无线传感器网络路由算法的研究
摘要
无线传感器网络是由众多传感器在空间上以无线通信方式自组织成一个多跳的网络系统。目前在IT行业比较热门有物联网和智慧地球两个概念,其核心的底层技术之一就是无线传感器网络。无线传感器网络的出现越来越引起了全世界人们的关注,继因特网之后,它是会给21世纪人们的生产和生活方式带来重大改变的IT技术之一。并且它具有节点众多、快速部署、自组织成网、有限的节点能量和多变的拓扑结构等特点,由于无线传感器网络自身的原因,以往的网络协议已不在适用于无线传感器网络。因此,研究并设计新的路由协议是无线传感器网络面临的一个新的挑战。
无线传感器网络具有十分广阔的应用前景,越来越成为计算机科学领域的研究热点问题。近年来有许多研究学者研究并提出了许多典型的无线传感器网络路由算法,但这些路由算法都或多或少有一些缺陷与不足。本文从智能优化算法的研究角度出发,比较系统地研究了无线传感器网络路由算法,在对一些典型的路由算法与协议分析比较之后,针对无线传感器网络的特点,提出了基于蚁群的WSN路由算法。
蚁群算法是计算领域非常成功的案例之一,是模拟真实蚂蚁寻找最优路径的仿生优化算法,具有分布式计算、支持多路径和易实现等特点。本文通过对蚁群算法的深入分析研究后,发现蚁群算法在寻找路径的过程中会产生无效的周游路径,即对蚂蚁间的协作产生影响,这将显著地降低算法的收敛速度和性能,为克服这种现象,本文对经典蚁群算法进行改进,提出了一种基于混合行为的新蚁群算法。算法中引入停止蚂蚁的概念和局部调优策略,通过构造局部路线,防止无用路径的产生,仿真实验表明,能比较显著地提高算法的性能。本论文的另一个研究内容是通过对LEACH协议进行研究,指出其不足之处并对其进行改进,提出一种基于改进蚁群的WSN路由算法,在选取簇头时不仅把传感器节点的剩余能量考虑进来,而且将改进后蚁群算法应用于簇间寻找路径,从而形成簇间多跳路由,能有效地减少簇头节点能量的消耗。经过对改进后路由算法的测试分析,发现算法有效可行,延长了网络的总体生命周期,使无线传感器网络的总体性能得到了改善。
Wireless sensor network is made up of numerous micro-sensor nodes which monitor the area and make up a multi-hop self-organizing system by means of wireless communication. In the IT industry, there are two popular concepts which is the internet of things and smart planet, one of core technology is wireless sensor networks. It draws the attention of people all over the world, and will have a significant impact on the lifestyle of the 21st century after the internet. Wireless sensor network owns the features that have many nodes, rapid deployment, self-organize, lower node’s energy and the changing topology. The traditional network protocol is no longer suitable for wireless sensor networks due to the limits of wireless sensor network’s characteristics. Therefore, the research and design of the new routing algorithm for wireless sensor networks faces a new challenge.
Wireless sensor network has very broad application prospects, and is increasingly the hot issues in the areas of computer science. In recent years, many researchers have proposed a number of typical routing algorithms for wireless sensor networks, but these routing algorithms have some more or less flaws and shortcomings. This pater from the viewpoint of study on intelligent optimization algorithm, systematic researches on wireless sensor network routing algorithm and proposes a WSN routing algorithm based on Ant Colony Optimization after analyzing and comparing with some typical routing algorithms and routing protocols of wireless sensor networks.
Ant Colony algorithm is one of the success cases in intelligent computing field. It is simulating ants Bionic optimization algorithms, which has distribute computing, support for multiple paths and ease of implementation characteristics. This article, after deeply analyzing and researching on ant colony algorithm, founds that the invalid travel path of the ant colony algorithm could be generated, having an impact on ant interaction. This will significantly reduce the speed of convergence and performance of the algorithm, to avoid this phenomenon, ant colony algorithm based on hybrid behavior is proposed. The algorithm introduces the stopping ant and local optimization strategies to construct a local route to prevent useless path. Simulation experiments show that it can significantly improve the performance of algorithms. Another content of this paper is researching LEACH Protocol. Pointing out its shortcomings and improving it, a WSN routing algorithm based on improved ant colony optimization is proposed which considers the remaining energy of the nodes in choosing the cluster heads and also the improved ant colony algorithm is applied to cluster path and form clustered multi-hop routing, these could effectively reduce the energy consumption of the cluster head node. After simulation test on the routing algorithm, it is effective and feasible, extends the network life cycle, and improves the performance of wireless sensor networks.
无线传感器网络具有十分广阔的应用前景,越来越成为计算机科学领域的研究热点问题。近年来有许多研究学者研究并提出了许多典型的无线传感器网络路由算法,但这些路由算法都或多或少有一些缺陷与不足。本文从智能优化算法的研究角度出发,比较系统地研究了无线传感器网络路由算法,在对一些典型的路由算法与协议分析比较之后,针对无线传感器网络的特点,提出了基于蚁群的WSN路由算法。
蚁群算法是计算领域非常成功的案例之一,是模拟真实蚂蚁寻找最优路径的仿生优化算法,具有分布式计算、支持多路径和易实现等特点。本文通过对蚁群算法的深入分析研究后,发现蚁群算法在寻找路径的过程中会产生无效的周游路径,即对蚂蚁间的协作产生影响,这将显著地降低算法的收敛速度和性能,为克服这种现象,本文对经典蚁群算法进行改进,提出了一种基于混合行为的新蚁群算法。算法中引入停止蚂蚁的概念和局部调优策略,通过构造局部路线,防止无用路径的产生,仿真实验表明,能比较显著地提高算法的性能。本论文的另一个研究内容是通过对LEACH协议进行研究,指出其不足之处并对其进行改进,提出一种基于改进蚁群的WSN路由算法,在选取簇头时不仅把传感器节点的剩余能量考虑进来,而且将改进后蚁群算法应用于簇间寻找路径,从而形成簇间多跳路由,能有效地减少簇头节点能量的消耗。经过对改进后路由算法的测试分析,发现算法有效可行,延长了网络的总体生命周期,使无线传感器网络的总体性能得到了改善。
Wireless sensor network is made up of numerous micro-sensor nodes which monitor the area and make up a multi-hop self-organizing system by means of wireless communication. In the IT industry, there are two popular concepts which is the internet of things and smart planet, one of core technology is wireless sensor networks. It draws the attention of people all over the world, and will have a significant impact on the lifestyle of the 21st century after the internet. Wireless sensor network owns the features that have many nodes, rapid deployment, self-organize, lower node’s energy and the changing topology. The traditional network protocol is no longer suitable for wireless sensor networks due to the limits of wireless sensor network’s characteristics. Therefore, the research and design of the new routing algorithm for wireless sensor networks faces a new challenge.
Wireless sensor network has very broad application prospects, and is increasingly the hot issues in the areas of computer science. In recent years, many researchers have proposed a number of typical routing algorithms for wireless sensor networks, but these routing algorithms have some more or less flaws and shortcomings. This pater from the viewpoint of study on intelligent optimization algorithm, systematic researches on wireless sensor network routing algorithm and proposes a WSN routing algorithm based on Ant Colony Optimization after analyzing and comparing with some typical routing algorithms and routing protocols of wireless sensor networks.
Ant Colony algorithm is one of the success cases in intelligent computing field. It is simulating ants Bionic optimization algorithms, which has distribute computing, support for multiple paths and ease of implementation characteristics. This article, after deeply analyzing and researching on ant colony algorithm, founds that the invalid travel path of the ant colony algorithm could be generated, having an impact on ant interaction. This will significantly reduce the speed of convergence and performance of the algorithm, to avoid this phenomenon, ant colony algorithm based on hybrid behavior is proposed. The algorithm introduces the stopping ant and local optimization strategies to construct a local route to prevent useless path. Simulation experiments show that it can significantly improve the performance of algorithms. Another content of this paper is researching LEACH Protocol. Pointing out its shortcomings and improving it, a WSN routing algorithm based on improved ant colony optimization is proposed which considers the remaining energy of the nodes in choosing the cluster heads and also the improved ant colony algorithm is applied to cluster path and form clustered multi-hop routing, these could effectively reduce the energy consumption of the cluster head node. After simulation test on the routing algorithm, it is effective and feasible, extends the network life cycle, and improves the performance of wireless sensor networks.
引文
1. Hewish M. Little Brother is Watching you: Unattended Ground Sensors[J]. Defense Review, 2001, 34(6): 46-52.
2. Arici T, Altunbasak Y. Adaptive Sensing for Environment Monitoring Using Wireless Sensor Networks[C]. In Proceeding of the IEEE Wireless Communications and Networking Conference(WCNC), 2004,5(1): 2350-2355.
3.赵泽,崔莉.一种基于无线传感器网络的远程医疗监护系统[J].信息与控制,2006, 35(2): 265-269.
4. Meyer S, Rakotonirainy A. A Survey of Research on Context-aware Homes[C]. Workshop on Wearable, Invisible, Context-aware, Ambient, Pervasive and Ubiquitous Computing, 2003, 1: 159-168.
5. Coleri S, Cheung S Y, Varaiya P. Sensor Networks for Monitoring Traffic[C]. In Proceeding of Forty Second Annual Allerton Conference on Commuinication, Control and Computing, U. of Illinois, September 2004: 1-10.
6. Lacoss R, Walton R. Strawman Design for a DSN to Detect and Track Low Flying Aircraft[C]. In Proceeding of Distributed Sensor Nets Conference, 1978: 41-52.
7. Byrne J A. 21 Ideas for the 21st Century. Business Week, 1999,8:78-167.
8. 10 Emerging Technologies that Will Change the World. Technology Review, 2003,106(1):33-49.
9. Heinzelman WR, Chandrakasan A,Balakrishnan H.An application specific protocol architecture for wireless micro-sensor networks[J] . IEEE Transaction on Wireless Communications, 2002,1(4): 660-670.
10. Lindsev S, Raghavendra C S. Sivalingam K. Data gathering in sensor networks using the energy delay metric[C], in: Proceedings of the IPDPS Workshop on Issues in Wireless Networks and Mobile Computing, San Francisco, CA, April 2001.
11.中国科学院上海微系统与信息技术研究所科研成果[EB/OL], http://www.sim.ac.cn/xwzx/zhxw/200710/t20071022_1908127.html, 2007-10-22.
12. Colorni A, Dorigo M, Maniezzo V. Distributed optimization by ant colonies[C]. Prceeding of the First European Conference on Artificial Life. Paris France:Elsevier Publishing, 1991:134-142.
13.任丰原,黄海宁,林闯.无线传感器网络[J].软件学报, 2003, 14(8): 1281-1291.
14.孙雨耕,张静,孙永进等.无线自组传感器网络[J].传感技术学报,2004,2(20): 331-348.
15. Federal Agencies Need to Improve Controls over Wireless Networks[R]. United States Government Accountability Office, 2005,5.
16.于海滨,曾鹏,王中峰等.分布式无线传感器网络通信协议研究[J].通信学报,2004,25(10): 102-110.
17. Woo A, Culler D E. A Transmission Control Scheme for Media Access in Sensor Networks [A]. Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM′01 [C]. Rome (Italy), 2001. New York (NY, USA): ACM Press,2001.221—235.
18. TinyOS[OL]. http://www.tinyos.net.
19. Younis O,Fahmy S.Distributed Clustering in Ad-Hoe Sensor Networks:A Hybrid,Energy-Efficient Approach[C] . In proceeding of the IEEE Conference On Computer Communications(INFOCOM),Mar.2004:629—640.
20.黄少奕,曹阳,王悦伟.无线传感器网络中的路由技术[J].计算机工程与应用,2004.19:12-128.
21.唐宏,谢静等.无线传感器网络原理及应用[M].人民邮电出版社,2010.8.
22. Bao L, Garcia-Luna-Aceves J. Topology Management in Ad Hoc Networks[J]. In Proceeding of the 4th ACM International Symposium on Mobile Ad Hoc Networking & Computing, June 2003: 129– 140.
23. Tubaishat M, Madria S. Sensor Networks: An Overview[J]. IEEE Potentials, 2003, 22(2): 20-23.
24. Cullar D, Estrin D, Strvastava M. Overview of Sensor Network[J]. Computer, 2004, 37(8): 41-49.
25. Al-Karaki J N, karmal A E. Routing Techniques in Wireless Sensor Networks: A Survey[J]. IEEE Personal Communications, 2004, 11(6): 6-28.
26. Hedetniemi S, Liestman A. A Survey of Gossiping and Broadcasting in Communication Networks[J]. 1998, 18(4): 319-349.
27. Kulik J, Heinzelman WR, etal. Negotiation-based protocols for disseminating information in wireless sensor networks[J]. Wireless Networks, 2002,8(8): 169-185.
28. Lindsey S, Raghavendra CS. PEGASIS: Power-Efficient gathering in sensor information systems. In: Proc of the IEEE Aerospace Conf. Montana: IEEE Aerospace and Electronic Systems Society. 2002, 1125-1130.
29. R. Shah and J. Rabaey, Energy Aware Routing for Low Energy Ad Hoc Sensor Networks[C]. In the Proceedings of the IEEE Wireless Communications and Networking Conference(WCNC), Orlando, FL, March, 2002: 350-355.
30. Intanagonwiwat C, Govindan R, Estrin D. Directed diffusion for wireless sensor networking[J]. IEEE/ACM Transactions. On Networking, 2003, 11(1):2-16.
31. Braginsky D, Estrin D, Rumor routing algorithm for sensor networks[C].In Proceeding of the 1st workshop on sensor networks and applications. Atlanta: ACM Press, 2002:22-31.
32. Yan Yu, Ramesh Govindan, Deborah Estrin. Geographical and Energy Aware Routing: a Recursive Data Dissemination Protocol for Wireless Sensor Networks[R]. UCLA Computer Science Department Technical Report, UCLA-CSD TR-01-0023, May 2001.
33. Newsom J, Song D. GEM: Graph Embedding for Routing and Data-centric Storage in Sensor Networks without Geographic Information[C].In Proceeding of 1st ACM Conference on Embedded Networked Sensor Systems, Redwood, CA. November 2003: 76-88.
34. Sohrabi K, Gao J, Ailawadhi, etal. Protocols for self-organization of a wireless sensor network[J]. IEEE Personal Communications, October 2000,7(5):16-27.
35. Rugin R, Mazzini G. A Simple and Efficient MAC-routing Integrated Algorithm for Sensor Network[C].In Proceeding of IEEE International Conference of Communications2004,6:3499-3503.
36. Rabunal J R, Dorado J. Artificial Neural Networks in Real-Life Applications. London: Idea Group Publishing, 2006.
37. GOLDBERGDE.Genetic algorithms in search optimization and machine learning. Addison-Wesley Longman Press, Boston,1989.
38. Kennedy J, Eberhart R. Particle swarm optimization.Proceedings of the IEEE international conference on neural networks. Piscataway, NJ: IEEE Service Center, 1995:1942-1948.
39. James K, Russell C, Eberhart, with Yuhui Shi. Swarm Intelligence. New York: Morgan kaufmann Publishers, 2001.
40. Von N. Theory of self-Reproduction Automata. Urbana, IL: University of Illinois Press, 1966.
41. Dorigo M, Maniezzo V, Colorni A. The ant system: Optimization by a colony of cooperating agents[J]. IEEE Transaction on Systems Man and Cybernatic, 1996, 26(1): 29-41.
42. Stutzle T,Hoos H. Max-Min ant systems[J]. Future Generation Computer Systems, 2000, 16(19):889-914.
43.陈崚,沈洁,秦玲,陈宏建.基于分布均匀度的自适应蚁群算法[J].软件学报. 2003, 4(08):1370-1387.
44.高玮.新型智能仿生模型---蚁群模型.智能系统学报,2008,3(3): 270-278.
45.段海滨,张祥银,徐春芳.仿生智能计算[M].科学出版社. 2011.1.
46.闭应洲,丁立新,陆建波.基于免疫修复的快速蚁群优化算法[J].控制与决策.2010,24(10):1509-1512.
47.杨磊,于舒娟.基于精英策略的逆向蚁群优化盲检测算法[J].计算机技术与发展. 2010,20(12):90-93.
48.彭沛夫,林亚平,胡斌,张桂芳.基于遗传因子的自适应蚁群算法最优PID控制[J].电子学报. 2006,34(6):1109-1113.
49. Cai Zhaoquan, Huang Han.Ant colony optimization algorithm based on adaptive weight and volatility parameters[C].Shang-Hai:IEEE Press,2008:75-79.
50.于海滨,曾鹏等编著.智能无线传感器网络系统[M].科学出版社,2006.9.
51. Akyildizif, Suw, Sankarasubramaniamy, etal. A Survey on Sensor Networks[J]. IEEE Communications Magazine, August 2002, 40(8):102-114.
52. Singh G, Das S,Gosavisv,etal. Ant colony algorithms for steiner trees:an application to routing in sensor networks[C]. Recent Developments in Biologically Inspired Computing,2003:183-206.
53.苏淼,钱海,王煦法.基于蚁群的无线传感器网络双簇头算法[J].计算机工程,2008,34(13):174—176.
54.张秋余,彭铎,刘洪国.基于能量的无线传感器网络分簇路由算法[J].计算机应用研究,2009,26(2):674-676.
55. Kassabalidis I, EI-Sharkawi MA, Marks RJ. Swarm intelligence for routing in communication networks[J]. Global Telecommunications Conference, 2001.6(6):3613-3617.
56. Heirtzelman W, Chandrakasan A, Balakrishnan H. An Application specific Protocol Architecture for Wireless Microsensor Networks[J]. IEEE Transactions on Wireless Communications, 2002,1(4):660-670.
57. WOO A, TONG T, CULLER D. Taming the underlying challenges of reliable multi-hop routing in sensor networks[A]. Proceedings of the 3 rd ACM Conference on Embedded Networked Sensor Systems[C]. 2003:14-27.
58.吴臻,金心宇.无线传感器网络的LEACH算法的改进[J].传感技术学报. 2006, 2,19(1):34-36.
2. Arici T, Altunbasak Y. Adaptive Sensing for Environment Monitoring Using Wireless Sensor Networks[C]. In Proceeding of the IEEE Wireless Communications and Networking Conference(WCNC), 2004,5(1): 2350-2355.
3.赵泽,崔莉.一种基于无线传感器网络的远程医疗监护系统[J].信息与控制,2006, 35(2): 265-269.
4. Meyer S, Rakotonirainy A. A Survey of Research on Context-aware Homes[C]. Workshop on Wearable, Invisible, Context-aware, Ambient, Pervasive and Ubiquitous Computing, 2003, 1: 159-168.
5. Coleri S, Cheung S Y, Varaiya P. Sensor Networks for Monitoring Traffic[C]. In Proceeding of Forty Second Annual Allerton Conference on Commuinication, Control and Computing, U. of Illinois, September 2004: 1-10.
6. Lacoss R, Walton R. Strawman Design for a DSN to Detect and Track Low Flying Aircraft[C]. In Proceeding of Distributed Sensor Nets Conference, 1978: 41-52.
7. Byrne J A. 21 Ideas for the 21st Century. Business Week, 1999,8:78-167.
8. 10 Emerging Technologies that Will Change the World. Technology Review, 2003,106(1):33-49.
9. Heinzelman WR, Chandrakasan A,Balakrishnan H.An application specific protocol architecture for wireless micro-sensor networks[J] . IEEE Transaction on Wireless Communications, 2002,1(4): 660-670.
10. Lindsev S, Raghavendra C S. Sivalingam K. Data gathering in sensor networks using the energy delay metric[C], in: Proceedings of the IPDPS Workshop on Issues in Wireless Networks and Mobile Computing, San Francisco, CA, April 2001.
11.中国科学院上海微系统与信息技术研究所科研成果[EB/OL], http://www.sim.ac.cn/xwzx/zhxw/200710/t20071022_1908127.html, 2007-10-22.
12. Colorni A, Dorigo M, Maniezzo V. Distributed optimization by ant colonies[C]. Prceeding of the First European Conference on Artificial Life. Paris France:Elsevier Publishing, 1991:134-142.
13.任丰原,黄海宁,林闯.无线传感器网络[J].软件学报, 2003, 14(8): 1281-1291.
14.孙雨耕,张静,孙永进等.无线自组传感器网络[J].传感技术学报,2004,2(20): 331-348.
15. Federal Agencies Need to Improve Controls over Wireless Networks[R]. United States Government Accountability Office, 2005,5.
16.于海滨,曾鹏,王中峰等.分布式无线传感器网络通信协议研究[J].通信学报,2004,25(10): 102-110.
17. Woo A, Culler D E. A Transmission Control Scheme for Media Access in Sensor Networks [A]. Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM′01 [C]. Rome (Italy), 2001. New York (NY, USA): ACM Press,2001.221—235.
18. TinyOS[OL]. http://www.tinyos.net.
19. Younis O,Fahmy S.Distributed Clustering in Ad-Hoe Sensor Networks:A Hybrid,Energy-Efficient Approach[C] . In proceeding of the IEEE Conference On Computer Communications(INFOCOM),Mar.2004:629—640.
20.黄少奕,曹阳,王悦伟.无线传感器网络中的路由技术[J].计算机工程与应用,2004.19:12-128.
21.唐宏,谢静等.无线传感器网络原理及应用[M].人民邮电出版社,2010.8.
22. Bao L, Garcia-Luna-Aceves J. Topology Management in Ad Hoc Networks[J]. In Proceeding of the 4th ACM International Symposium on Mobile Ad Hoc Networking & Computing, June 2003: 129– 140.
23. Tubaishat M, Madria S. Sensor Networks: An Overview[J]. IEEE Potentials, 2003, 22(2): 20-23.
24. Cullar D, Estrin D, Strvastava M. Overview of Sensor Network[J]. Computer, 2004, 37(8): 41-49.
25. Al-Karaki J N, karmal A E. Routing Techniques in Wireless Sensor Networks: A Survey[J]. IEEE Personal Communications, 2004, 11(6): 6-28.
26. Hedetniemi S, Liestman A. A Survey of Gossiping and Broadcasting in Communication Networks[J]. 1998, 18(4): 319-349.
27. Kulik J, Heinzelman WR, etal. Negotiation-based protocols for disseminating information in wireless sensor networks[J]. Wireless Networks, 2002,8(8): 169-185.
28. Lindsey S, Raghavendra CS. PEGASIS: Power-Efficient gathering in sensor information systems. In: Proc of the IEEE Aerospace Conf. Montana: IEEE Aerospace and Electronic Systems Society. 2002, 1125-1130.
29. R. Shah and J. Rabaey, Energy Aware Routing for Low Energy Ad Hoc Sensor Networks[C]. In the Proceedings of the IEEE Wireless Communications and Networking Conference(WCNC), Orlando, FL, March, 2002: 350-355.
30. Intanagonwiwat C, Govindan R, Estrin D. Directed diffusion for wireless sensor networking[J]. IEEE/ACM Transactions. On Networking, 2003, 11(1):2-16.
31. Braginsky D, Estrin D, Rumor routing algorithm for sensor networks[C].In Proceeding of the 1st workshop on sensor networks and applications. Atlanta: ACM Press, 2002:22-31.
32. Yan Yu, Ramesh Govindan, Deborah Estrin. Geographical and Energy Aware Routing: a Recursive Data Dissemination Protocol for Wireless Sensor Networks[R]. UCLA Computer Science Department Technical Report, UCLA-CSD TR-01-0023, May 2001.
33. Newsom J, Song D. GEM: Graph Embedding for Routing and Data-centric Storage in Sensor Networks without Geographic Information[C].In Proceeding of 1st ACM Conference on Embedded Networked Sensor Systems, Redwood, CA. November 2003: 76-88.
34. Sohrabi K, Gao J, Ailawadhi, etal. Protocols for self-organization of a wireless sensor network[J]. IEEE Personal Communications, October 2000,7(5):16-27.
35. Rugin R, Mazzini G. A Simple and Efficient MAC-routing Integrated Algorithm for Sensor Network[C].In Proceeding of IEEE International Conference of Communications2004,6:3499-3503.
36. Rabunal J R, Dorado J. Artificial Neural Networks in Real-Life Applications. London: Idea Group Publishing, 2006.
37. GOLDBERGDE.Genetic algorithms in search optimization and machine learning. Addison-Wesley Longman Press, Boston,1989.
38. Kennedy J, Eberhart R. Particle swarm optimization.Proceedings of the IEEE international conference on neural networks. Piscataway, NJ: IEEE Service Center, 1995:1942-1948.
39. James K, Russell C, Eberhart, with Yuhui Shi. Swarm Intelligence. New York: Morgan kaufmann Publishers, 2001.
40. Von N. Theory of self-Reproduction Automata. Urbana, IL: University of Illinois Press, 1966.
41. Dorigo M, Maniezzo V, Colorni A. The ant system: Optimization by a colony of cooperating agents[J]. IEEE Transaction on Systems Man and Cybernatic, 1996, 26(1): 29-41.
42. Stutzle T,Hoos H. Max-Min ant systems[J]. Future Generation Computer Systems, 2000, 16(19):889-914.
43.陈崚,沈洁,秦玲,陈宏建.基于分布均匀度的自适应蚁群算法[J].软件学报. 2003, 4(08):1370-1387.
44.高玮.新型智能仿生模型---蚁群模型.智能系统学报,2008,3(3): 270-278.
45.段海滨,张祥银,徐春芳.仿生智能计算[M].科学出版社. 2011.1.
46.闭应洲,丁立新,陆建波.基于免疫修复的快速蚁群优化算法[J].控制与决策.2010,24(10):1509-1512.
47.杨磊,于舒娟.基于精英策略的逆向蚁群优化盲检测算法[J].计算机技术与发展. 2010,20(12):90-93.
48.彭沛夫,林亚平,胡斌,张桂芳.基于遗传因子的自适应蚁群算法最优PID控制[J].电子学报. 2006,34(6):1109-1113.
49. Cai Zhaoquan, Huang Han.Ant colony optimization algorithm based on adaptive weight and volatility parameters[C].Shang-Hai:IEEE Press,2008:75-79.
50.于海滨,曾鹏等编著.智能无线传感器网络系统[M].科学出版社,2006.9.
51. Akyildizif, Suw, Sankarasubramaniamy, etal. A Survey on Sensor Networks[J]. IEEE Communications Magazine, August 2002, 40(8):102-114.
52. Singh G, Das S,Gosavisv,etal. Ant colony algorithms for steiner trees:an application to routing in sensor networks[C]. Recent Developments in Biologically Inspired Computing,2003:183-206.
53.苏淼,钱海,王煦法.基于蚁群的无线传感器网络双簇头算法[J].计算机工程,2008,34(13):174—176.
54.张秋余,彭铎,刘洪国.基于能量的无线传感器网络分簇路由算法[J].计算机应用研究,2009,26(2):674-676.
55. Kassabalidis I, EI-Sharkawi MA, Marks RJ. Swarm intelligence for routing in communication networks[J]. Global Telecommunications Conference, 2001.6(6):3613-3617.
56. Heirtzelman W, Chandrakasan A, Balakrishnan H. An Application specific Protocol Architecture for Wireless Microsensor Networks[J]. IEEE Transactions on Wireless Communications, 2002,1(4):660-670.
57. WOO A, TONG T, CULLER D. Taming the underlying challenges of reliable multi-hop routing in sensor networks[A]. Proceedings of the 3 rd ACM Conference on Embedded Networked Sensor Systems[C]. 2003:14-27.
58.吴臻,金心宇.无线传感器网络的LEACH算法的改进[J].传感技术学报. 2006, 2,19(1):34-36.