面向水环境监测的无线传感网络协作波束形成远距离传输优化方法
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摘要
在无线传感网络技术应用于水环境的自主监测过程中,无线传感网络限制的传输距离成为制约其发展的主要问题之一。针对当前水环境监测及无线传感网络远距离传输存在的问题,提出了基于改进的高斯骨架差分进化的波束合成远距离传输优化方法。首先根据远距离传输要求建立优化模型,该模型不仅考虑接收节点方向协作波束的主瓣增益,还考虑了旁瓣对其他非接收方向的干扰问题;然后提出了改进高斯骨架差分优化方法,该方法在交叉过程中增加对旁瓣幅值的判断,加速了节点功率优化的收敛速度。实验对比和验证分析结果表明,该方法在不同主瓣增益要求条件下最小旁瓣增益性能比典型优化算法提升了6.8%~10.2%,比随机优化方法提升了31.8%~35.4%,不仅能够满足实际要求,而且能够有效减少对其他非接收方向的干扰,为实现水环境监测远距离传输提供了有效的理论和技术支撑。
During applying the technique of wireless sensor network to the autonomous water environment monitoring,the transmission distance limited by wireless sensor network becomes the major problem to restrict its development. Aiming at the problems from water environment monitoring and long-distance transmission of wireless sensor network at present,an optimization method for the improved Gaussian bare-bones DE algorithm based-beamforming long-distance transmission is proposed herein. At first,an optimization model is established based on the requirement for the relevant long-distance transmission,by which not only the mainlobe gain of the collaborative wavebeam in the direction of receiving node is considered,but the interference from the sidelobe on the other non-receiving directions is also taken into account. Whereafter,the improved Gaussian bare-bones DE optimization method is put forward,in which the judgment of the amplitude value of sidelobe is added into the process of the crossoperation,thus the convergence rate of the optimization of the node power is accelerated. Through relevant experimental comparison and confirmatory analysis,it is indicated that under the conditions of various main lobe gain requirements,the minimum sidelobe gain performance of the proposed method is increased by 6. 8% ~ 10. 2% if compared with that of the typical optimization algorithm,while it is increased by 31. 8% ~ 35. 4% if compared with the stochastic optimization method,from which not only the relevant actual requirements can be met,but the interfereces from the other non-receiving directions can also be effectively reduced,and then an effective theoretical and technical support for the long-distance transmission of water environment monitoring is achieved.
During applying the technique of wireless sensor network to the autonomous water environment monitoring,the transmission distance limited by wireless sensor network becomes the major problem to restrict its development. Aiming at the problems from water environment monitoring and long-distance transmission of wireless sensor network at present,an optimization method for the improved Gaussian bare-bones DE algorithm based-beamforming long-distance transmission is proposed herein. At first,an optimization model is established based on the requirement for the relevant long-distance transmission,by which not only the mainlobe gain of the collaborative wavebeam in the direction of receiving node is considered,but the interference from the sidelobe on the other non-receiving directions is also taken into account. Whereafter,the improved Gaussian bare-bones DE optimization method is put forward,in which the judgment of the amplitude value of sidelobe is added into the process of the crossoperation,thus the convergence rate of the optimization of the node power is accelerated. Through relevant experimental comparison and confirmatory analysis,it is indicated that under the conditions of various main lobe gain requirements,the minimum sidelobe gain performance of the proposed method is increased by 6. 8% ~ 10. 2% if compared with that of the typical optimization algorithm,while it is increased by 31. 8% ~ 35. 4% if compared with the stochastic optimization method,from which not only the relevant actual requirements can be met,but the interfereces from the other non-receiving directions can also be effectively reduced,and then an effective theoretical and technical support for the long-distance transmission of water environment monitoring is achieved.
引文
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[2]王骥,林杰华,谢仕义.基于无线传感网络的环境监测系统[J].传感技术学报,2015,24(11):1732-1740.
[3]杨冬,马剑,张宏科.智慧协同工业无线传感器网络[J].电子学报,2017,45(6):1537-1544.
[4]黄欣,赵志刚,万荣泽.面向精细农业的无线传感器网络关键技术研究[J].农机化研究,2017,39(11):208-211.
[5]俞姝颖,吴小兵,陈贵海,等.无线传感器网络在桥梁健康监测中的应用[J].软件学报,2015,26(6):1486-1498.
[6]彭宇帆,温华.基于无线传感器网络技术的水体环境监测系统[J].物联网技术,2017,7(8):26-27.
[7]YETGIN H,CHEUNG K T K,EL-HAJJAR M,et al.A survey of network lifetime maximization techniques in wireless sensor networks[J].IEEE communications surveys&tutorials,2017,19(2):828-854.
[8]JAYAPRAKASAM S,RAHIM S K A,LEOW C Y.Distributed and collaborative beamforming in wireless sensor networks:classifications,trends and research directions[J].IEEE communications surveys&tutorials,2017,19(4):2092-2116.
[9]OCHIAIH,MITRAN P,POOR H V,et al.Collaborative beamforming in ad hoc networks[C].2004 IEEE information theory workshop,Piscataway New Jersey,USA:IEEE,2004:396-401.
[10]OCHIAI H,MITRAN P,POOR H V,et al.Collaborative beamforming for distributed wireless ad hoc sensor networks[J].IEEE transactions on signal processing,2007,53(11):4110-4124.
[11]AHMED M F A,VOROBYOV S A.Collaborative beamforming for wireless sensor networks with Gaussian distributed sensor nodes[J].IEEE transactions on wireless communications,2009,8(2):638-643.
[12]秦智超,周正,赵小川.采用免疫遗传的无线传感器网络波束成形机制[J].北京邮电大学学报,2013,36(1):77-81.
[13]SUN G,LIU Y,ZHANG J,et al.Node selection optimization for collaborative beamforming in wireless sensor networks[J].Ad hoc networks,2016,37(P2):389-403.
[14]CHEN J C,WEN C K,WONG K K.An efficient sensor-node selection algorithm for sidelobecontrol in collaborative beamforming[J].IEEE transactions on vehicular technology,2016,65(8):5984-5994.
[15]JAYAPRAKASAM S,RAHIM S K A,LEOW C Y,et al.Beampatten optimization in distributed beamforming using multiobjective and metaheuristic method[C].2014 symposium on IEEE wireless technology and applications,Piscataway New Jersey,USA:IEEE,2014:86-91.
[16]JAYAPRAKASAM S,ABDUL RAHIM S K,LEOW C Y,et al.Sidelobe reduction and capacity improvement of open-loop collaborative beamforming in wireless sensor networks[J].Plos one,2017,12(5):1-33.
[17]SUN G,LIU Y,WANG A,et al.Sidelobecontrol by node selection algorithm based on virtual linear array for collaborative beamforming in WSNs[J].Wireless personal communications,2016,90(3):1-20.
[18]WANG H,RAHNAMAYAN S,SUN H,et al.Gaussian bare-bones differential evolution[J].IEEE transactions on cybernetics,2013,43(2):634-647.
[19]FAN Q,YAN X.Self-adaptive differential evolution algorithm with discrete mutation control parameters[J].Expert systems with applications,2015,42(3):1551-1572.