可充電無線感測網路充電器佈置之改進粒子群最佳化;Improved Particle Swarm Optimization for Charger Deployment in Wireless Rechargeable Sensor Networks

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Title:	可充電無線感測網路充電器佈置之改進粒子群最佳化;Improved Particle Swarm Optimization for Charger Deployment in Wireless Rechargeable Sensor Networks
Authors:	林廷諭;Lin, Ting-Yu
Contributors:	資訊工程學系
Keywords:	可充電無線感測網路;粒子群最佳化演算法;永續性;無線充電器佈置;Wireless Rechargeable Sensor Network;Particle Swarm Optimization;Sustainability;Wireless Charger Deployment
Date:	2018-06-13
Issue Date:	2018-08-31 14:45:43 (UTC+8)
Publisher:	國立中央大學
Abstract:	在可充電無線感測網路(Wireless Rechargeable Sensor Networks, WRSNs)中，透過無線充電技術以無線充電器(Wireless Charger)對網路中的感測節點進行電力補充，使得所有感測節點可以持續運作，讓WRSN具有永續性(sustainability)。由於無線充電器價格昂貴，因此有許多演算法嘗試以較少的充電器覆蓋所有感測節點以達成WRSN的永續性，包括執行時間較長但是效能較佳使用粒子群最佳化(Particle Swarm Optimization, PSO)概念的PSCD(Particle Swarm Charger Deployment)演算法，及執行時間較短但是性能較差使用貪婪解題策略的NB-GCS(Node Based Greedy Cone Selection)及PB-GCS(Pair Based Greedy Cone Selection)演算法。本論文改進PSCD演算法，提出IPSCD(Improved Particle Swarm Charger Deployment)與LPSCD(Lexicographical Particle Swarm Charger Deployment)粒子群最佳化演算法進行WRSN充電器佈置最佳化。PSCD演算法透過執行固定數目的迭代，在粒子群中找出一個一個最佳充電器佈置位置；但是此時充電器的指向性天線(directional antenna)方向對該最佳位置而言卻不是最佳的。另外，當感測器數量少時，所有粒子群的位置可能都無法針對感測器有效充電，因此找不到最佳的充電器佈置位置。IPSCD演算法改進PSCD演算法，在每次找到最佳充電器佈置位置時，再以PSO概念找出充電效益最高的指向性天線方向。LPSCD演算法也是改進PSCD演算法，當搜尋過程中有找不到任何具充電效益的充電器佈置位置時，LPSCD演算法改為以最靠近感測器之粒子位置作為最佳充電器佈置位置，如此可加速找到具有實際充電效益的最佳充電器佈置位置。我們進行模擬實驗以比較IPSCD演算法、LPSCD演算法、PSCD演算法、PB-GCS演算法與NB-GCS演算法的效能，實驗結果顯示IPSCD與LPSCD都能以較少的充電器達到所有感測器的充電需求，使得WRSN具有永續性。;In Wireless Rechargeable Sensor Networks (WRSNs), wireless chargers can recharge batteries of sensor nodes so that they can operate sustainably to provide WRSNs with the property of sustainability. Since wireless chargers are costly, many algorithms try to deploy as few as possible chargers to cover all sensor nodes and fulfill their charging demands for making WRSNs sustainable. Typical algorithms include the PSCD (Particle Swarm Charger Deployment) algorithm and two greedy algorithms, namely, the NB-GCS (Node Based Greedy Cone Selection) and PB-GCS (Pair Based Greedy Cone Selection) algorithms. The first algorithm is based on the Particle Swarm Optimization (PSO) concept and has better performance than the other two algorithms. This study tries to improve the PSCD algorithm, and proposes the IPSCD (Improved Particle Swarm Charger Deployment) algorithm and LPSCD (Lexicographical Particle Swarm Charger Deployment) algorithm for optimizing WRSN charger deployment. The PSCD algorithm deploys chargers by running a specific number of iterations to find the best charger position one by one. However, when the best charger position is found, its directional antenna direction is not optimal with respect to the position. The IPSCD algorithm improves the PSCD algorithm by re-applying the PSO concept to find the antenna direction with the optimal charging efficiency. For some cases, the PSCD algorithm cannot find any charger position to charge any sensor with effective charging efficiency. Encountering such cases, the LPSCD algorithm tries to find the position that is nearest to some sensor, instead of the positon with the maximal charging efficiency, to deploy a charger. This can accelerate the finding of positions with good and effective charging efficiency. We perform simulation experiments to compare the performance of the IPSCD, LPSCD, PSCD, PB-GCS, and NB-GCS algorithms. The simulation results show that the IPSCD and LPSCD algorithms indeed outperform the other three algorithms in sense that they use fewer chargers to fulfill the charging requirements of all sensor nodes to make WRSNs sustainable.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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