无线传感网络在环境监测中的应用
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摘要
传统的环境监测系统有线监测组网高成本、施工困难、维护工作量繁重,因此很难应用于大面积范围环境监测。针对这个问题,本文提出了在环境中建立无线传感网络来实现对其监测。无线传感器网络的出现使大范围环境随机研究数据获取更易实现,也对人类难以到达的环境长期监控提供了极大的方便。本课题设计主要以阜新煤矿的煤矸石山的监控为应用背景,组建一个无线传感网络监测系统,简要介绍无线传感器网络的建立与实现,说明了基于无线传感器网络环境监测网的组建方案,提出了传感器节点、簇头、基站、监控中心的四层结构。在监测过程中,部署的传感器用来采集影响煤矸石自燃的重要环境因素的信息数据,将信息发送到簇头,簇头将采集到的数据进行过滤压缩打包并通过单跳路由将信息发送到基站,最后传输到监控中心。当监测参数超出正常范围时,监控系统给予报警处理,同时显示不正常参数及可执行操作供参考选择。
与有线网络进行比较,不难看出无线传感网络的能源受到极大的限制,网络优化的关键就是延长网络寿命。本文主要采用伪并行遗传算法这一现代全局寻优工具,根据本文矸石山中传感网络的组网模式,进行了编码机制的设计,适应度函数的建立,控制参数的调整等重要工作,实现对网络的优化设计。在网络优化过程中,休眠误差参数,网络连通参数、能量参数都被考虑进去,优化了网络的能量管理,使网络的生命周期达到最长。
Because of wired high-cost, construction difficulties, maintenance of heavy workload to build a traditional environmental monitoring system, it is difficult to apply on a large range of environmental monitoring. As to this question, we adopt the establishment of wireless sensor networks to achieve the environmental monitoring. The emergence of wireless sensor networks make the environment a wide range of large randomized study of data acquisition is more easily achieved, and provide a great convenience to the environment where is hard to reach. This paper designs a formation of the wireless sensor network monitoring system to Fuxin coal gangue hills surveillance. It introduces simply the establishment and implementation of the Wireless sensor networks, describes the environmental monitoring network based on wireless sensor networks, expounds the formation of the program proposed,which includes four-story structure:sensor nodes, cluster head, base stations and control centers. In the monitoring process, the sensors are used to collect the environment information and data, then send the information to cluster head. The cluster head filters the data and makes compression package, then sends the information through a single-hop routing to the base station, finally transmits to the monitoring center. If the monitoring parameter outside the normal range, the monitoring system will alarm, show abnormal parameters and perform the available operation.
Compared with the wired network, wireless sensor network is easy to see that the energy is very important for the lives of wireless sensor networks, pseudo-parallel genetic algorithms, one of modern global optimization tools, were used in this article. According to the network model of the wireless sensor networks of Fuxin coal gangue. hill, coding mechanism was designed, fitness function was established, and control parameters were adjusted. In the adaptive network design, dormant error parameters, network connectivity parameters, energy parameters were taken into account, the energy management of network was optimized, network life cycle reach to the maximum.
与有线网络进行比较,不难看出无线传感网络的能源受到极大的限制,网络优化的关键就是延长网络寿命。本文主要采用伪并行遗传算法这一现代全局寻优工具,根据本文矸石山中传感网络的组网模式,进行了编码机制的设计,适应度函数的建立,控制参数的调整等重要工作,实现对网络的优化设计。在网络优化过程中,休眠误差参数,网络连通参数、能量参数都被考虑进去,优化了网络的能量管理,使网络的生命周期达到最长。
Because of wired high-cost, construction difficulties, maintenance of heavy workload to build a traditional environmental monitoring system, it is difficult to apply on a large range of environmental monitoring. As to this question, we adopt the establishment of wireless sensor networks to achieve the environmental monitoring. The emergence of wireless sensor networks make the environment a wide range of large randomized study of data acquisition is more easily achieved, and provide a great convenience to the environment where is hard to reach. This paper designs a formation of the wireless sensor network monitoring system to Fuxin coal gangue hills surveillance. It introduces simply the establishment and implementation of the Wireless sensor networks, describes the environmental monitoring network based on wireless sensor networks, expounds the formation of the program proposed,which includes four-story structure:sensor nodes, cluster head, base stations and control centers. In the monitoring process, the sensors are used to collect the environment information and data, then send the information to cluster head. The cluster head filters the data and makes compression package, then sends the information through a single-hop routing to the base station, finally transmits to the monitoring center. If the monitoring parameter outside the normal range, the monitoring system will alarm, show abnormal parameters and perform the available operation.
Compared with the wired network, wireless sensor network is easy to see that the energy is very important for the lives of wireless sensor networks, pseudo-parallel genetic algorithms, one of modern global optimization tools, were used in this article. According to the network model of the wireless sensor networks of Fuxin coal gangue. hill, coding mechanism was designed, fitness function was established, and control parameters were adjusted. In the adaptive network design, dormant error parameters, network connectivity parameters, energy parameters were taken into account, the energy management of network was optimized, network life cycle reach to the maximum.
引文
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[18]Joachim S. Parallel Genetic Algorithms:Theory and Applications[M]. ISO Press.1993.
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[24]N. Noury, T. Herve, V. Rialle, G. Virone, E. Mercier, G. Morey, A. Moro, T. Porcheron, Monitoring behavior in home using a smart fall sensor[J]. IEEE-EMBS Special Topic Conference on Microtechnologies in Medicine and Biology, October 2000, pp:607-610.
[25]B. Sibbald, Use computerized systems to cut adverse drug events:report. CMAJ: Canadian Medical Association Journal 164 (13) (2001):1878, 1/2p, 1c.
[26]E. M. Petriu, N. D. Georganas, D. C. Petriu, D. Makrakis, V. Z. Groza. Sensor-based information appliances[J]. IEEE Instrumentation and Measurement Magazine (December 2000): 31-35.
[27]C. Herring, S. Kaplan. Component-based software systems for smart environments[J]. IEEE Personal Communications, Ocober 2000, pp:60-61.
[28]J. M. Rabaey, M. J. Ammer, J. L. daSilva Jr. D. Patel, S. Roundy, PicoRadio supports ad hoc ultra-low power I. F. Akyildiz et al./Computer Networks 38 (2002) 393-422 421 wireless networking, IEEE Computer Magazine (2000):42-48.
[29]E. Shih, S. Cho, N. Ickes, R. Min, A. Sinha, A. Wang. A. Chandrakasan, Physical layer driven protocol and algorithm design for energy-efficient wireless sensor networks[J]. Proceedings of ACM MobiCom'01, Rome. Italy. July 2001:pp.272-286.
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[32]张文修,梁怡.遗传算法的数学基础[M].西安：西安交通大学出版社,2000.
[33]张宗炳.遗传与进化[M].北京：人民教育出版社,1981.
[34]周明,孙树栋.遗传算法原理及应用[M].北京：国防工业出版社,1999.
[35]席裕庚.遗传算法综述.控制理论与应用[M],1996：(6).
[36]徐宗本,高勇.遗传算法早收敛现象的特征分析及预防[M].中国科学1996：26(4).
[37]周明,孙树栋等.遗传算法原理及应用[M].北京：国防工业出版社,2000.
[38]I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, Wireless sensor networks: a survey, Computer Networks 38 (2002):393-422.
[39]S. Slijepcevic, M. Potkonjak, Power efficient organization of wireless sensor networks[J]. in: Proc. IEEE Int. Conf. on Communications. Helsinki, Finland.2001:pp.472-476.
[40]D. E. Goldberg, Genetic Algorithms in Search Optimization and Machine Learning[J]. Addison-Wesley. Reading. MA.1989.
[2]戴奕.基于无线传感网络GSM的环境监测网的研究[D].吉林大学,2008.
[3]李海建,王民,王怀德.无线传感器网络在农田信息采集中的应用[J].农机化研究,2008：3(3).
[4]农业部环保能源司等.农业环境管理法规资料汇编[M].天津：天津科技翻译出版社,1999：
266-267.
[5]马斌强,刘美琪,季宝杰,李勉,路康,哀超.ZigBee技术在精准农业中的应用[J].江西农业学报,2008(10).
[6]鲍军民.ZigBee技术在温室监控系统中的应用[J].农机化研究,2008(2).
[7]马继伟,刘士光等.基于ZigBee技术的农业设施群测控系统的研制[J].农机化研究,2005,(8).
[8]潘大宇.ZigBee技术在农业自动化监控系统中的研究与应用[D].曲阜师范大学,2009.
[9]杨国清,刘康怀.固体废物处理工程[J].2000.
[10]潘荣锟,余明高.矸石山的危害及自燃原因关联分析[J].安全与环境工程,2006(2)：66-67.
[11]卢淑芝.阜新市煤矸石山的综合治理[J].能源环境保护,2003(3)：35-36.
[12]任丰原,黄海宁,林闯.无线传感器网络[J].软件学报,2003(7)：1282-1291.
[13]于海滨,曾鹏.分布式无线传感器网络协议研究[J]..通信学报.2004(10)：102-110.
[14]李建中,李金宝,石胜飞.传感器网络及其数据管理的概念问题与进展[J].软件学报,2003(10)：1717-1727.
[15]纪红.无线传感器网络未来新的高技术产业[J].当代通信,.2004：21.
[16]杨佑禄.无线传感器网络环境监测系统节点设计与实现[D].电子科技大学,2007.
[17]孙利民,李建中等.传感器网络[M].北京：清华大学出版社,2005.
[18]Joachim S. Parallel Genetic Algorithms:Theory and Applications[M]. ISO Press.1993.
[19]崔莉,苗勇,刘巍等.无线传感器网络研完进展[J].计算机研究与发展,2005(1)：163-174.
[20]M. Bhardwaj, T. Garnett, A. P. Chandrakasan, Upper bounds on the lifetime of sensor [J], networks [J]. IEEE International Conference on Communications ICC'01. Helsinki, Finland, June 2001.
[21]P. Bonnet, J. Gehrke, P. Seshadri, Querying the physical world[J]. IEEE Personal Communications (October 2000):10-15.
[22]N. Bulusu, D. Estrin, L. Girod, J. Heidemann, Scalable coordination for wireless sensor networks:self-configuring localization systems. International Symposium on Communication Theory and Applications (ISCTA 2001)[J]. Ambleside, UK, July 2001.
[23]J. M. Kahn,R. H. Katz, K. S. J. Pister, Next century challenges:mobile networking for smart dust, Proceedings of the ACM MobiCom'99[J], Washington, USA,1999:pp.271-278.
[24]N. Noury, T. Herve, V. Rialle, G. Virone, E. Mercier, G. Morey, A. Moro, T. Porcheron, Monitoring behavior in home using a smart fall sensor[J]. IEEE-EMBS Special Topic Conference on Microtechnologies in Medicine and Biology, October 2000, pp:607-610.
[25]B. Sibbald, Use computerized systems to cut adverse drug events:report. CMAJ: Canadian Medical Association Journal 164 (13) (2001):1878, 1/2p, 1c.
[26]E. M. Petriu, N. D. Georganas, D. C. Petriu, D. Makrakis, V. Z. Groza. Sensor-based information appliances[J]. IEEE Instrumentation and Measurement Magazine (December 2000): 31-35.
[27]C. Herring, S. Kaplan. Component-based software systems for smart environments[J]. IEEE Personal Communications, Ocober 2000, pp:60-61.
[28]J. M. Rabaey, M. J. Ammer, J. L. daSilva Jr. D. Patel, S. Roundy, PicoRadio supports ad hoc ultra-low power I. F. Akyildiz et al./Computer Networks 38 (2002) 393-422 421 wireless networking, IEEE Computer Magazine (2000):42-48.
[29]E. Shih, S. Cho, N. Ickes, R. Min, A. Sinha, A. Wang. A. Chandrakasan, Physical layer driven protocol and algorithm design for energy-efficient wireless sensor networks[J]. Proceedings of ACM MobiCom'01, Rome. Italy. July 2001:pp.272-286.
[30]CC2420 Data Sheet[M]. TI Corporation,2006.
[31]MSP430F 149 data sheet[M]. TI Corporation,2003.
[32]张文修,梁怡.遗传算法的数学基础[M].西安：西安交通大学出版社,2000.
[33]张宗炳.遗传与进化[M].北京：人民教育出版社,1981.
[34]周明,孙树栋.遗传算法原理及应用[M].北京：国防工业出版社,1999.
[35]席裕庚.遗传算法综述.控制理论与应用[M],1996：(6).
[36]徐宗本,高勇.遗传算法早收敛现象的特征分析及预防[M].中国科学1996：26(4).
[37]周明,孙树栋等.遗传算法原理及应用[M].北京：国防工业出版社,2000.
[38]I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, Wireless sensor networks: a survey, Computer Networks 38 (2002):393-422.
[39]S. Slijepcevic, M. Potkonjak, Power efficient organization of wireless sensor networks[J]. in: Proc. IEEE Int. Conf. on Communications. Helsinki, Finland.2001:pp.472-476.
[40]D. E. Goldberg, Genetic Algorithms in Search Optimization and Machine Learning[J]. Addison-Wesley. Reading. MA.1989.