一种QACO-LEACH无线传感器网络分簇路由算法
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摘要
在研究经典低功耗自适应集簇分层型协议(Low Energy Adaptive Clustering Hierarchy,LEACH)和基于蚁群的分簇路由算法的基础上,提出了一种基于量子蚁群的无线传感器网络分簇路由算法.该算法采用在簇间路由阶段引入量子蚁群算法的方式,利用量子蚁群算法在全局寻优和收敛速度方面的优势,更好的搜寻到各个簇头到Sink节点的最佳路径进行数据的传输,从而有效的降低了簇头节点的能耗.仿真结果表明,该算法与LEACH算法和蚁群优化分簇路由算法相比,有效的找出最佳路由路径,从而节约了网络能耗,延长了网络生命周期.
Based on the research of Low Energy Adaptive Clustering Hierarchy(LEACH) and Ant Colony-based clustering routing algorithm,a wireless sensor network clustering routing algorithm based on the Quantum Ant Colony algorithm is proposed. The algorithm adopts the method of introducing the Quantum Ant Colony algorithm in the inter-cluster routing stage,and utilizes the advantages of the quantum ant colony algorithm in global optimization and convergence speed,and better searches for the best path from each cluster head to the sink node. Transmission,which effectively reduces the energy consumption of the cluster head node. The simulation results show that compared with LEACH algorithm and ant colony optimization clustering routing algorithm,the algorithm can effectively find the optimal routing path,which saves network energy consumption and make the network life cycle longer.
Based on the research of Low Energy Adaptive Clustering Hierarchy(LEACH) and Ant Colony-based clustering routing algorithm,a wireless sensor network clustering routing algorithm based on the Quantum Ant Colony algorithm is proposed. The algorithm adopts the method of introducing the Quantum Ant Colony algorithm in the inter-cluster routing stage,and utilizes the advantages of the quantum ant colony algorithm in global optimization and convergence speed,and better searches for the best path from each cluster head to the sink node. Transmission,which effectively reduces the energy consumption of the cluster head node. The simulation results show that compared with LEACH algorithm and ant colony optimization clustering routing algorithm,the algorithm can effectively find the optimal routing path,which saves network energy consumption and make the network life cycle longer.
引文
[1] Sun Li-min,Li Jian-zhong,Chen Yu,et al. Wireless sensor network[M]. Beijing:Tsinghua University Press,2005.
[2] Heinzelman W,Chandrakasan A,Balakrishnan H. Energy efficient communication protocol for w ireless microsensor netw orks[C].Proceeding of the Haw aii International Conference on System Sciences,Haw aii,January 2000,HICSS:186-192.
[3] Wang Dong. Design and application of wireless sensor network system[D]. Chongqing:Chongqing University,2006.
[4] Lei Hua-jun,Qin Kai-yu. Test optimization selection based on quantum evolutionary algorithm under unreliable test conditions[J]. Acta Electronica Sinica,2017,45(10):2464-2472.
[5] Li Pan-chi,Li Shi-yong. Quantum ant colony algorithm for solving continuous space optimization problems[J]. Control Theory&Applications,2008,25(2):237-241.
[6] Yang Jia,Xu Qiang,Zhang Jin-rong,et al. A new quantum ant colony optimization algorithm[J]. Journal of Sun Yatsen University(Natural Science),2009,48(3):22-27.
[7] Chen Guo-shuai. Research on energy management strategy of wireless sensor netw ork for energy harvesting[D]. Xi'an:Xidian University,2017.
[8] Li An-chao,Chen Gui-fen. Improved clustering routing algorithm for energy heterogeneous w ireless sensor netw orks[J]. Journal of Transduction Technology,2017,30(11):1712-1718.
[9] Jiang Ai-lian,Zheng Li-hong. An effective hybrid routing algorithm in WSN:ant colony optimization in combination w ith hop count minimization[J]. Sensors(Basel,Sw itzerland),2018,18(4):1627-1631.
[10] Wang Qi-ming,Li Zhan-guo,Fan Ai-wan. Quantum ant colony algorithm based on game theory[J]. Journal of Shandong University(Engineering Science),2015,45(2):33-36.
[11] Huang Li-xiao,Wang Hui,Yuan Li-yong,et al. Improved algorithm of LEACH protocol based on energy balance and efficient WSN[J]. Journal on Communications,2017,38(S2):164-169.
[12] Jiang Hua,Cai Zhen-hai,Wang Xin. Energy routing protocol for underw ater w ireless sensor netw orks based on ant colony[J]. M icroelectronics&Computer,2017,34(8):12-16.
[1]孙利民,李建中,陈渝,等.无线传感器网络[M].北京:清华大学出版社,2005.
[3]王东.无线传感器网络系统设计与应用[D].重庆:重庆大学,2006.
[4]雷化军,秦开宇.测试不可靠条件下基于量子进化算法的测试优化选择[J].电子学报,2017,45(10):2464-2472.
[5]李盼池,李士勇.求解连续空间优化问题的量子蚁群算法[J].控制理论与应用,2008,25(2):237-241.
[6]杨佳,许强,张金荣,等.一种新的量子蚁群优化算法[J].中山大学学报(自然科学版),2009,48(3):22-27.
[7]陈国帅.能量收集的无线传感器网络能量管理策略研究[D].西安:西安电子科技大学,2017.
[8]李安超,陈桂芬.能量异构无线传感器网络分簇路由改进算法[J].传感技术学报,2017,30(11):1712-1718.
[10]王启明,李战国,樊爱宛.基于博弈论的量子蚁群算法[J].山东大学学报(工学版),2015,45(2):33-36.
[11]黄利晓,王晖,袁利永,等.基于能量均衡高效WSN的LEACH协议改进算法[J].通信学报,2017,38(S2):164-169.
[12]蒋华,蔡振海,王鑫.基于蚁群的水下无线传感器网络能量路由协议[J].微电子与计算机,2017,34(8):12-16.
[2] Heinzelman W,Chandrakasan A,Balakrishnan H. Energy efficient communication protocol for w ireless microsensor netw orks[C].Proceeding of the Haw aii International Conference on System Sciences,Haw aii,January 2000,HICSS:186-192.
[3] Wang Dong. Design and application of wireless sensor network system[D]. Chongqing:Chongqing University,2006.
[4] Lei Hua-jun,Qin Kai-yu. Test optimization selection based on quantum evolutionary algorithm under unreliable test conditions[J]. Acta Electronica Sinica,2017,45(10):2464-2472.
[5] Li Pan-chi,Li Shi-yong. Quantum ant colony algorithm for solving continuous space optimization problems[J]. Control Theory&Applications,2008,25(2):237-241.
[6] Yang Jia,Xu Qiang,Zhang Jin-rong,et al. A new quantum ant colony optimization algorithm[J]. Journal of Sun Yatsen University(Natural Science),2009,48(3):22-27.
[7] Chen Guo-shuai. Research on energy management strategy of wireless sensor netw ork for energy harvesting[D]. Xi'an:Xidian University,2017.
[8] Li An-chao,Chen Gui-fen. Improved clustering routing algorithm for energy heterogeneous w ireless sensor netw orks[J]. Journal of Transduction Technology,2017,30(11):1712-1718.
[9] Jiang Ai-lian,Zheng Li-hong. An effective hybrid routing algorithm in WSN:ant colony optimization in combination w ith hop count minimization[J]. Sensors(Basel,Sw itzerland),2018,18(4):1627-1631.
[10] Wang Qi-ming,Li Zhan-guo,Fan Ai-wan. Quantum ant colony algorithm based on game theory[J]. Journal of Shandong University(Engineering Science),2015,45(2):33-36.
[11] Huang Li-xiao,Wang Hui,Yuan Li-yong,et al. Improved algorithm of LEACH protocol based on energy balance and efficient WSN[J]. Journal on Communications,2017,38(S2):164-169.
[12] Jiang Hua,Cai Zhen-hai,Wang Xin. Energy routing protocol for underw ater w ireless sensor netw orks based on ant colony[J]. M icroelectronics&Computer,2017,34(8):12-16.
[1]孙利民,李建中,陈渝,等.无线传感器网络[M].北京:清华大学出版社,2005.
[3]王东.无线传感器网络系统设计与应用[D].重庆:重庆大学,2006.
[4]雷化军,秦开宇.测试不可靠条件下基于量子进化算法的测试优化选择[J].电子学报,2017,45(10):2464-2472.
[5]李盼池,李士勇.求解连续空间优化问题的量子蚁群算法[J].控制理论与应用,2008,25(2):237-241.
[6]杨佳,许强,张金荣,等.一种新的量子蚁群优化算法[J].中山大学学报(自然科学版),2009,48(3):22-27.
[7]陈国帅.能量收集的无线传感器网络能量管理策略研究[D].西安:西安电子科技大学,2017.
[8]李安超,陈桂芬.能量异构无线传感器网络分簇路由改进算法[J].传感技术学报,2017,30(11):1712-1718.
[10]王启明,李战国,樊爱宛.基于博弈论的量子蚁群算法[J].山东大学学报(工学版),2015,45(2):33-36.
[11]黄利晓,王晖,袁利永,等.基于能量均衡高效WSN的LEACH协议改进算法[J].通信学报,2017,38(S2):164-169.
[12]蒋华,蔡振海,王鑫.基于蚁群的水下无线传感器网络能量路由协议[J].微电子与计算机,2017,34(8):12-16.