基于改进粒子群算法的Ad Hoc网络移动模型研究
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摘要
Ad Hoc网络是一种自组织,无中心的无线移动网络。由于它的建立不需要固定的通信基础设施,且具有较强的鲁棒性和抗毁性,故其广泛应用于各种要求临时通信的场合。路由协议是Ad Hoc网络数据成功传输的重要保障。移动模型是研究路由算法的基础,它的设计会直接影响路由协议的性能和稳定性,因此有效和贴近真实场景的移动模型设计是十分必要的。
粒子群算法(Particle Swarm Optimization,PSO)是根据鸟群如何寻找食物这种模型提出的算法,在解决工程问题中有广泛的应用。粒子群算法有实际场景基础,基于粒子群算法建立的网络移动模型能有效的模拟生活中各种智能追踪模型,如森林中野生动物的寻找、飞机作战时目标物体的寻找和警察抓小偷等,可用于评估路由协议。为了能够构建贴近真实运动的移动节点,本文结合牛顿第一定律和ad hoc网络的实际环境因素,对粒子群算法进行改进,并基于改进的粒子群算法建立一种新移动模型,主要工作如下:
(1)根据Ad Hoc网络移动模型的实际情况,改进了粒子群算法。在改进的粒子群算法中,利用加速度和最大加速度对基本粒子群算法的速度和位移更新公式进行改进,使节点移动的速度大小和方向不存在突变问题;通过设置最大移动速度来限制各种移动节点速度的无限变大;引入环境因子来模拟环境受地形和天气的影响。
(2)建立了包含障碍物模型、速度初始化函数和无边界仿真区域的改进粒子群算法移动模型。当节点移动到障碍物附近,新模型会依照一定的规律绕开障碍物继续对目标进行寻找,并利用合理的概率公式对新移动模型中节点的初始速度进行初始化。
(3)在MATLAB仿真工具上验证了改进粒子群算法的有效性。在OPNET网络仿真软件上用AODV(Ad-Hoc On-Demand Distance Vector Routing Protocol,距离矢量路由协议)协议对基于改进PSO的移动模型和经典的随机位点移动模型进行仿真。结果表明,在相同网络场景下,AODV协议在新模型的端到端时延、协议开销和包发送成功率等指标均优于在随机位点移动模型上的对应指标,证实了新移动模型在Ad Hoc网络中具有实际应用价值。
Ad Hoc network is a type of wireless mobile network of self-organization and no center. Its construction does not need the fixed communication infrastructure. Additionally it possesses the character of strong robustness and destroy-resistance. So it is widely used in various environments requesting temporary communication. Routing protocols are important guarantee of the successful transmission of data packets and the mobility model is basic for the researches of routing protocols in ad hoc network. Since the design of the mobility model directly affects the performance and the stabilitiy of the protocol, it is neccassary to design a realitic mobility model.
Swarm intelligence optimization is a kind of bionic optimization algorithm, which has been being swiftly developed in these years. The proposed model here belongs to traces and can be applied to imitate these scenes, such as wild animals in forest and airplanes in an air battle. It is helpful to the evaluation of the routing protocol. To mimic the movement of the nodes, we improved the basic PSO algorithm using accelartioin and environmental varibles and then established a new mobility model based on the improved PSO alogrithm. The main tasks of the thesis are shown as follows:
(1) According to the actual mobility model in ad hoc, we improved the PSO algorithm. In the improved PSO alogrithm we solved the abrupt change of speed and make it suitable to be used in mobility model of ad hoc network, this paper introduced the conception of acceleration and environmental variable into the updating formulas of the velocity and displacement of the PSO algorithm, and proposed to decompose its velocity and the displacement under rectangular coordinate system
(2) We established a new mobility model based on the improved PSO algorithm, which included the obstacle model, the velocity initialization function and the boundless area. When the nodes come to an obstacle, it will round the obstacle according to some rules. We also initialize the distribution for the nodes.
(3) We have proved the validity of the imoroved PSO algorithm in Matlab. To testify the actual application of the new mobility model, we made experiments in OPNET, which shows the performance parameters of the ad hoc network, such as the packet delivery rate、average end-to-end delay、throughput and network load, under the new mobility model were significantly better than the random waypoint mobility model (RWM).
粒子群算法(Particle Swarm Optimization,PSO)是根据鸟群如何寻找食物这种模型提出的算法,在解决工程问题中有广泛的应用。粒子群算法有实际场景基础,基于粒子群算法建立的网络移动模型能有效的模拟生活中各种智能追踪模型,如森林中野生动物的寻找、飞机作战时目标物体的寻找和警察抓小偷等,可用于评估路由协议。为了能够构建贴近真实运动的移动节点,本文结合牛顿第一定律和ad hoc网络的实际环境因素,对粒子群算法进行改进,并基于改进的粒子群算法建立一种新移动模型,主要工作如下:
(1)根据Ad Hoc网络移动模型的实际情况,改进了粒子群算法。在改进的粒子群算法中,利用加速度和最大加速度对基本粒子群算法的速度和位移更新公式进行改进,使节点移动的速度大小和方向不存在突变问题;通过设置最大移动速度来限制各种移动节点速度的无限变大;引入环境因子来模拟环境受地形和天气的影响。
(2)建立了包含障碍物模型、速度初始化函数和无边界仿真区域的改进粒子群算法移动模型。当节点移动到障碍物附近,新模型会依照一定的规律绕开障碍物继续对目标进行寻找,并利用合理的概率公式对新移动模型中节点的初始速度进行初始化。
(3)在MATLAB仿真工具上验证了改进粒子群算法的有效性。在OPNET网络仿真软件上用AODV(Ad-Hoc On-Demand Distance Vector Routing Protocol,距离矢量路由协议)协议对基于改进PSO的移动模型和经典的随机位点移动模型进行仿真。结果表明,在相同网络场景下,AODV协议在新模型的端到端时延、协议开销和包发送成功率等指标均优于在随机位点移动模型上的对应指标,证实了新移动模型在Ad Hoc网络中具有实际应用价值。
Ad Hoc network is a type of wireless mobile network of self-organization and no center. Its construction does not need the fixed communication infrastructure. Additionally it possesses the character of strong robustness and destroy-resistance. So it is widely used in various environments requesting temporary communication. Routing protocols are important guarantee of the successful transmission of data packets and the mobility model is basic for the researches of routing protocols in ad hoc network. Since the design of the mobility model directly affects the performance and the stabilitiy of the protocol, it is neccassary to design a realitic mobility model.
Swarm intelligence optimization is a kind of bionic optimization algorithm, which has been being swiftly developed in these years. The proposed model here belongs to traces and can be applied to imitate these scenes, such as wild animals in forest and airplanes in an air battle. It is helpful to the evaluation of the routing protocol. To mimic the movement of the nodes, we improved the basic PSO algorithm using accelartioin and environmental varibles and then established a new mobility model based on the improved PSO alogrithm. The main tasks of the thesis are shown as follows:
(1) According to the actual mobility model in ad hoc, we improved the PSO algorithm. In the improved PSO alogrithm we solved the abrupt change of speed and make it suitable to be used in mobility model of ad hoc network, this paper introduced the conception of acceleration and environmental variable into the updating formulas of the velocity and displacement of the PSO algorithm, and proposed to decompose its velocity and the displacement under rectangular coordinate system
(2) We established a new mobility model based on the improved PSO algorithm, which included the obstacle model, the velocity initialization function and the boundless area. When the nodes come to an obstacle, it will round the obstacle according to some rules. We also initialize the distribution for the nodes.
(3) We have proved the validity of the imoroved PSO algorithm in Matlab. To testify the actual application of the new mobility model, we made experiments in OPNET, which shows the performance parameters of the ad hoc network, such as the packet delivery rate、average end-to-end delay、throughput and network load, under the new mobility model were significantly better than the random waypoint mobility model (RWM).
引文
[1]陈林星.移动Ad Hoc网络——自组织分组无线网络技术.北京:电子工业出版社,2006,4-10
[2] T. Camp, J. Boleng, V. Davies. A survey of mobility models for Ad Hoc network research. Wireless Communication & Mobile Computing (WCMC), 2002, 2(5):483-502
[3]郑相全.无线自组网技术实用教程.北京:清华大学出版社,2004,7-12
[4]黄浩.移动Ad Hoc网络中基于指定群首的分层路由协议设计:[硕士学位论文].成都:电子科技大学,2007
[5]周武旸.无线Internet技术.北京:人民邮电出版社,2006,305-306
[6]陈林星.移动Ad Hoc网络.北京:国防工业出版社,2004,20-22
[7]藤晓菲,张元亭.移动医疗:穿戴式医疗仪器的发展趋势,中国医疗器械杂志, 2006, 30:330-340
[8] A. E. Gallad, M. E. Hawary, A. Sallam. Enhancing the particle swarm optimizer via proper parameters selection. Canadian Conference on Electrical and Computer Engineering,Vol.2:792-797
[9] M. Lovbjerg, T. Krink. Extending particle swarm opimizers with self-organized criticality. The 4th IEEE Congress on Evolutionary Computation (2002,Hawaii),Vol.1:200-208
[10] R. Mendes. Population topologies and their influence in particle swarm performance: [PHD thesis]. Minho: University of Minho,2004
[11] K. Kassabalidis, I. E. Sharkawi. Adaptive-SDR: Adaptive swarm-based distributed routing. 2002 International Joint Conference on Neural Networks,Vol.1:351-354
[12] R. C. Eberhart. Human tremor analysis using particle swarm optimization. 1999 Congress on Evolutionary Computation (Washingon DC),Vol.1:1927-1930
[13] A. P. Engelbrecht, A. Ismail. Training product unit neural networks. Stability and Control: Theory and Applications, 1999, 2(1~2):59-74
[14] H. A. Yoshida. Particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Transactions on Power Systems, 2000, 15(1):1232-1239
[15] M. Clerc. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. 1999 Congress on Evolutionary Computation,Vol.1:1951-1957
[16] M. Wachowiak, R. Smolikova, Y. Zheng, et al. An approach to multimodal biomedical image registration utilizing particle swarm optimization. IEEE Transactions on Evolutionary Computation, 2004, 8(3):289-301
[17] Z. Zaharis, D. Kampitaki, A. Papastergiou. Optimal design of a linear antenna array under the restriction of uniform excitation distribution using a particle swarm optimization based method. WSEAS Transactions on Communications, 2007, 6(1):52-59
[18] D. Johnson, D. Maltz. Dynamic source routing in ad hoc wireless networks. Boston: Kluwer Academic Publishers, 1996: 153-181
[19] D. N. Alparslan, K. Sohraby. A generalized random mobility model for wireless ad hoc networks and its analysis: one-dimensional case. IEEE Transaction on Networking, 2007, 15(3): 602-615
[20] D. N. Alparslan, K. Sohraby. Two-Dimensional modeling and analysis of generalized random mobility models for wireless ad hoc networks. IEEE Transaction on Networking, 2007, 15(3): 616-629
[21] E. M. Royer, P. M. M. Smith, L. E. Moser. An analysis of the optimum node density for ad hoc mobile networks. In Proceedings of the IEEE International Conference on Communications. Helsinki: IEEE Press, 2001: 857– 861
[22] V. Tolety. Load reduction in ad hoc networks using mobile servers:[PHD thesis]. Colorado: Colorado School of Mines, 1999
[23] V. Davies. Evaluating mobility models within an ad hoc network:[PHD thesis]. Colorado: Colorado School of Mines, 2000
[24] X. Hong, M. Gerla, G. Pei, C. Chiang. A group mobility model for ad hoc wireless networks. In Proceedings of the ACM International Workshop in Modeling and Simulation of Wireless and Mobile Systems. Seattle: ACM Press, 1999: 53-60
[25] J. M. Ng, Y. Zhang. A mobility model with group partitioning for wireless ad hoc networks. The International Conference on Information Technology and Applications. Sydney: IEEE Press, 2005: 289-294
[26] C. L. Hsien, W. T. Yi, H Shih, etal. Ant mobility model platform for network simulation. Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC’04). Las Vegas: IEEE Press, 2004: 380-384
[27] H. S. Lope, L. S. Coelho. Particle swarn optimization with fast local search for the blind traveling salesman problem. 2005 HIS’05 Fifth International Conference on Hybrid Intelligent Systems,Vol.1:245-250
[28] J. Stender, L. Brainware. Introduction to genetic algorithms. IEEE Colloquium on Applications of Genetic Algorithms, 15 Mar 1994:1-4
[29] J. Kennedy, R. Eberhart. Particle Swarm Optimization. IEEE International Conference on Neural Networks. Perth: IEEE Press, 1995: 1942-1948
[30] C. Bettstetter. Smooth is better than sharp: a random mobility model for simulation of wireless networks. Proceedings of the 4th ACM international workshop on Modeling, analysis and simulation of wireless and mobile systems. Rome: ACM Press, 2001: 19-27
[31]付国江,王少梅,等.改进的速度变异粒子群算法.计算机工程与应用,2006,13: 48-51
[32] B. Christian, H. Hannes, P. C. Xavier. Stochastic properties of the random waypoint mobility model. ACM/Kluwer Wireless Networks:Special Issue on Modeling and Analysis of Mobile Networks. 2004, 10 (5):555-567
[33] B. Christian, H. Hannes, P. C. Xavier. Stochastic properties of the random waypoint mobility model: epoch length, direction distribution, and cell change rate. The 5th ACM Int’1 Workshop on Modeling, Analysis, and Simulation of Wireless and Mobile Systems, Atlanta, USA, 2005: 7-14
[34]赵金晶,朱培栋.Ad Hoc网络移动模型及其应用.计算机工程与科学. 2005, 27 (5): 15-17
[35] L. Jian, L. Pmohapatra. Location aided knowledge extraction routing for mobile ad hoc networks. Wireless Communications and Networking, Calnornia, USA, 2003:1180一1184
[36] H. Dcengel, T. CBarret, Morin. A spatial analysis of mobility models: application to wireless ad hoc network simulation. Simulation Symposium Proceedings, New York, USA, 2004:35-42
[37] J. Broch, D. Maltz, D. Johnson, Y. Hu, and J.Jetcheva. Multi-hop wireless ad hoc network routing protocols. In Proceedings of the Joint Conference of the IEEE Computer and Communications Societies. San Francisco:IEEE press,1998: 85-97
[38] C.Chiang and M.Gerla. On-demad multicast in mobile wireless networks. In Proceedings of the IEEE international Conferencce on Network Protocols,1998
[39] J.J. Carcia-Luna_Aceves and E.L. Madrga. A multicast routing protocol for and an hoc networks. In Proceedings of the Joint Conference of the IEEE Computer and Communications Societies. 1999:784-792
[40] P. Johansson, T. Larsson, N.Hedman, B. Mielczarek, and M.Degermark. Routing protocols for ad hoc networks. In Proceedings of the ACM/IEEE Inernational Conference on Mobile Computing and Networking. 1999:192-206
[41] Y.Ko and N.H. vaidya. Location-aided routing(LAR) in mobile ad hoc network. In Proceedings of the ACM/IEEE Inernational Conference on Mobile Computing and Networking. 1998:66-75
[42] C. Bettstetter, Giovanni Resta, Paolo Santi. The node distribution of the Random Waypoint mobility model for wireless ad hoc networks. IEEE Trans. Mobile Computing, 2003, 3(2): 257-269
[43] C. Bettstetter, C. Wanger. The spatial node distribution of the Random Waypoint mobility model. The 1st German Workshop on Mobile ad hoc networks. Ulm, Germany, 2002
[44]刘元安,唐碧华,胡月梅. Ad hoc网络中的路由算法.北京邮电大学学报,2004, 27(2): 1-7
[45]桂超,孙宝林,玉虹.无线移动Ad Hoc网络的路由技术研究.武汉科技学院学报.2003, 16(4): 43-46
[46]张禄林,李承恕.MANET路由选择协议的比较分析研究.电子学报2000, 28 (11) : 88-92
[47]陈敏.OPNET网络仿真.广州:志必得工作室,2004
[48] A. Boukerche. A simulation based study of on-demand routing protocols for ad hoc wireless networks. 2001 International Simulation Symposium,Vol.1:22-26
[49] A Jardosh, P B Royer, etc. Towards realistic mobility models for mobile ad hoc networks . Proceedings of the 9th annual international conference on Mobile computing and networking . San Diego: ACM Press, 2003:217– 229
[50] L. Tao, S. F. Midkiff, J. S. Park. A framework for wireless ad hoc routing protocols. IEEE Wireless Communication and Networking, 2003, 2(20):1162-1167
[51] X. H. Hu, Y. Liu, L. Myung. Design and evaluation of a region-based ad hoc routing protocol.2004 IEEE Military Communications Conference,Vol.3:1275-128
[2] T. Camp, J. Boleng, V. Davies. A survey of mobility models for Ad Hoc network research. Wireless Communication & Mobile Computing (WCMC), 2002, 2(5):483-502
[3]郑相全.无线自组网技术实用教程.北京:清华大学出版社,2004,7-12
[4]黄浩.移动Ad Hoc网络中基于指定群首的分层路由协议设计:[硕士学位论文].成都:电子科技大学,2007
[5]周武旸.无线Internet技术.北京:人民邮电出版社,2006,305-306
[6]陈林星.移动Ad Hoc网络.北京:国防工业出版社,2004,20-22
[7]藤晓菲,张元亭.移动医疗:穿戴式医疗仪器的发展趋势,中国医疗器械杂志, 2006, 30:330-340
[8] A. E. Gallad, M. E. Hawary, A. Sallam. Enhancing the particle swarm optimizer via proper parameters selection. Canadian Conference on Electrical and Computer Engineering,Vol.2:792-797
[9] M. Lovbjerg, T. Krink. Extending particle swarm opimizers with self-organized criticality. The 4th IEEE Congress on Evolutionary Computation (2002,Hawaii),Vol.1:200-208
[10] R. Mendes. Population topologies and their influence in particle swarm performance: [PHD thesis]. Minho: University of Minho,2004
[11] K. Kassabalidis, I. E. Sharkawi. Adaptive-SDR: Adaptive swarm-based distributed routing. 2002 International Joint Conference on Neural Networks,Vol.1:351-354
[12] R. C. Eberhart. Human tremor analysis using particle swarm optimization. 1999 Congress on Evolutionary Computation (Washingon DC),Vol.1:1927-1930
[13] A. P. Engelbrecht, A. Ismail. Training product unit neural networks. Stability and Control: Theory and Applications, 1999, 2(1~2):59-74
[14] H. A. Yoshida. Particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Transactions on Power Systems, 2000, 15(1):1232-1239
[15] M. Clerc. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. 1999 Congress on Evolutionary Computation,Vol.1:1951-1957
[16] M. Wachowiak, R. Smolikova, Y. Zheng, et al. An approach to multimodal biomedical image registration utilizing particle swarm optimization. IEEE Transactions on Evolutionary Computation, 2004, 8(3):289-301
[17] Z. Zaharis, D. Kampitaki, A. Papastergiou. Optimal design of a linear antenna array under the restriction of uniform excitation distribution using a particle swarm optimization based method. WSEAS Transactions on Communications, 2007, 6(1):52-59
[18] D. Johnson, D. Maltz. Dynamic source routing in ad hoc wireless networks. Boston: Kluwer Academic Publishers, 1996: 153-181
[19] D. N. Alparslan, K. Sohraby. A generalized random mobility model for wireless ad hoc networks and its analysis: one-dimensional case. IEEE Transaction on Networking, 2007, 15(3): 602-615
[20] D. N. Alparslan, K. Sohraby. Two-Dimensional modeling and analysis of generalized random mobility models for wireless ad hoc networks. IEEE Transaction on Networking, 2007, 15(3): 616-629
[21] E. M. Royer, P. M. M. Smith, L. E. Moser. An analysis of the optimum node density for ad hoc mobile networks. In Proceedings of the IEEE International Conference on Communications. Helsinki: IEEE Press, 2001: 857– 861
[22] V. Tolety. Load reduction in ad hoc networks using mobile servers:[PHD thesis]. Colorado: Colorado School of Mines, 1999
[23] V. Davies. Evaluating mobility models within an ad hoc network:[PHD thesis]. Colorado: Colorado School of Mines, 2000
[24] X. Hong, M. Gerla, G. Pei, C. Chiang. A group mobility model for ad hoc wireless networks. In Proceedings of the ACM International Workshop in Modeling and Simulation of Wireless and Mobile Systems. Seattle: ACM Press, 1999: 53-60
[25] J. M. Ng, Y. Zhang. A mobility model with group partitioning for wireless ad hoc networks. The International Conference on Information Technology and Applications. Sydney: IEEE Press, 2005: 289-294
[26] C. L. Hsien, W. T. Yi, H Shih, etal. Ant mobility model platform for network simulation. Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC’04). Las Vegas: IEEE Press, 2004: 380-384
[27] H. S. Lope, L. S. Coelho. Particle swarn optimization with fast local search for the blind traveling salesman problem. 2005 HIS’05 Fifth International Conference on Hybrid Intelligent Systems,Vol.1:245-250
[28] J. Stender, L. Brainware. Introduction to genetic algorithms. IEEE Colloquium on Applications of Genetic Algorithms, 15 Mar 1994:1-4
[29] J. Kennedy, R. Eberhart. Particle Swarm Optimization. IEEE International Conference on Neural Networks. Perth: IEEE Press, 1995: 1942-1948
[30] C. Bettstetter. Smooth is better than sharp: a random mobility model for simulation of wireless networks. Proceedings of the 4th ACM international workshop on Modeling, analysis and simulation of wireless and mobile systems. Rome: ACM Press, 2001: 19-27
[31]付国江,王少梅,等.改进的速度变异粒子群算法.计算机工程与应用,2006,13: 48-51
[32] B. Christian, H. Hannes, P. C. Xavier. Stochastic properties of the random waypoint mobility model. ACM/Kluwer Wireless Networks:Special Issue on Modeling and Analysis of Mobile Networks. 2004, 10 (5):555-567
[33] B. Christian, H. Hannes, P. C. Xavier. Stochastic properties of the random waypoint mobility model: epoch length, direction distribution, and cell change rate. The 5th ACM Int’1 Workshop on Modeling, Analysis, and Simulation of Wireless and Mobile Systems, Atlanta, USA, 2005: 7-14
[34]赵金晶,朱培栋.Ad Hoc网络移动模型及其应用.计算机工程与科学. 2005, 27 (5): 15-17
[35] L. Jian, L. Pmohapatra. Location aided knowledge extraction routing for mobile ad hoc networks. Wireless Communications and Networking, Calnornia, USA, 2003:1180一1184
[36] H. Dcengel, T. CBarret, Morin. A spatial analysis of mobility models: application to wireless ad hoc network simulation. Simulation Symposium Proceedings, New York, USA, 2004:35-42
[37] J. Broch, D. Maltz, D. Johnson, Y. Hu, and J.Jetcheva. Multi-hop wireless ad hoc network routing protocols. In Proceedings of the Joint Conference of the IEEE Computer and Communications Societies. San Francisco:IEEE press,1998: 85-97
[38] C.Chiang and M.Gerla. On-demad multicast in mobile wireless networks. In Proceedings of the IEEE international Conferencce on Network Protocols,1998
[39] J.J. Carcia-Luna_Aceves and E.L. Madrga. A multicast routing protocol for and an hoc networks. In Proceedings of the Joint Conference of the IEEE Computer and Communications Societies. 1999:784-792
[40] P. Johansson, T. Larsson, N.Hedman, B. Mielczarek, and M.Degermark. Routing protocols for ad hoc networks. In Proceedings of the ACM/IEEE Inernational Conference on Mobile Computing and Networking. 1999:192-206
[41] Y.Ko and N.H. vaidya. Location-aided routing(LAR) in mobile ad hoc network. In Proceedings of the ACM/IEEE Inernational Conference on Mobile Computing and Networking. 1998:66-75
[42] C. Bettstetter, Giovanni Resta, Paolo Santi. The node distribution of the Random Waypoint mobility model for wireless ad hoc networks. IEEE Trans. Mobile Computing, 2003, 3(2): 257-269
[43] C. Bettstetter, C. Wanger. The spatial node distribution of the Random Waypoint mobility model. The 1st German Workshop on Mobile ad hoc networks. Ulm, Germany, 2002
[44]刘元安,唐碧华,胡月梅. Ad hoc网络中的路由算法.北京邮电大学学报,2004, 27(2): 1-7
[45]桂超,孙宝林,玉虹.无线移动Ad Hoc网络的路由技术研究.武汉科技学院学报.2003, 16(4): 43-46
[46]张禄林,李承恕.MANET路由选择协议的比较分析研究.电子学报2000, 28 (11) : 88-92
[47]陈敏.OPNET网络仿真.广州:志必得工作室,2004
[48] A. Boukerche. A simulation based study of on-demand routing protocols for ad hoc wireless networks. 2001 International Simulation Symposium,Vol.1:22-26
[49] A Jardosh, P B Royer, etc. Towards realistic mobility models for mobile ad hoc networks . Proceedings of the 9th annual international conference on Mobile computing and networking . San Diego: ACM Press, 2003:217– 229
[50] L. Tao, S. F. Midkiff, J. S. Park. A framework for wireless ad hoc routing protocols. IEEE Wireless Communication and Networking, 2003, 2(20):1162-1167
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