无线传感器网络路由及汇聚节点选址算法研究
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摘要
无线传感器网络(Wireless Sensor Networks, WSNs)是传感器技术、嵌入式计算、分布式处理和网络通信等技术相互结合的无线通信系统,在军事、医疗、环境、生物和工业等方面有广泛的应用。传感器节点通常是由不可充电的电池供电,因此能量消耗是网络设计过程中最受关注的问题之一。网络路由算法和汇聚节点选址算法是直接影响网络能耗大小的两个重要因素,根据网络中的剩余能量分布设计适应性强的路由协议以及选择合适的汇聚节点位置,将有效地减少网络能耗和延长网络寿命。
本文从WSNs的网络特性出发,提出了一个蚁群路由算法,并研究了节点随机分布的网络中汇聚节点的选址算法。论文的主要工作如下:
(1)本文分析了WSNs中路由的能耗特点,然后提出一种基于路由代价的蚁群路由算法。该路由算法一方面保证了网络选择较短的路径传送数据,另一方面能使路由绕过剩余能量较低的节点或者区域,使网络的剩余能量分布得到均衡。通过仿真实验可以看到,与其它构建方式相比,使用路由代价构建的路由算法可以较好地适应节点均匀和非均匀随机分布的传感器网络,同时实现降低网络能耗和改善能量的均衡分布,有效延长网络寿命。
(2)本文分别研究了单跳WSNs中面向能量的选址算法和面向寿命的选址算法,结合一阶无线模型(First Order Radio Model)可以分析得到,面向能量的选址算法中,汇聚节点的最佳位置就是使汇聚节点到传感器节点距离的二阶矩最小的位置,特别地,在凸监测区域中就是其重心位置;而面向寿命的选址算法中,汇聚节点的最佳位置是使网络中最长的通信距离最小化的位置。仿真数据验证了分析结果的准确性。
(3)本文还进一步研究了凸区域多跳WSNs中面向能量的选址算法和面向寿命的选址算法。多跳网络中通过源节点到汇聚节点的距离估算路由中的能量消耗,在面向能量的选址算法中,汇聚节点的最佳选址是使所有传感器节点到汇聚节点总距离的数学期望最小的位置;面向寿命的选址算法中,优化过程中除了考虑到各节点到汇聚节点的距离以外,还考虑了汇聚节点附近的节点密度分布,以延长网络中能耗最快区域的节点寿命。通过仿真显示,面向寿命的选址算法对应的能耗略大于面向能量的选址算法,但前者对应的网络寿命也较长。
(4)最后,本文还简单研究了非凸区域多跳WSNs的选址算法。首先,我们把非凸监测区域划分为网格图,然后提出以最短有效网格距离代替欧几里德距离对网络中的路由能耗进行估算,并使用迭代算法计算各格点到汇聚节点的总距离,最后把该迭代算法嵌入到选址算法中,得到汇聚节点的最佳位置。算法减少了求解最短距离过程的计算复杂度,与欧几里德距离相比,计算误差也得到减少。通过仿真实验分别对比了基于两种不同的距离计算方式的面向能量的选址算法和面向寿命的选址算法。仿真结果显示,使用最短有效网格距离的计算方法可以减少网络能耗和延长网络寿命。
Wireless sensor networks (WSNs) are wireless communication systems integrating sensor technology, embedded computing, distributed processing, and network communication technology, which have wide applications in military, medicine, environment, biology, and industry. Sensor nodes are usually powered by non-rechargeable batteries; so energy consumption is one of the most concerned topics in the network design. Routing algorithm and sink node placement algorithm are two important factors which have direct impacts on energy consumptions in WSNs. According to the residual energy distribution in the networks, an adaptive routing protocol is designed and the appropriate placement of the sink node is selected to reduce energy consumption and prolong network lifetime effectively.
Based on the characteristics of WSNs, we propose an ant routing algorithm and investigate the sink node placement algorithms in the networks with randomly distributed nodes. The major contributions in this paper are as follows:
(1) This paper first analyzes the characteristics of energy consumption of the routing in WSNs and proposes a routing-cost based ant routing algorithm. The algorithm ensures that the networks choose a shorter path to transmit data. Furthermore, it can make a detour to avoid the sensor nodes and sensing a region with low residual energy, so that the energy distribution can reaches a balance in the networks. The simulation results show that, compared with other construction methods, the algorithm constructed by routing-cost adapts very well to WSNs with sensor nodes in a uniform or non-uniform random distribution. It can both reduce energy consumption and improve the energy balance in WSNs. Therefore the network lifetime can be prolonged effectively.
(2) An energy-oriented placement algorithm and a lifetime-oriented placement algorithm are investigated in the single-hop WSNs. From the first-order radio model, we find that in the energy-oriented placement algorithm the optimal placement of the sink node is the place which minimizes the secong-order moment of the distance between the source nodes to the sink node, and particularly, the optimal placement is the center of gravity of the convex sensing region. This position minimizes the longest communication distance in the networks in the lifetime-oriented placement algorithm. Simulation data verify the accuracy of the simulation results.
(3) This paper then investigates an energy-oriented placement algorithm and a lifetime-oriented placement algorithm in the multi-hop WSNs in a convex region. In a multi-hop network, the energy consumption on the route can be estimated by the distance between the source nodes to the sink node. In the energy-oriented placement algorithm, the best placement of the sink node is on the position which minimizes the expectation of the total distance between all sensor nodes to the sink node. In the lifetime-oriented placement algorithm, we not only consider the total distance but also take the sensor density near the sink node into account. Therefore the sensor nodes in the area which consume energy fastest have the longest lifetime. Simulation results show that the networks with a lifetime-oriented algorithm consume energy faster, but have a longer lifetime.
(4) Finally the paper briefly studied the placement algorithms of the multi-hop WSNs with non-convex sensing region. The non-convex region is first divided into grids. We then use the shortest effective grid-distance instead of the Euclidean distance to estimate the energy consumption on the route. An iterative algorithm is used to calculate the total distance between all grids to the sink node and is eventually embedded into the sink node placement algorithm so that we can find the optimal placement. The algorithm reduces the computational complexity in the process of finding the shortest distance. Compared with the algorithm based on the Euclidean distance, the algorithm proposed in this paper has a smaller error. We simulated the networks with the energy-oriented and the lifetime-oriented placement algorithms based on the two distance-calculation methods, respectively. The results show that the algorithm based on the shortest effective grid-distance can reduce network energy consumption and prolong network lifetime.
本文从WSNs的网络特性出发,提出了一个蚁群路由算法,并研究了节点随机分布的网络中汇聚节点的选址算法。论文的主要工作如下:
(1)本文分析了WSNs中路由的能耗特点,然后提出一种基于路由代价的蚁群路由算法。该路由算法一方面保证了网络选择较短的路径传送数据,另一方面能使路由绕过剩余能量较低的节点或者区域,使网络的剩余能量分布得到均衡。通过仿真实验可以看到,与其它构建方式相比,使用路由代价构建的路由算法可以较好地适应节点均匀和非均匀随机分布的传感器网络,同时实现降低网络能耗和改善能量的均衡分布,有效延长网络寿命。
(2)本文分别研究了单跳WSNs中面向能量的选址算法和面向寿命的选址算法,结合一阶无线模型(First Order Radio Model)可以分析得到,面向能量的选址算法中,汇聚节点的最佳位置就是使汇聚节点到传感器节点距离的二阶矩最小的位置,特别地,在凸监测区域中就是其重心位置;而面向寿命的选址算法中,汇聚节点的最佳位置是使网络中最长的通信距离最小化的位置。仿真数据验证了分析结果的准确性。
(3)本文还进一步研究了凸区域多跳WSNs中面向能量的选址算法和面向寿命的选址算法。多跳网络中通过源节点到汇聚节点的距离估算路由中的能量消耗,在面向能量的选址算法中,汇聚节点的最佳选址是使所有传感器节点到汇聚节点总距离的数学期望最小的位置;面向寿命的选址算法中,优化过程中除了考虑到各节点到汇聚节点的距离以外,还考虑了汇聚节点附近的节点密度分布,以延长网络中能耗最快区域的节点寿命。通过仿真显示,面向寿命的选址算法对应的能耗略大于面向能量的选址算法,但前者对应的网络寿命也较长。
(4)最后,本文还简单研究了非凸区域多跳WSNs的选址算法。首先,我们把非凸监测区域划分为网格图,然后提出以最短有效网格距离代替欧几里德距离对网络中的路由能耗进行估算,并使用迭代算法计算各格点到汇聚节点的总距离,最后把该迭代算法嵌入到选址算法中,得到汇聚节点的最佳位置。算法减少了求解最短距离过程的计算复杂度,与欧几里德距离相比,计算误差也得到减少。通过仿真实验分别对比了基于两种不同的距离计算方式的面向能量的选址算法和面向寿命的选址算法。仿真结果显示,使用最短有效网格距离的计算方法可以减少网络能耗和延长网络寿命。
Wireless sensor networks (WSNs) are wireless communication systems integrating sensor technology, embedded computing, distributed processing, and network communication technology, which have wide applications in military, medicine, environment, biology, and industry. Sensor nodes are usually powered by non-rechargeable batteries; so energy consumption is one of the most concerned topics in the network design. Routing algorithm and sink node placement algorithm are two important factors which have direct impacts on energy consumptions in WSNs. According to the residual energy distribution in the networks, an adaptive routing protocol is designed and the appropriate placement of the sink node is selected to reduce energy consumption and prolong network lifetime effectively.
Based on the characteristics of WSNs, we propose an ant routing algorithm and investigate the sink node placement algorithms in the networks with randomly distributed nodes. The major contributions in this paper are as follows:
(1) This paper first analyzes the characteristics of energy consumption of the routing in WSNs and proposes a routing-cost based ant routing algorithm. The algorithm ensures that the networks choose a shorter path to transmit data. Furthermore, it can make a detour to avoid the sensor nodes and sensing a region with low residual energy, so that the energy distribution can reaches a balance in the networks. The simulation results show that, compared with other construction methods, the algorithm constructed by routing-cost adapts very well to WSNs with sensor nodes in a uniform or non-uniform random distribution. It can both reduce energy consumption and improve the energy balance in WSNs. Therefore the network lifetime can be prolonged effectively.
(2) An energy-oriented placement algorithm and a lifetime-oriented placement algorithm are investigated in the single-hop WSNs. From the first-order radio model, we find that in the energy-oriented placement algorithm the optimal placement of the sink node is the place which minimizes the secong-order moment of the distance between the source nodes to the sink node, and particularly, the optimal placement is the center of gravity of the convex sensing region. This position minimizes the longest communication distance in the networks in the lifetime-oriented placement algorithm. Simulation data verify the accuracy of the simulation results.
(3) This paper then investigates an energy-oriented placement algorithm and a lifetime-oriented placement algorithm in the multi-hop WSNs in a convex region. In a multi-hop network, the energy consumption on the route can be estimated by the distance between the source nodes to the sink node. In the energy-oriented placement algorithm, the best placement of the sink node is on the position which minimizes the expectation of the total distance between all sensor nodes to the sink node. In the lifetime-oriented placement algorithm, we not only consider the total distance but also take the sensor density near the sink node into account. Therefore the sensor nodes in the area which consume energy fastest have the longest lifetime. Simulation results show that the networks with a lifetime-oriented algorithm consume energy faster, but have a longer lifetime.
(4) Finally the paper briefly studied the placement algorithms of the multi-hop WSNs with non-convex sensing region. The non-convex region is first divided into grids. We then use the shortest effective grid-distance instead of the Euclidean distance to estimate the energy consumption on the route. An iterative algorithm is used to calculate the total distance between all grids to the sink node and is eventually embedded into the sink node placement algorithm so that we can find the optimal placement. The algorithm reduces the computational complexity in the process of finding the shortest distance. Compared with the algorithm based on the Euclidean distance, the algorithm proposed in this paper has a smaller error. We simulated the networks with the energy-oriented and the lifetime-oriented placement algorithms based on the two distance-calculation methods, respectively. The results show that the algorithm based on the shortest effective grid-distance can reduce network energy consumption and prolong network lifetime.
引文
[1]孙立民,李建中,陈瑜.无线传感器网络[M].北京:清华大学出版社. 2005.
[2] Heinzelman W.R., Kulik J., Balakrishnan H. Adaptive protocols for information dissemination in wireless sensor networks [C]. proceedings of the The Fifth Annual ACM/IEEE International Conference on Mobile Computing and Networking, Seattle, USA. 1999: 174-85.
[3] Intanagonwiwat C., Govindan R., Estrin D. Directed diffusion: A scalable and robust communication paradigm for sensor networks [C]. proceedings of the Sixth Annual International Conference on Mobile Computing and Networking, Boston, USA. 2000: 56-67.
[4] Sohrabi K., Gao J., Ailawadhi V., et al. Protocols for self-organization of a wireless sensor network [J]. IEEE Personal Communications, 2000, 7(5): 16-27.
[5] Braginsky D., Estrin D. Rumor routing algorthim for sensor networks [C]. proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications, Atlanta, USA. 2002: 22-31.
[6] Heinzelman W.R., Chandrakasan A., Balakrishnan H. Energy-efficient communication protocol for wireless microsensor networks [C]. proceedings of the 33rd Annual Hawaii International Conference on System Sciences, Maui, USA. 2000: 908-18.
[7] Manjeshwar A., Agrawal D.P. Teen: A routing protocol for enhanced efficiency in wireless sensor networks [C]. proceedings of the 15th International Parallel and Distributed Processing Symposium, San Francisco, USA. 2001: 2009 - 15.
[8] Lindsey S., Raghavendra C.S. Pegasis: Power-efficient gathering in sensor information systems [C]. proceedings of the IEEE Aerospace Conference Proceedings, Big Sky, USA. 2002: 1125-30.
[9] Teo J.-Y., Ha Y., Tham C.-K. Interference-minimized multipath routing with congestion control in wireless sensor network for high-rate streaming [J]. IEEE Transactions on Mobile Computing 2008, 7(9): 1124-37.
[10] Zabin F., Misra S., Woungang I., et al. Reep: Data-centric, energy-efficient andreliable routing protocol for wireless sensor networks [J]. IET Communications, 2008, 2(8): 995-1008.
[11] Shanti C., Sahoo A. Dgram: A delay guaranteed routing and mac protocol for wireless sensor networks [J]. IEEE Transactions on Mobile Computing, 2010, 9(10): 1407-23.
[12] Colorni A., Dorigo M., Maniezzo V. Distributed optimization by ant colonies [C]. proceedings of the First European Conference on Artificial Life, Paris, France. 1991: 134-42.
[13] Dorigo M., Maniezzo V., Colorni A. Ant system: Optimization by a colony of cooperating agents [J]. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, 1996, 26(1): 29-41.
[14] Dorigo M., Gambardella L.M. Ant colony system: A cooperative learning approach to the traveling salesman problem [J]. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 53 - 66.
[15] Stutzle T., Hoos H. Max-min ant system and local search for the traveling salesman problem [C]. proceedings of the IEEE International Conference on Evolutionary Computation, Indianapolis, USA. 1997: 309-14.
[16] Sim K.M., Sun W.H. Ant colony optimization for routing and load-balancing: Survey and new directions [J]. IEEE Transactions on Systems Man and Cybernetics Part a-Systems and Humans, 2003, 33(5): 560-72.
[17] Di Caro G., Dorigo M. Antnet: Distributed stigmergetic control for communications networks [J]. Journal of Artificial Intelligence Research, 1998, 9(1): 317-65.
[18] Gunes M., Spaniol O. Ant-routing-algorithm for mobile multi-hop ad-hoc networks [C]. proceedings of the Network Control and Engineering for QoS, Security and Mobility II, IFIP TC6 / WG62 & WG67 Conference on Network Control and Engineering for QoS, Muscat, Oman. 2003: 120-38.
[19] Di Caro G., Ducatelle F., Gambardella L.M. Anthocnet: An ant-based hybrid routing algorithm for mobile ad hoc networks [C]. proceedings of the 8th International Conference on Parallel Problem Solving from Nature Birmingham, UK. 2004: 461-70.
[20] Ducatelle F., Di Caro G., Gambardella L.M. Using ant agents to combine reactive and proactive strategies for routing in mobile ad hoc networks [J]. International Journal ofComputational Intelligence and Applications, Special Issue on Nature-Inspired Approaches to Networks and Telecommunications, 2005, 5(2): 169-84.
[21] Camara D., Loureiro A.a.F. A novel routing algorithm for ad hoc networks [C]. proceedings of the 33rd Annual Hawaii International Conference on System Science, Hawaii,USA. 2000: 1-8.
[22] Osagie E., Thulasiraman P., Thulasiram R.K. Paconet: Improved ant colony optimization routing algorithm for mobile ad hoc networks [C]. proceedings of the 22nd International Conference on Advanced Information Networking and Applications, GinoWan, Japan. 2008: 204-11.
[23] Wu Z., Song H., Jiang S., et al. Ant-based energy aware disjoint multipath routing algorithm in manets [C]. proceedings of the International Conference on Multimedia and Ubiquitous Engineering, Proceedings, Seoul, Korea. 2007: 674-9.
[24] Zheng X., Guo W., Liu R. An ant-based distributed routing algorithm for ad-hoc networks [C]. proceedings of the International Conference on Communication, Circuits, and Systems, Chengdu, China. 2004: 412-7.
[25] Rosati L., Berioli M., Reali G. On ant routing algorithms in ad hoc networks with critical connectivity [J]. Ad Hoc Networks, 2008, 6(6): 827-59.
[26] Wang J., Osagie E., Thulasiraman P., et al. Hopnet: A hybrid ant colony optimization routing algorithm for mobile ad hoc network [J]. Ad Hoc Networks, 2009, 7(4): 690-705.
[27] Liu L.G., Feng G.Z. A novel ant colony based qos-aware routing algorithm for manets [C]. proceedings of the The 2005 International Conference on Natural Computation, Changsha, China. 2005: 457-66.
[28] Kamali S., Opatrny J. A position based ant colony routing algorithm for mobile ad-hoc networks [J]. Journal of Networks, 2008, 3(4): 31-41.
[29] Hussein O., Saadawi T. Ant routing algorithm for mobile ad-hoc networks (arama) [C]. proceedings of the 2003 IEEE International Performance, Computing, and Communications Conference, Phoenix, USA. 2003: 281-90.
[30] Baras J.S., Mehta H. A probabilistic emergent routing algorithm for mobile ad hoc networks [C]. proceedings of the Modeling and Optimization in Mobile, Ad-Hoc andWireless Networks, Sophia-Antipolis, France. 2003.
[31] Marwaha S., Tham C.K., Srinivasan D. Mobile agents based routing protocol for mobile ad hoc networks [C]. proceedings of the IEEE Global Telecommunications Conference, Taipei, China. 2002: 163-7.
[32] Wu Z., Song H. Ant-based energy-aware disjoint multipath routing algorithm for manets [J]. The Computer Journal, 2010, 53(2): 166-76.
[33] Zhang Y., Kuhn L.D., Fromherz M.P.J. Improvements on ant routing for sensor networks [C]. proceedings of the Fourth International Workshop on Ant Colony Optimization and Swarm Intelligence, Brussels, Belgium. 2004: 154-65.
[34]梁华为,陈万明,李帅, et al.基于蚁群优化的无线传感器网络能量均衡路由算法[J].模式识别与人工智能, 2007, 20(2): 275-80.
[35] Tu Z., Wang Q., Shen Y. Optimal mobile agent routing for data fusion in distributed sensor networks using improved ant colony algorithm [C]. proceedings of the IEEE International Instrumentation and Measurement Technology Conference, Victoria, Canada. 2008: 155-9.
[36] Xie H., Zhang Z., Zhou X. A novel routing protocol in wireless sensor networks based on ant colony optimization [C]. proceedings of the International Conference on Environmental Science and Information Application Technology, Wuhan, China. 2009: 646-9.
[37] Ghasemaghaei R., Rahman A., Gueaieb W., et al. Ant colony-based reinforcement learning algorithm for routing in wireless sensor networks [C]. proceedings of the Instrumentation and Measurement Technology Conference Proceedings, Warsaw, Poland. 2007: 1-6.
[38] Muraleedharan R., Osadciw L. Balancing the performance of a sensor network using an ant-system [C]. proceedings of the 37th Annual Conference on Information Sciences and Systems, Baltimore, Maryland. 2003.
[39] Wang J., D'auriol B.J., Lee Y.-K., et al. A swarm intelligence inspired autonomic routing scenario in ubiquitous sensor networks [C]. proceedings of the 1st International Conference on Multimedia and Ubiquitous Engineering, Seoul, Korea. 2007: 745-50.
[40] Muraleedharan R., Osadciw L. Sensor communication network using swarm intelligence [C]. proceedings of the 2nd IEEE Upstate New York Workshop on Sensor Networks, Syracuse, USA. 2002: 4-7.
[41] Yang J., Lin Y., Xiong W., et al. Ant colony-based multi-path routing algorithm for wireless sensor networks [C]. proceedings of the International Workshop on Intelligent Systems and Applications, Wuhan, China. 2009: 1-4.
[42] Okdem S., Karaboga D. Routing in wireless sensor networks using an ant colony optimization (aco) router chip [J]. Sensors, 2009, 9(2): 909-21.
[43]杭海存,郭爱煌,舒文杰.基于leach与蚁群算法的wsn路由机制及性能分析[J].传感技术学报, 2008, 21(10): 1735-8.
[44] Wen Y., Chen Y., Qian D. An ant-based approach to power-efficient algorithm for wireless sensor networks [C]. proceedings of the World Congress on Engineering 2007, London, U.K. 2007: 1546-50.
[45]郑巍,刘三阳,寇晓丽.基于蚁群策略的无线传感器网络能量有效路由算法[J].系统工程与电子技术, 2009, 31(8): 1993-6.
[46] Holland J.H. Adaptation in natural and artificial systems [M]. Cambridge, USA: MIT Press, 1992.
[47] Sun Y., Tian J. Wsn path optimization based on fusion of improved ant colony algorithm and genetic algorithm [J]. Journal of Computational Information Systems, 2010, 6(5): 1591-9.
[48] Camilo T., Carreto C., Sa Silva J., et al. An energy-efficient ant-based routing algorithm for wireless sensor networks [C]. proceedings of the 5th International Workshop on Ant Colony Optimization and Swarm Intelligence, Brussels, Belgium. 2006: 49-59.
[49]吴小兵,陈贵海.无线传感器网络中节点非均匀分布的能量空洞问题[J].计算机学报, 2008, 31(2): 253-61.
[50] Wu X., Chen G., Das S.K. Avoiding energy holes in wireless sensor networks with nonuniform node distribution [J]. IEEE Transactions on Parallel and Distributed Systems, 2008, 19(5): 710-20.
[51] Li Q.Q., Yang M., Jin Y., et al. Load-similar node distribution for prolonging networklifetime in pmrc-based wireless sensor networks [J]. Communication and Networking, 2009, 56: 384-93.
[52] Yun Z., Bai X., Xuan D., et al. Optimal deployment patterns for full coverage and k-connectivity (k <= 6) wireless sensor networks [J]. IEEE/ACM Transactions on Networking, 2010, 18(3): 934-47.
[53] Zhang C., Bai X., Teng J., et al. Constructing low-connectivity and full-coverage three dimensional sensor networks [J]. IEEE Journal on Selected Areas in Communications, 2010, 28(7): 984-93.
[54] Demin W., Bin X., Agrawal D.P. Coverage and lifetime optimization of wireless sensor networks with gaussian distribution [J]. IEEE Transactions on Mobile Computing, 2008, 7(12): 1444-58.
[55] Xu K., Hassanein H., Takahara G., et al. Relay node deployment strategies in heterogeneous wireless sensor networks [J]. IEEE Transactions on Mobile Computing, 2010, 1(28): 145-59.
[56] Misra S., Hong S.D., Xue G., et al. Constrained relay node placement in wireless sensor networks: Formulation and approximations [J]. IEEE-ACM Transactions on Networking, 2010, 18(2): 434-47.
[57] Kaur T., Baek J. A strategic deployment and cluster-header selection for wireless sensor networks [J]. IEEE Transactions on Consumer Electronics, 2009, 55(4): 1890-7.
[58]刘安丰,聂红伟,吴贤佑, et al.基于网络效率的线性无线传感器网络优化部署算法[J].计算机科学, 2009, 36(11): 83-7.
[59]俞黎阳,王能,张卫.异构无线传感器网络中异构节点的部署与优化[J].计算机科学, 2008, 35(9): 48-51.
[60] Xu K., Wang Q., Hassanein H., et al. Optimal wireless sensor networks (wsns) deployment: Minimum cost with lifetime constraint [C]. proceedings of the IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, Maui, USA. 2005: 454-61.
[61] Lloyd E.L., Xue G. Relay node placement in wireless sensor networks [J]. IEEE Transactions on Computers, 2007, 56(1): 134-8.
[62] Wang Q., Xu K., Takahara G., et al. Transactions papers - device placement for heterogeneous wireless sensor networks: Minimum cost with lifetime constraints [J]. IEEE Transactions on Wireless Communications, 2007, 6(7): 2444-53.
[63] Li J.S., Kao H.C., Ke J.D. Voronoi-based relay placement scheme for wireless sensor networks [J]. IET Communications, 2009, 3(4): 530-8.
[64] Leon C. Location-allocation problems [J]. Operations Research, 1963, 11(3): 331-43.
[65] Leon C. Heuristic methods for location-allocation problems [J]. SIAM Review, 1964, 6(1): 37-53.
[66]吴威,王春平,刘智武, et al.地理位置无关的多汇聚节点部署方法[P].中国:CN101232517, 2008-07-30.
[67] Efrat A., Har-Peled S., Mitchell J.S.B. Approximation algorithms for two optimal location problems in sensor networks [C]. proceedings of the 2nd International Conference on Broadband Networks, Boston, USA. 2005.
[68] Luo J., Hubaux J.P. Joint mobility and routing for lifetime elongation in wireless sensor networks [C]. proceedings of the 24th Annual Joint Conference of the IEEE Computer and Communications Societies, Miami, USA. 2005: 1735-46.
[69]罗玎玎,赵海,尹震宇, et al. Wsns中基于pmp的多sink节点布局研究与实现[J].小型微型计算机系统, 2007, 28(6): 979-82.
[70] Luo D., Zhao H., Bi Y., et al. Multiple sink nodes' deployment based on pmp in wsns [C]. proceedings of the 2nd International Conference on Pervasive Computing and Applications, Birmingham, UK. 2007: 675-8.
[71]徐久强,柏大治,罗玎玎, et al.遗传算法在wsns多sink节点布局中的应用[J].东北大学学报(自然科学版), 2008, 29(6): 815-8.
[72]范一鸣,秦本涛.大规模wsns中多sink节点优化部署遗传算法[J].传感器与微系统, 2010, 29(6): 32-5.
[73] Gu Y., Ji Y., Li J., et al. Towards an optimal sink placement in wireless sensor networks [C]. proceedings of the IEEE International Conference on Communications, Cape Town, South Africa. 2010: 1-5.
[74] Younis M., Bangad M., Akkaya K. Base-station repositioning for optimized performance of sensor networks [C]. proceedings of the IEEE 58th VehicularTechnology Conference, Orlando, USA. 2003: 2956-60.
[75] Akkaya K., Younis M., Bangad M. Sink repositioning for enhanced performance in wireless sensor networks [J]. Computer Networks, 2005, 49(4): 512-34.
[76]刘巍,崔莉.基于蚁群算法的传感器网络节点部署设计[J].通信学报, 2009, 30(10): 24-33.
[77] Hu Y., Xue Y., Li Q., et al. The sink node placement and performance implication in mobile sensor networks [J]. Mobile Networks and Applications, 2009, 14(2): 230-40.
[78] Kim H., Seok Y., Choi N., et al. Optimal multi-sink positioning and energy-efficient routing in wireless sensor networks [C]. proceedings of the Information Networking, Convergence in Broadband and Mobile Networking, International Conference, Cheju Island, Korea. 2005: 264-74.
[79] Bogdanov A., Maneva E., Riesenfeld S. Power-aware base station positioning for sensor networks [C]. proceedings of the Twenty-third Annual Joint Conference of the IEEE Computer and Communications Societies, Hong Kong, China. 2004: 575-85.
[80] Pan J.P., Hou Y.T., Cai L., et al. Locating base-stations for video sensor networks [C]. proceedings of the 58th Vehicular Technology Conference, Orlando, USA. 2004: 3000-4.
[81] Pan J.P., Cai L., Hou Y.T., et al. Optimal base-station locations in two-tiered wireless sensor networks [J]. IEEE Transactions on Mobile Computing, 2005, 4(5): 458-73.
[82] Vincze Z., Vida R., Vidacs A. Deploying multiple sinks in multi-hop wireless sensor networks [C]. proceedings of the IEEE International Conference on Pervasive Services, Istanbul, Turkey. 2007: 55-63.
[83] Guney E., Aras N., Altinel I.K., et al. Efficient integer programming formulations for optimum sink location and routing in heterogeneous wireless sensor networks [J]. Computer Networks, 2010, 54(11): 1805-22.
[84] Türkogullari Y.B., Aras N., Kuban Altinel I., et al. An efficient heuristic for placement, scheduling and routing in wireless sensor networks [J]. Ad Hoc Networks, 2010, 8(6): 654-67.
[85] Hartigan J.A. Clustering algorithms [M]. New York, USA: Wiley, 1975.
[86] Oyman E.I., Ersoy C. Multiple sink network design problem in large scale wirelesssensor networks [C]. proceedings of the IEEE International Conference on Communications, Paris, France. 2004: 3663-7.
[87] ChlebíkováJ. Approximating the maximally balanced connected partition problem in graphs [J]. Information Processing Letters, 1996, 60(5): 225-30.
[88] Slama I., Jouaber B., Zeghlache D. Multiple mobile sinks deployment for energy efficiency in large scale wireless sensor networks [J]. E-Business and Telecommunications, 2009, 48: 412-27.
[89] Zhou R., Chen M., Feng G., et al. Genetic clustering route algorithm in wsn [C]. proceedings of the Sixth International Conference on Natural Computation, Yantai, China, 2010. 2010: 4023-6.
[90] Guo L., Tang Q. An improved routing protocol in wsn with hybrid genetic algorithm [C]. proceedings of the Second International Conference on Networks Security Wireless Communications and Trusted Computing, Wuhan, China. 2010: 289-92.
[91] Zou X., Cao Y. Effects of inertia weight on dpso-based single-hop routing protocol for wireless sensor networks [C]. proceedings of the 7th World Congress on Intelligent Control and Automation, Chongqing, China. 2008: 6707-10.
[92] Liu A., Ma M., Chen Z., et al. Energy-hole avoidance routing algorithm for wsn [C]. proceedings of the Fourth International Conference on Natural Computation, Jinan, China. 2008: 76-80.
[93]陈延军,潘泉,耶刚强, et al.基于蚁群-遗传的无线传感器网络路由算法[J].湖南大学学报(自然科学版), 2009, 36(7): 46-51.
[94] Schrijver A. On the history of combinatorial optimization (till 1960) [M]//K. AARDAL G L N, WEISMANTEL R. Handbooks in operations research and management science. Elsevier. 2005: 1-68.
[95] Kalaavathi B., Madhavi S., Vijayaragavan S., et al. Review of ant based routing protocols for manet [C]. proceedings of the International Conference on Computing, Communication and Networking, Tamilnadu, India. 2008: 1-9.
[96] Lian J., Naik K., Agnew G.B. Data capacity improvement of wireless sensor networks using non-uniform sensor distribution [J]. International Journal of Distributed Sensor Networks, 2006, 2(2): 121-45.
[97] He Y., Xu T. The research of non-uniform node distribution in wireless sensor networks [C]. proceedings of the 2nd International Symposium on Information Engineering and Electronic Commerce, Ternopil, Ukraine. 2010: 1-6.
[98] Olariu S., Stojmenovic I. Design guidelines for maximizing lifetime and avoiding energy holes in sensor networks with uniform distribution and uniform reporting [C]. proceedings of the 25th IEEE International Conference on Computer Communications, Barcelona, Spain. 2006: 1-12.
[99]宋超.基于蚁群优化解决传感器网络中的能量洞问题[J].软件学报, 2009, 20(10): 15.
[100] Song C., Liu M., Cao J., et al. Maximizing network lifetime based on transmission range adjustment in wireless sensor networks [J]. Computer Communications, 2009, 32(11): 1316-25.
[101] Dietrich I., Dressler F. On the lifetime of wireless sensor networks [J]. ACM Transactions on Sensor Networks, 2009, 5(1): 1-39.
[102] Champ J., Saad C., Baert A.E. Lifetime in wireless sensor networks [C]. proceedings of the 3rd International Conference on Complex, Intelligent and Software Intensive Systems, Fukuoka, Japan. 2009: 293-8.
[103] Chen Y., Zhao Q. On the lifetime of wireless sensor networks [J]. IEEE Communications Letters, 2005, 9(11): 976-8.
[104] Li J., Mohapatra P. An analytical model for the energy hole problem in many-to-one sensor networks [C]. proceedings of the IEEE 62nd Vehicular Technology Conference, Dallas, USA. 2005: 2721-5.
[105] Li J., Mohapatra P. Analytical modeling and mitigation techniques for the energy hole problem in sensor networks [J]. Pervasive and Mobile Computing, 2007, 3(3): 233-54.
[106] Kennedy J., Eberhart R. Particle swarm optimization [C]. proceedings of the IEEE International Conference on Neural Networks, Perth, Australia. 1995: 1942-8.
[107] Eberhart, Yuhui S. Particle swarm optimization: Developments, applications and resources [C]. proceedings of the 2001 Congress on Evolutionary Computation, Seoul, Korea. 2001: 81-6.
[108]刘玉亮,李卫华,陈强辉.无线基站的选址方法研究[J].无线电工程, 2007,37(11): 15-7.
[109] Han K.H., Ko Y.B., Kim J.H. A novel gradient approach for efficient data dissemination in wireless sensor networks [C]. proceedings of the IEEE 60th Vehicular Technology Conference, Los Angeles, USA. 2004: 2979-83.
[110]郑明才,张大方,赵小超.最小跳数路由无线传感器网络中的路由数估计[J].计算机工程与应用, 2007, 43(15): 151-6.
[111] Dijkstra E.W. A note on two problems in connexion with graphs [J]. Numerische Mathematik, 1959, 1(1): 269-71.
[112] Warshall S. A theorem on boolean matrices [J]. Journal of the ACM, 1962, 9(1): 11-2.
[113] Floyd R.W. Algorithm 96: Ancestor [J]. Communications of the ACM, 1962, 5(6): 344-5.
[114] Floyd R.W. Algorithm 97: Shortest path [J]. Communications of the ACM, 1962, 5(6): 345.
[115] Hart P.E., Nilsson N.J., Raphael B. A formal basis for the heuristic determination of minimum cost paths [J]. IEEE Transactions on Systems Science and Cybernetics, 1968, 4(2): 100-7.
[2] Heinzelman W.R., Kulik J., Balakrishnan H. Adaptive protocols for information dissemination in wireless sensor networks [C]. proceedings of the The Fifth Annual ACM/IEEE International Conference on Mobile Computing and Networking, Seattle, USA. 1999: 174-85.
[3] Intanagonwiwat C., Govindan R., Estrin D. Directed diffusion: A scalable and robust communication paradigm for sensor networks [C]. proceedings of the Sixth Annual International Conference on Mobile Computing and Networking, Boston, USA. 2000: 56-67.
[4] Sohrabi K., Gao J., Ailawadhi V., et al. Protocols for self-organization of a wireless sensor network [J]. IEEE Personal Communications, 2000, 7(5): 16-27.
[5] Braginsky D., Estrin D. Rumor routing algorthim for sensor networks [C]. proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications, Atlanta, USA. 2002: 22-31.
[6] Heinzelman W.R., Chandrakasan A., Balakrishnan H. Energy-efficient communication protocol for wireless microsensor networks [C]. proceedings of the 33rd Annual Hawaii International Conference on System Sciences, Maui, USA. 2000: 908-18.
[7] Manjeshwar A., Agrawal D.P. Teen: A routing protocol for enhanced efficiency in wireless sensor networks [C]. proceedings of the 15th International Parallel and Distributed Processing Symposium, San Francisco, USA. 2001: 2009 - 15.
[8] Lindsey S., Raghavendra C.S. Pegasis: Power-efficient gathering in sensor information systems [C]. proceedings of the IEEE Aerospace Conference Proceedings, Big Sky, USA. 2002: 1125-30.
[9] Teo J.-Y., Ha Y., Tham C.-K. Interference-minimized multipath routing with congestion control in wireless sensor network for high-rate streaming [J]. IEEE Transactions on Mobile Computing 2008, 7(9): 1124-37.
[10] Zabin F., Misra S., Woungang I., et al. Reep: Data-centric, energy-efficient andreliable routing protocol for wireless sensor networks [J]. IET Communications, 2008, 2(8): 995-1008.
[11] Shanti C., Sahoo A. Dgram: A delay guaranteed routing and mac protocol for wireless sensor networks [J]. IEEE Transactions on Mobile Computing, 2010, 9(10): 1407-23.
[12] Colorni A., Dorigo M., Maniezzo V. Distributed optimization by ant colonies [C]. proceedings of the First European Conference on Artificial Life, Paris, France. 1991: 134-42.
[13] Dorigo M., Maniezzo V., Colorni A. Ant system: Optimization by a colony of cooperating agents [J]. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, 1996, 26(1): 29-41.
[14] Dorigo M., Gambardella L.M. Ant colony system: A cooperative learning approach to the traveling salesman problem [J]. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 53 - 66.
[15] Stutzle T., Hoos H. Max-min ant system and local search for the traveling salesman problem [C]. proceedings of the IEEE International Conference on Evolutionary Computation, Indianapolis, USA. 1997: 309-14.
[16] Sim K.M., Sun W.H. Ant colony optimization for routing and load-balancing: Survey and new directions [J]. IEEE Transactions on Systems Man and Cybernetics Part a-Systems and Humans, 2003, 33(5): 560-72.
[17] Di Caro G., Dorigo M. Antnet: Distributed stigmergetic control for communications networks [J]. Journal of Artificial Intelligence Research, 1998, 9(1): 317-65.
[18] Gunes M., Spaniol O. Ant-routing-algorithm for mobile multi-hop ad-hoc networks [C]. proceedings of the Network Control and Engineering for QoS, Security and Mobility II, IFIP TC6 / WG62 & WG67 Conference on Network Control and Engineering for QoS, Muscat, Oman. 2003: 120-38.
[19] Di Caro G., Ducatelle F., Gambardella L.M. Anthocnet: An ant-based hybrid routing algorithm for mobile ad hoc networks [C]. proceedings of the 8th International Conference on Parallel Problem Solving from Nature Birmingham, UK. 2004: 461-70.
[20] Ducatelle F., Di Caro G., Gambardella L.M. Using ant agents to combine reactive and proactive strategies for routing in mobile ad hoc networks [J]. International Journal ofComputational Intelligence and Applications, Special Issue on Nature-Inspired Approaches to Networks and Telecommunications, 2005, 5(2): 169-84.
[21] Camara D., Loureiro A.a.F. A novel routing algorithm for ad hoc networks [C]. proceedings of the 33rd Annual Hawaii International Conference on System Science, Hawaii,USA. 2000: 1-8.
[22] Osagie E., Thulasiraman P., Thulasiram R.K. Paconet: Improved ant colony optimization routing algorithm for mobile ad hoc networks [C]. proceedings of the 22nd International Conference on Advanced Information Networking and Applications, GinoWan, Japan. 2008: 204-11.
[23] Wu Z., Song H., Jiang S., et al. Ant-based energy aware disjoint multipath routing algorithm in manets [C]. proceedings of the International Conference on Multimedia and Ubiquitous Engineering, Proceedings, Seoul, Korea. 2007: 674-9.
[24] Zheng X., Guo W., Liu R. An ant-based distributed routing algorithm for ad-hoc networks [C]. proceedings of the International Conference on Communication, Circuits, and Systems, Chengdu, China. 2004: 412-7.
[25] Rosati L., Berioli M., Reali G. On ant routing algorithms in ad hoc networks with critical connectivity [J]. Ad Hoc Networks, 2008, 6(6): 827-59.
[26] Wang J., Osagie E., Thulasiraman P., et al. Hopnet: A hybrid ant colony optimization routing algorithm for mobile ad hoc network [J]. Ad Hoc Networks, 2009, 7(4): 690-705.
[27] Liu L.G., Feng G.Z. A novel ant colony based qos-aware routing algorithm for manets [C]. proceedings of the The 2005 International Conference on Natural Computation, Changsha, China. 2005: 457-66.
[28] Kamali S., Opatrny J. A position based ant colony routing algorithm for mobile ad-hoc networks [J]. Journal of Networks, 2008, 3(4): 31-41.
[29] Hussein O., Saadawi T. Ant routing algorithm for mobile ad-hoc networks (arama) [C]. proceedings of the 2003 IEEE International Performance, Computing, and Communications Conference, Phoenix, USA. 2003: 281-90.
[30] Baras J.S., Mehta H. A probabilistic emergent routing algorithm for mobile ad hoc networks [C]. proceedings of the Modeling and Optimization in Mobile, Ad-Hoc andWireless Networks, Sophia-Antipolis, France. 2003.
[31] Marwaha S., Tham C.K., Srinivasan D. Mobile agents based routing protocol for mobile ad hoc networks [C]. proceedings of the IEEE Global Telecommunications Conference, Taipei, China. 2002: 163-7.
[32] Wu Z., Song H. Ant-based energy-aware disjoint multipath routing algorithm for manets [J]. The Computer Journal, 2010, 53(2): 166-76.
[33] Zhang Y., Kuhn L.D., Fromherz M.P.J. Improvements on ant routing for sensor networks [C]. proceedings of the Fourth International Workshop on Ant Colony Optimization and Swarm Intelligence, Brussels, Belgium. 2004: 154-65.
[34]梁华为,陈万明,李帅, et al.基于蚁群优化的无线传感器网络能量均衡路由算法[J].模式识别与人工智能, 2007, 20(2): 275-80.
[35] Tu Z., Wang Q., Shen Y. Optimal mobile agent routing for data fusion in distributed sensor networks using improved ant colony algorithm [C]. proceedings of the IEEE International Instrumentation and Measurement Technology Conference, Victoria, Canada. 2008: 155-9.
[36] Xie H., Zhang Z., Zhou X. A novel routing protocol in wireless sensor networks based on ant colony optimization [C]. proceedings of the International Conference on Environmental Science and Information Application Technology, Wuhan, China. 2009: 646-9.
[37] Ghasemaghaei R., Rahman A., Gueaieb W., et al. Ant colony-based reinforcement learning algorithm for routing in wireless sensor networks [C]. proceedings of the Instrumentation and Measurement Technology Conference Proceedings, Warsaw, Poland. 2007: 1-6.
[38] Muraleedharan R., Osadciw L. Balancing the performance of a sensor network using an ant-system [C]. proceedings of the 37th Annual Conference on Information Sciences and Systems, Baltimore, Maryland. 2003.
[39] Wang J., D'auriol B.J., Lee Y.-K., et al. A swarm intelligence inspired autonomic routing scenario in ubiquitous sensor networks [C]. proceedings of the 1st International Conference on Multimedia and Ubiquitous Engineering, Seoul, Korea. 2007: 745-50.
[40] Muraleedharan R., Osadciw L. Sensor communication network using swarm intelligence [C]. proceedings of the 2nd IEEE Upstate New York Workshop on Sensor Networks, Syracuse, USA. 2002: 4-7.
[41] Yang J., Lin Y., Xiong W., et al. Ant colony-based multi-path routing algorithm for wireless sensor networks [C]. proceedings of the International Workshop on Intelligent Systems and Applications, Wuhan, China. 2009: 1-4.
[42] Okdem S., Karaboga D. Routing in wireless sensor networks using an ant colony optimization (aco) router chip [J]. Sensors, 2009, 9(2): 909-21.
[43]杭海存,郭爱煌,舒文杰.基于leach与蚁群算法的wsn路由机制及性能分析[J].传感技术学报, 2008, 21(10): 1735-8.
[44] Wen Y., Chen Y., Qian D. An ant-based approach to power-efficient algorithm for wireless sensor networks [C]. proceedings of the World Congress on Engineering 2007, London, U.K. 2007: 1546-50.
[45]郑巍,刘三阳,寇晓丽.基于蚁群策略的无线传感器网络能量有效路由算法[J].系统工程与电子技术, 2009, 31(8): 1993-6.
[46] Holland J.H. Adaptation in natural and artificial systems [M]. Cambridge, USA: MIT Press, 1992.
[47] Sun Y., Tian J. Wsn path optimization based on fusion of improved ant colony algorithm and genetic algorithm [J]. Journal of Computational Information Systems, 2010, 6(5): 1591-9.
[48] Camilo T., Carreto C., Sa Silva J., et al. An energy-efficient ant-based routing algorithm for wireless sensor networks [C]. proceedings of the 5th International Workshop on Ant Colony Optimization and Swarm Intelligence, Brussels, Belgium. 2006: 49-59.
[49]吴小兵,陈贵海.无线传感器网络中节点非均匀分布的能量空洞问题[J].计算机学报, 2008, 31(2): 253-61.
[50] Wu X., Chen G., Das S.K. Avoiding energy holes in wireless sensor networks with nonuniform node distribution [J]. IEEE Transactions on Parallel and Distributed Systems, 2008, 19(5): 710-20.
[51] Li Q.Q., Yang M., Jin Y., et al. Load-similar node distribution for prolonging networklifetime in pmrc-based wireless sensor networks [J]. Communication and Networking, 2009, 56: 384-93.
[52] Yun Z., Bai X., Xuan D., et al. Optimal deployment patterns for full coverage and k-connectivity (k <= 6) wireless sensor networks [J]. IEEE/ACM Transactions on Networking, 2010, 18(3): 934-47.
[53] Zhang C., Bai X., Teng J., et al. Constructing low-connectivity and full-coverage three dimensional sensor networks [J]. IEEE Journal on Selected Areas in Communications, 2010, 28(7): 984-93.
[54] Demin W., Bin X., Agrawal D.P. Coverage and lifetime optimization of wireless sensor networks with gaussian distribution [J]. IEEE Transactions on Mobile Computing, 2008, 7(12): 1444-58.
[55] Xu K., Hassanein H., Takahara G., et al. Relay node deployment strategies in heterogeneous wireless sensor networks [J]. IEEE Transactions on Mobile Computing, 2010, 1(28): 145-59.
[56] Misra S., Hong S.D., Xue G., et al. Constrained relay node placement in wireless sensor networks: Formulation and approximations [J]. IEEE-ACM Transactions on Networking, 2010, 18(2): 434-47.
[57] Kaur T., Baek J. A strategic deployment and cluster-header selection for wireless sensor networks [J]. IEEE Transactions on Consumer Electronics, 2009, 55(4): 1890-7.
[58]刘安丰,聂红伟,吴贤佑, et al.基于网络效率的线性无线传感器网络优化部署算法[J].计算机科学, 2009, 36(11): 83-7.
[59]俞黎阳,王能,张卫.异构无线传感器网络中异构节点的部署与优化[J].计算机科学, 2008, 35(9): 48-51.
[60] Xu K., Wang Q., Hassanein H., et al. Optimal wireless sensor networks (wsns) deployment: Minimum cost with lifetime constraint [C]. proceedings of the IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, Maui, USA. 2005: 454-61.
[61] Lloyd E.L., Xue G. Relay node placement in wireless sensor networks [J]. IEEE Transactions on Computers, 2007, 56(1): 134-8.
[62] Wang Q., Xu K., Takahara G., et al. Transactions papers - device placement for heterogeneous wireless sensor networks: Minimum cost with lifetime constraints [J]. IEEE Transactions on Wireless Communications, 2007, 6(7): 2444-53.
[63] Li J.S., Kao H.C., Ke J.D. Voronoi-based relay placement scheme for wireless sensor networks [J]. IET Communications, 2009, 3(4): 530-8.
[64] Leon C. Location-allocation problems [J]. Operations Research, 1963, 11(3): 331-43.
[65] Leon C. Heuristic methods for location-allocation problems [J]. SIAM Review, 1964, 6(1): 37-53.
[66]吴威,王春平,刘智武, et al.地理位置无关的多汇聚节点部署方法[P].中国:CN101232517, 2008-07-30.
[67] Efrat A., Har-Peled S., Mitchell J.S.B. Approximation algorithms for two optimal location problems in sensor networks [C]. proceedings of the 2nd International Conference on Broadband Networks, Boston, USA. 2005.
[68] Luo J., Hubaux J.P. Joint mobility and routing for lifetime elongation in wireless sensor networks [C]. proceedings of the 24th Annual Joint Conference of the IEEE Computer and Communications Societies, Miami, USA. 2005: 1735-46.
[69]罗玎玎,赵海,尹震宇, et al. Wsns中基于pmp的多sink节点布局研究与实现[J].小型微型计算机系统, 2007, 28(6): 979-82.
[70] Luo D., Zhao H., Bi Y., et al. Multiple sink nodes' deployment based on pmp in wsns [C]. proceedings of the 2nd International Conference on Pervasive Computing and Applications, Birmingham, UK. 2007: 675-8.
[71]徐久强,柏大治,罗玎玎, et al.遗传算法在wsns多sink节点布局中的应用[J].东北大学学报(自然科学版), 2008, 29(6): 815-8.
[72]范一鸣,秦本涛.大规模wsns中多sink节点优化部署遗传算法[J].传感器与微系统, 2010, 29(6): 32-5.
[73] Gu Y., Ji Y., Li J., et al. Towards an optimal sink placement in wireless sensor networks [C]. proceedings of the IEEE International Conference on Communications, Cape Town, South Africa. 2010: 1-5.
[74] Younis M., Bangad M., Akkaya K. Base-station repositioning for optimized performance of sensor networks [C]. proceedings of the IEEE 58th VehicularTechnology Conference, Orlando, USA. 2003: 2956-60.
[75] Akkaya K., Younis M., Bangad M. Sink repositioning for enhanced performance in wireless sensor networks [J]. Computer Networks, 2005, 49(4): 512-34.
[76]刘巍,崔莉.基于蚁群算法的传感器网络节点部署设计[J].通信学报, 2009, 30(10): 24-33.
[77] Hu Y., Xue Y., Li Q., et al. The sink node placement and performance implication in mobile sensor networks [J]. Mobile Networks and Applications, 2009, 14(2): 230-40.
[78] Kim H., Seok Y., Choi N., et al. Optimal multi-sink positioning and energy-efficient routing in wireless sensor networks [C]. proceedings of the Information Networking, Convergence in Broadband and Mobile Networking, International Conference, Cheju Island, Korea. 2005: 264-74.
[79] Bogdanov A., Maneva E., Riesenfeld S. Power-aware base station positioning for sensor networks [C]. proceedings of the Twenty-third Annual Joint Conference of the IEEE Computer and Communications Societies, Hong Kong, China. 2004: 575-85.
[80] Pan J.P., Hou Y.T., Cai L., et al. Locating base-stations for video sensor networks [C]. proceedings of the 58th Vehicular Technology Conference, Orlando, USA. 2004: 3000-4.
[81] Pan J.P., Cai L., Hou Y.T., et al. Optimal base-station locations in two-tiered wireless sensor networks [J]. IEEE Transactions on Mobile Computing, 2005, 4(5): 458-73.
[82] Vincze Z., Vida R., Vidacs A. Deploying multiple sinks in multi-hop wireless sensor networks [C]. proceedings of the IEEE International Conference on Pervasive Services, Istanbul, Turkey. 2007: 55-63.
[83] Guney E., Aras N., Altinel I.K., et al. Efficient integer programming formulations for optimum sink location and routing in heterogeneous wireless sensor networks [J]. Computer Networks, 2010, 54(11): 1805-22.
[84] Türkogullari Y.B., Aras N., Kuban Altinel I., et al. An efficient heuristic for placement, scheduling and routing in wireless sensor networks [J]. Ad Hoc Networks, 2010, 8(6): 654-67.
[85] Hartigan J.A. Clustering algorithms [M]. New York, USA: Wiley, 1975.
[86] Oyman E.I., Ersoy C. Multiple sink network design problem in large scale wirelesssensor networks [C]. proceedings of the IEEE International Conference on Communications, Paris, France. 2004: 3663-7.
[87] ChlebíkováJ. Approximating the maximally balanced connected partition problem in graphs [J]. Information Processing Letters, 1996, 60(5): 225-30.
[88] Slama I., Jouaber B., Zeghlache D. Multiple mobile sinks deployment for energy efficiency in large scale wireless sensor networks [J]. E-Business and Telecommunications, 2009, 48: 412-27.
[89] Zhou R., Chen M., Feng G., et al. Genetic clustering route algorithm in wsn [C]. proceedings of the Sixth International Conference on Natural Computation, Yantai, China, 2010. 2010: 4023-6.
[90] Guo L., Tang Q. An improved routing protocol in wsn with hybrid genetic algorithm [C]. proceedings of the Second International Conference on Networks Security Wireless Communications and Trusted Computing, Wuhan, China. 2010: 289-92.
[91] Zou X., Cao Y. Effects of inertia weight on dpso-based single-hop routing protocol for wireless sensor networks [C]. proceedings of the 7th World Congress on Intelligent Control and Automation, Chongqing, China. 2008: 6707-10.
[92] Liu A., Ma M., Chen Z., et al. Energy-hole avoidance routing algorithm for wsn [C]. proceedings of the Fourth International Conference on Natural Computation, Jinan, China. 2008: 76-80.
[93]陈延军,潘泉,耶刚强, et al.基于蚁群-遗传的无线传感器网络路由算法[J].湖南大学学报(自然科学版), 2009, 36(7): 46-51.
[94] Schrijver A. On the history of combinatorial optimization (till 1960) [M]//K. AARDAL G L N, WEISMANTEL R. Handbooks in operations research and management science. Elsevier. 2005: 1-68.
[95] Kalaavathi B., Madhavi S., Vijayaragavan S., et al. Review of ant based routing protocols for manet [C]. proceedings of the International Conference on Computing, Communication and Networking, Tamilnadu, India. 2008: 1-9.
[96] Lian J., Naik K., Agnew G.B. Data capacity improvement of wireless sensor networks using non-uniform sensor distribution [J]. International Journal of Distributed Sensor Networks, 2006, 2(2): 121-45.
[97] He Y., Xu T. The research of non-uniform node distribution in wireless sensor networks [C]. proceedings of the 2nd International Symposium on Information Engineering and Electronic Commerce, Ternopil, Ukraine. 2010: 1-6.
[98] Olariu S., Stojmenovic I. Design guidelines for maximizing lifetime and avoiding energy holes in sensor networks with uniform distribution and uniform reporting [C]. proceedings of the 25th IEEE International Conference on Computer Communications, Barcelona, Spain. 2006: 1-12.
[99]宋超.基于蚁群优化解决传感器网络中的能量洞问题[J].软件学报, 2009, 20(10): 15.
[100] Song C., Liu M., Cao J., et al. Maximizing network lifetime based on transmission range adjustment in wireless sensor networks [J]. Computer Communications, 2009, 32(11): 1316-25.
[101] Dietrich I., Dressler F. On the lifetime of wireless sensor networks [J]. ACM Transactions on Sensor Networks, 2009, 5(1): 1-39.
[102] Champ J., Saad C., Baert A.E. Lifetime in wireless sensor networks [C]. proceedings of the 3rd International Conference on Complex, Intelligent and Software Intensive Systems, Fukuoka, Japan. 2009: 293-8.
[103] Chen Y., Zhao Q. On the lifetime of wireless sensor networks [J]. IEEE Communications Letters, 2005, 9(11): 976-8.
[104] Li J., Mohapatra P. An analytical model for the energy hole problem in many-to-one sensor networks [C]. proceedings of the IEEE 62nd Vehicular Technology Conference, Dallas, USA. 2005: 2721-5.
[105] Li J., Mohapatra P. Analytical modeling and mitigation techniques for the energy hole problem in sensor networks [J]. Pervasive and Mobile Computing, 2007, 3(3): 233-54.
[106] Kennedy J., Eberhart R. Particle swarm optimization [C]. proceedings of the IEEE International Conference on Neural Networks, Perth, Australia. 1995: 1942-8.
[107] Eberhart, Yuhui S. Particle swarm optimization: Developments, applications and resources [C]. proceedings of the 2001 Congress on Evolutionary Computation, Seoul, Korea. 2001: 81-6.
[108]刘玉亮,李卫华,陈强辉.无线基站的选址方法研究[J].无线电工程, 2007,37(11): 15-7.
[109] Han K.H., Ko Y.B., Kim J.H. A novel gradient approach for efficient data dissemination in wireless sensor networks [C]. proceedings of the IEEE 60th Vehicular Technology Conference, Los Angeles, USA. 2004: 2979-83.
[110]郑明才,张大方,赵小超.最小跳数路由无线传感器网络中的路由数估计[J].计算机工程与应用, 2007, 43(15): 151-6.
[111] Dijkstra E.W. A note on two problems in connexion with graphs [J]. Numerische Mathematik, 1959, 1(1): 269-71.
[112] Warshall S. A theorem on boolean matrices [J]. Journal of the ACM, 1962, 9(1): 11-2.
[113] Floyd R.W. Algorithm 96: Ancestor [J]. Communications of the ACM, 1962, 5(6): 344-5.
[114] Floyd R.W. Algorithm 97: Shortest path [J]. Communications of the ACM, 1962, 5(6): 345.
[115] Hart P.E., Nilsson N.J., Raphael B. A formal basis for the heuristic determination of minimum cost paths [J]. IEEE Transactions on Systems Science and Cybernetics, 1968, 4(2): 100-7.