粒子族群最優化演算法應用於無線感測器網路

Applications of Particle Swarm Optimization Algorithms for Wireless Sensor Networks

陳萬仁(Wan-Ren Chen)

指導教授 ： 陳永隆

國立臺中科技大學/資訊與流通學院/資訊工程系碩士班/碩士(2012年)

https://doi.org/10.6826/NUTC.2012.00041

摘要

無線感測器網路的主要特性是能量有限且無法補充，因此，提高能量效率之演算法設計為一重要主題。在本文中，我們提出兩個能量效率(energy efficiency)的演算法，第一種演算法為PSO-LEACH with Taguchi以Low-Energy Adaptive Clustering Hierarchy (LEACH)結合Particle Swarm Optimization (PSO)演算法和Taguchi方法來選擇最佳叢集頭在無線叢集感測網路中。在無線感測器網路上使用PSO演算法來挑選叢集頭，達到所有節點之能量消耗平衡，減少節點提早死亡的機率。由於PSO演算法中參數的選擇非常重要，為了達成最佳的參數值，會花費太多實驗次數，故本論文採用Taguchi方法來減少實驗的次數及節點之能量消耗，求得PSO演算法的最佳參數值，藉此平衡每一節點之能量消耗。模擬結果顯示，我們提出的PSO-LEACH with Taguchi演算法比LEACH架構和PSO-LEACH演算法，減少節點能量消耗且達到每一個節點之能量消耗平衡進而延長整個無線感測器網路的生命週期。進一步我們提出Hierarchical Clustering of Energy Efficiency with Particle Swarm Optimization (HCEE-PSO)演算法為三階層的叢集架構，在劃分叢集與子叢集階段我們提出新的Total Routing Cost ( )，所定義的 可以平衡無線感測器節點的能量、子叢集頭的能量以及叢集頭的能量。在子叢集頭階段我們使用PSO演算法去尋找合適的子叢集頭，此定義的PSO演算法的cost function保障子叢集頭與叢集頭之間的距離不會太近且子叢集頭的能量趨近於叢集頭能量的特性，將整個無線感測器網路劃分成三個階段的傳輸，藉此減少叢集成員的能量消耗並且分散叢集頭的能量消耗。模擬結果證明，我們提出的HCEE-PSO演算法比LEACH、EECS和MOECS演算法更能減少無線感測器節點的能量消耗，達到整體無線感測器網路的壽命延長。

關鍵字

叢集頭 ； 子叢集頭 ； 田口 ； 粒子族群最佳化

並列摘要

The main features of wireless sensors are energy limited, hence, the algorithm design with the enhancement of energy efficiency becomes an important topic. In this thesis, we propose two energy efficiency algorithms. One is PSO-LEACH algorithm with Taguchi algorithm which associates LEACH and PSO to select the optimal cluster heads from a wireless cluster sensor network. In the WSNs, PSO algorithm is used to select cluster head and reach the energy dissipation balance among all nodes, which can reduce the probability of the early death of the nodes. In PSO algorithm, the selection of parameter is very important, hence, in order to achieve optimal parameter value, too much number of experiments is usually spent, therefore, in this thesis, Taguchi method was adopted to reduce the number of experiments and the energy dissipation of the node. Consequently, we can get the optimal parameter value of PSO algorithm, and the energy dissipation of each node can then be balanced. From the simulation result, it can be seen that our proposed PSO-LEACH architecture with Taguchi algorithm has reduced energy dissipation as compared to LEACH architecture and PSO-LEACH architecture. Our second energy efficiency algorithm is called Hierarchical Clustering of Energy Efficiency with Particle Swarm Optimization (HCEE-PSO) algorithm for three-layer cluster architecture, divided into cluster and sub-cluster phases. We propose the Total Routing Cost ( ) to balance the energy of the sensor node, the sub-cluster head and the cluster head. In the sub-cluster head selection phase, we use the PSO algorithm to search for the suitable sub-cluster head. The cost function of the PSO algorithm ensures that the distance between the sub-cluster head and cluster head is not too close and that the amount of sub-cluster head energy is close to the cluster head energy. The entire WSNs is divided into three phases of transmission to reduce the energy consumption of the cluster members and disperse the energy consumption of the cluster head. Simulation results show that our proposed HCEE-PSO algorithm reduces the energy consumption of WSNs and so extends their lifetime more than do LEACH architecture, the EECS algorithm or the MOECS algorithm.

並列關鍵字

Cluster Head ； Sub Cluster Head ； Particle Swarm Optimization ； Taguchi

參考文獻

[1] I. F. Akyildiz, and I. H. Kasimoglu, “Wireless Sensor and Actor Networks: Research Challenges,” Ad Hoc Network, vol. 2, issue 4, pp. 351-367, October 2004.

[3] P. Bonnet, J. Gehrke, and P. Seshadri, “Querying the Physical World,” IEEE Personal Communications, vol. 7, issue 5, pp. 10-15, October 2000.

[4] J. Burrell, T. Brooke, and R. Beckwith, “Vineyard Computing: Sensor Networks in Agricultural Production,” IEEE Pervasive Computing, vol. 3, issue 1, pp. 38-45, March 2004.

[7] D. Estrin, D. Culler, K. Pister, and G. Sukhatme, “Connecting the Physical World with Pervasive Networks,” IEEE Pervasive Computing, vol. 1, issue 1, pp. 59-69, January-March 2002.

[10] C. H. Lung, and C. Zhou, “Using Hierarchical Agglomerative Clustering in Wireless Sensor Networks: An Energy-Efficient and Flexible Approach,” Ad Hoc Networks, vol. 8, issue 3, pp. 328-344, May 2010.