄 vii
表目錄 viii
圖目錄 ix
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 1
1.3 研究目的 3
1.4 研究方法 4
第二章 文獻探討 6
2.1 物聯網發展與趨勢（Internet of Thing，IoT） 6
2.2 無線感測器網路(Wireless Sensor Network，WSN） 7
2.3 人工蜂群演算法(Artificial Bee Colony algorithm，ABC） 9
2.4 多準則決策分析（Multiple-criteria decision analysis，MCDA） 13
2.5 粒子群演算法(Particle Swarm Optimization，PSO） 14
2.6 軟體定義網路(Software Define Network，SDN） 17
第三章 研究架構 20
3.1 情境定義 20
3.2 實驗情境需求 21
3.3 實驗架構 21
第四章 各模組詳細功能說明 23
4.1 基礎架構模組 23
4.2 MABC模組 24
4.2.1 感測節點分群 25
4.2.2 封包路徑規劃 31
4.3 SDN模組 46
4.4 應用模組 47
第五章 實驗設計與分析 48
5.1 實驗平台 48
5.2 系統實驗架構設計 49
5.2.1 無線網路拓撲定義 49
5.2.2 模擬模型開發 50
5.2.3 運行模擬程式 56
5.3 實驗設計 57
5.3.1 實驗流程 57
5.3.2 實驗參數介紹 60
第六章 討論與建議 63
6.1 實驗結果討論 63
6.1.1 實驗模式(一)結果討論 64
6.1.2 實驗模式(二)結果討論 65
6.2 演算法複雜度分析 74
第七章 結論與未來展望 75
參考文獻 76

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