WSAN中时延约束的协作数据汇聚能效优化研究
|
摘要
摘要：能效优化是无线传感器-执行器网络(Wireless Sensor and Actuator Network, WSAN)的核心问题之一。近年来,以动态拓扑控制和节点分布式协作为基本策略的协作数据汇聚是提升WSAN能量效率的重要研究方向。本文以重载桥梁和路基长期安全状态监测的WSAN为研究背景,采用增强学习、协作通信和博弈论等理论和方法,研究执行器路径规划、多执行器负载平衡和节点干扰抑制等问题,提出WSAN中时延约束下的能效优化机制。主要工作包括以下几个方面：
论文从应用于监测重载桥梁和路基的WSAN实例出发,分析了时延约束下多执行器WSAN能效优化的重点和难点,提出了基于移动数据汇聚和协作通信干扰抑制的WSAN系统架构,首先构建具有能量感知的动态拓扑,以此为基础选择轮询点,再规划执行器采集信息的路径；然后针对多执行器情况,采用在线模糊Q学习方法对网络进行自适应分区,保证多个执行器间的通信负载和能耗均衡；针对执行器采集信息过程中相邻节点间存在的数据干扰,提出基于协作通信和博弈论的干扰抑制方法。为实现低数据汇聚时延、高能量效率、高可靠性的WSAN提供一套有效的解决方案。
提高网络能效,优化网络生存时间是WSAN的核心目标。论文针对多跳路由数据汇聚的负载均衡问题,选择网络数据汇聚轮询点。首先根据传感器节点的剩余能量构造网络动态生成树；在全局拓扑信息可得的场景下提出基于入度优先的轮询点选择算法,在只能获得局部拓扑信息的场景下提出基于节点通信负载的轮询点选择算法。选择的轮询点作为局部网络数据的汇聚点以分散化、区域化的方式限制网络的路由跳数,均衡网络能耗。
针对WSAN的实时性要求,本文提出时延约束下的移动执行器的赛道寻优路径规划算法。以轮询点为执行器路径规划停留的参考位置,将执行器的路径规划问题转换为旅行商问题；考虑轮询点通信半径,采用基于赛道寻优的最近邻点启发式算法,求解执行器遍历所有轮询点的最优移动轨迹；同时将网络数据汇聚的时延约束转换成执行器的移动距离约束,作为算法的迭代收敛条件,保证网络数据汇聚实时性,并通过建立和求解空间域最优规划模型提供算法性能评估。
对多执行器场景,论文提出一种在线模糊Q学习的能耗均衡自适应分区算法。将每个执行器作为具有学习和决策能力的智能体,实时获取网络中传感器节点的能量和分布状态信息；再使用模糊推理机制离散化位置和能量分布状态,构建Q学习的状态空间和Q函数；根据当前的网络能量分布状态选择具有最大回报值的分区中心位置,并产生对应的Voronoi划分,实现能耗均衡的分区自适应调整。
WSAN中突发数据导致的相邻节点间的数据发送干扰是影响数据传输效率的重要问题。本文提出了一种基于协作数据汇聚的干扰抑制机制。首先将协作通信技术引入WSAN,通过协作中继转发和协作干扰转发的方式提升节点的抗干扰能力；然后考虑到节点自私特性,引入频谱共享机制激励中继节点参与协作干扰抑制；采用斯坦伯格博弈方法对协作干扰抑制过程进行分析；再通过求解博弈均衡来选择最优的策略进行协作信息传输,得到一种干扰情况下的协作数据传输方法。该方法可抑制节点间的干扰影响,确保数据公平、合理的协作传输。仿真实验验证了算法的有效性。
Abstract:The optimization of energy efficiency has been a key issue in wireless sensor and actuator networks (WSAN). Recent research has indicated that using dynamic topology control and distributed cooperation in WSAN to achieve collaborative data gathering can greatly increase the network energy efficiency. Under the background of heavy bridges and roadbed safety condition monitoring by WSAN, this paper investigates the multi-actuator load balance, path planning of actuators and transmission interference suppression and so on. Reinforcement learning, cooperative communication and game theory are adopted to propose an energy optimization scheme for WSAN with time delay constraints. The main work is as follows:
Considering the WSAN applied in heavy bridge and roadbed safety condition monitoring, this paper analyzes the characteristics and problems of energy optimization, and proposes the system framework based on mobile data gathering and cooperative communication interference suppression. Reinforcement Learning is firstly adopted to divide the network into partitions to balance communication and energy load between actuators. Then the dynamic topology with energy sensing is generated in each partition. Based on it, the polling points selection and actuator path planning strategy are proposed. Finally, to suppress the interference from the neighbor nodes, a cooperative communication and game theory based interference suppression strategy is presented in this paper. The proposed scheme will provide an efficient solution for WSAN with low data gathering delay, high energy efficiency and high reliability.
A core aim of WSAN is to improve the energy efficiency and to optimize the network life. This paper investigates the load balance of multi-hop data gathering problems and choose polling point of network data. First, a network dynamic spanning tree is constructed based on the residual energy of wireless sensors. Then in-degree priority polling point selection algorithm is proposed with global topology information and communication load based polling point selection algorithm is proposed with local topology information. The selected polling point restricts the number of hops, balancing the energy cost decentraly and regionally as a local network data gathering point.
To satisfy the real-time requirements of WSANs, a heuristic actuator path planning algorithm with delay constraints is proposed. The algorithm first converts mobile actuator path planning problem into TSP optimization problem; then considering communication radius of polling point, the Race Search based nearest neighbor heuristic algorithm is adopted to get the optimal actuator movements, which can traverse all the polling points. Finally, the algorithm converts the time-delay constraints into distance constraints, and uses it in iterative process to obtain the optimal actuator path solution. In order to evaluate the performance of our algorithm, this paper proposes linear programming model describing the actuator path planning for actuators in space domain. The1-ε optimal algorithm is used to achieve the theoretical optimal solution of mobile data gathering. In addition, Matlab tool is used to assess the effectiveness of our algorithm in large scale WS AN.
This paper proposes an online fuzzy Q learning based energy balancing adaptive partition algorithm for multi-actuators. Every actuator is treated as an agent with learning and decision abilities, and is used to obtain the energy and state information of sensors. Then fuzzy reasoning is used to discretize the location and energy state. And Q learning state space and Q function are constructed. Then the partition center location is selected based on the current network energy distribution state. Corresponding Voronoi partition is produced to adaptively adjust the partition.
Interference among the adjacent nodes caused by the unexpected data is the significant problem, which impacts the efficiency of the data transmission. A collaborative data transmission mechanism based on interference suppression is proposed. Firstly, the cooperative communication is introduced into WSAN, and the ability of anti-interference can be improved by relay transmission and cooperative interference. Considering the selfishness of the nodes, a spectrum sharing mechanism is introduced to motivate the relay node to participate in the cooperative interference suppression, and the Stackelberg game is used to analyze the process of the cooperative interference suppression. Then, a data transmit method with interference is obtained by solving the equilibrium to select the optimal strategy for cooperative information transmission. The method can suppress the interference among nodes, and can guarantee the fairness and rationality of the cooperative data transmission. The simulation results verify the effectiveness of the proposed algorithm.
论文从应用于监测重载桥梁和路基的WSAN实例出发,分析了时延约束下多执行器WSAN能效优化的重点和难点,提出了基于移动数据汇聚和协作通信干扰抑制的WSAN系统架构,首先构建具有能量感知的动态拓扑,以此为基础选择轮询点,再规划执行器采集信息的路径；然后针对多执行器情况,采用在线模糊Q学习方法对网络进行自适应分区,保证多个执行器间的通信负载和能耗均衡；针对执行器采集信息过程中相邻节点间存在的数据干扰,提出基于协作通信和博弈论的干扰抑制方法。为实现低数据汇聚时延、高能量效率、高可靠性的WSAN提供一套有效的解决方案。
提高网络能效,优化网络生存时间是WSAN的核心目标。论文针对多跳路由数据汇聚的负载均衡问题,选择网络数据汇聚轮询点。首先根据传感器节点的剩余能量构造网络动态生成树；在全局拓扑信息可得的场景下提出基于入度优先的轮询点选择算法,在只能获得局部拓扑信息的场景下提出基于节点通信负载的轮询点选择算法。选择的轮询点作为局部网络数据的汇聚点以分散化、区域化的方式限制网络的路由跳数,均衡网络能耗。
针对WSAN的实时性要求,本文提出时延约束下的移动执行器的赛道寻优路径规划算法。以轮询点为执行器路径规划停留的参考位置,将执行器的路径规划问题转换为旅行商问题；考虑轮询点通信半径,采用基于赛道寻优的最近邻点启发式算法,求解执行器遍历所有轮询点的最优移动轨迹；同时将网络数据汇聚的时延约束转换成执行器的移动距离约束,作为算法的迭代收敛条件,保证网络数据汇聚实时性,并通过建立和求解空间域最优规划模型提供算法性能评估。
对多执行器场景,论文提出一种在线模糊Q学习的能耗均衡自适应分区算法。将每个执行器作为具有学习和决策能力的智能体,实时获取网络中传感器节点的能量和分布状态信息；再使用模糊推理机制离散化位置和能量分布状态,构建Q学习的状态空间和Q函数；根据当前的网络能量分布状态选择具有最大回报值的分区中心位置,并产生对应的Voronoi划分,实现能耗均衡的分区自适应调整。
WSAN中突发数据导致的相邻节点间的数据发送干扰是影响数据传输效率的重要问题。本文提出了一种基于协作数据汇聚的干扰抑制机制。首先将协作通信技术引入WSAN,通过协作中继转发和协作干扰转发的方式提升节点的抗干扰能力；然后考虑到节点自私特性,引入频谱共享机制激励中继节点参与协作干扰抑制；采用斯坦伯格博弈方法对协作干扰抑制过程进行分析；再通过求解博弈均衡来选择最优的策略进行协作信息传输,得到一种干扰情况下的协作数据传输方法。该方法可抑制节点间的干扰影响,确保数据公平、合理的协作传输。仿真实验验证了算法的有效性。
Abstract:The optimization of energy efficiency has been a key issue in wireless sensor and actuator networks (WSAN). Recent research has indicated that using dynamic topology control and distributed cooperation in WSAN to achieve collaborative data gathering can greatly increase the network energy efficiency. Under the background of heavy bridges and roadbed safety condition monitoring by WSAN, this paper investigates the multi-actuator load balance, path planning of actuators and transmission interference suppression and so on. Reinforcement learning, cooperative communication and game theory are adopted to propose an energy optimization scheme for WSAN with time delay constraints. The main work is as follows:
Considering the WSAN applied in heavy bridge and roadbed safety condition monitoring, this paper analyzes the characteristics and problems of energy optimization, and proposes the system framework based on mobile data gathering and cooperative communication interference suppression. Reinforcement Learning is firstly adopted to divide the network into partitions to balance communication and energy load between actuators. Then the dynamic topology with energy sensing is generated in each partition. Based on it, the polling points selection and actuator path planning strategy are proposed. Finally, to suppress the interference from the neighbor nodes, a cooperative communication and game theory based interference suppression strategy is presented in this paper. The proposed scheme will provide an efficient solution for WSAN with low data gathering delay, high energy efficiency and high reliability.
A core aim of WSAN is to improve the energy efficiency and to optimize the network life. This paper investigates the load balance of multi-hop data gathering problems and choose polling point of network data. First, a network dynamic spanning tree is constructed based on the residual energy of wireless sensors. Then in-degree priority polling point selection algorithm is proposed with global topology information and communication load based polling point selection algorithm is proposed with local topology information. The selected polling point restricts the number of hops, balancing the energy cost decentraly and regionally as a local network data gathering point.
To satisfy the real-time requirements of WSANs, a heuristic actuator path planning algorithm with delay constraints is proposed. The algorithm first converts mobile actuator path planning problem into TSP optimization problem; then considering communication radius of polling point, the Race Search based nearest neighbor heuristic algorithm is adopted to get the optimal actuator movements, which can traverse all the polling points. Finally, the algorithm converts the time-delay constraints into distance constraints, and uses it in iterative process to obtain the optimal actuator path solution. In order to evaluate the performance of our algorithm, this paper proposes linear programming model describing the actuator path planning for actuators in space domain. The1-ε optimal algorithm is used to achieve the theoretical optimal solution of mobile data gathering. In addition, Matlab tool is used to assess the effectiveness of our algorithm in large scale WS AN.
This paper proposes an online fuzzy Q learning based energy balancing adaptive partition algorithm for multi-actuators. Every actuator is treated as an agent with learning and decision abilities, and is used to obtain the energy and state information of sensors. Then fuzzy reasoning is used to discretize the location and energy state. And Q learning state space and Q function are constructed. Then the partition center location is selected based on the current network energy distribution state. Corresponding Voronoi partition is produced to adaptively adjust the partition.
Interference among the adjacent nodes caused by the unexpected data is the significant problem, which impacts the efficiency of the data transmission. A collaborative data transmission mechanism based on interference suppression is proposed. Firstly, the cooperative communication is introduced into WSAN, and the ability of anti-interference can be improved by relay transmission and cooperative interference. Considering the selfishness of the nodes, a spectrum sharing mechanism is introduced to motivate the relay node to participate in the cooperative interference suppression, and the Stackelberg game is used to analyze the process of the cooperative interference suppression. Then, a data transmit method with interference is obtained by solving the equilibrium to select the optimal strategy for cooperative information transmission. The method can suppress the interference among nodes, and can guarantee the fairness and rationality of the cooperative data transmission. The simulation results verify the effectiveness of the proposed algorithm.
引文
] I. F. Akyildiz, W. L Su, Y. Sankarasubramaniam, E. Cayirci. A survey on sensor networks[J]. IEEE Communications Magazine,2002,40(8), pp.102-114.
] C. Y. Chong, S. P. Kumar. Sensor networks:evolution, opportunities, and challenges[J]. Proceedings of the IEEE,2003,91(8), pp.1247,1256.
] 任丰原,黄海宁,林闯.无线传感器网络[J].软件学报,2003,14(7),pp.1282-1291.
] K. Sohrabi, J. Gao, V. Ailawadhi, G. J. Pottie. Protocols for self-organization of a wireless sensor network[J]. Personal Communications, IEEE,2000,7(5), pp.16-27.
] J. H. Chang, L.Tassiulas. Maximum lifetime routing in wireless sensor networks[J]. IEEE/ACM Transactions on Networking,2004,12(4), pp.609-619..
] M. Zhao, Y. Y Yang. A framework for mobile data gathering with load balanced clustering and MIMO uploading[C]. IEEE INFOCOM, Shanghai, China,2011, pp. 2759-2767.
] D. Ruffieux, M. Contaldo, C. Enz. MEMS-based all-digital frequency synthesis for ultralow-power radio for WBAN and WSN applications [C]. IEEE International Symposium on Circuits and Systems (ISCAS), Rio de Janeiro,2011, pp.157-160.
] A. A. Aziz, Y. A. Sekercioglu, P. Fitzpatrick. A survey on distributed topology control techniques for extending the lifetime of battery powered wireless sensor networks[J]. IEEE Communications Surveys & Tutorials,2013,15(1), pp.121-144.
] M. A. Labrador, P. M. Wightman. Topology Control in Wireless Sensor Networks with a companion simulation tool for teaching and research [M]. Springer Science and Business Medi, Heidelberg,2009.
0] H. Jaleel, A. Rahmani, M. Egerstedt. Duty cycle scheduling in dynamic sensor networks for controlling event detection probabilities[C]. American Control Conference (ACC), San Francisco, California, USA,2011, pp.3233-3238.
1]丁一鸣,吴昊,李承恕.无线传感器网络中一种基于簇头预测和功率控制的节能路由算法[J].铁道学报,2010,32(1),pp.43-48.
2]马奎,黄河清,沈杰等.基于混合汇聚节点的无线传感器网络数据收集方法[J].光学精密工程,2008,16(9),pp.1752-1758.
3] V. Rodoplu, T. H. Meng. Minimum energy mobile wireless networks[J]. IEEE Journal on Selected Areas in Communications,1999,17(8), pp.1333-1344.
4] L. Li, J. Y. Halpern. Minimum-energy mobile wireless networks revisited[C]. IEEE International Conference on Communications (ICC), Helsinki, Finland,2001,1, pp. 278-283.
5] S. Narayanaswamy, V. Kawadia, R. S. Sreenivasl. Power control in ad-hoc networks: Theory, architecture, algorithm and implementation of the COMPOW protocol [C]. European wireless conference, Florence, Italy,2002, pp.156-162.
[16]易军,石为人,唐云建.无线传感器/执行器网络任务动态调度策略[J].电子学报.2010,38(6),pp.1239-1244.
[17]Y. Xu, J. Heidemann, D. Estrin. Geography-informed energy conservation for ad hoc routing[C]. ACM Annual International Conference on Mobile Computing and Networking, Rome, Italy,2001, pp.70-84.
[18]C. Schurgers, V. Tsiatsis, M. B. Srivastava. STEM:Topology management for energy efficient sensor networks[C]. IEEE Aerospace Conference Proceedings, Big Sky, Montana,2002, pp.78-89.
[19]A. Cerpa, D. Estrin. ASCENT:Adaptive self-configuring sensor networks topologies [J]. IEEE Transactions on Mobile Computing,2004,3(3), pp.272-285.
[20]K. P. Naveen, A. Kumar. Relay Selection for Geographical Forwarding in Sleep-Wake Cycling Wireless Sensor Networks[J]. IEEE Transactions on Mobile Computing,2013, 12(3), pp.475-488.
[21]K. Ozaki, K. Watanabe, T. Enokido. A fault-tolerant model of wireless sensor-actuator network[J]. International Journal of Distributed Sensor Networks,2008,4(2), pp. 110-128.
[22]J. Kim, H. E Jeon, J. Lee. Network management framework and lifetime evaluation method for wireless sensor networks[J]. Integrated Computer-Aided Engineering,2012, 19(2), pp,165-178.
[23]李晓鸿,王文艳,王东.一种最大化Ad Hoc网络生存期的拓扑控制算法[J].计算机研究与发展,2013,50(3),pp.461-471.
[24]L. M. Feeney, M. Nilsson. Investigating the energy consumption of a wireless network interface in an ad hoc networking environment[C]. INFOCOM, twentieth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings, Anchorage, Alaska, USA,2001,3, pp.1548-1557.
[25]D.t Niyato, L. Xiao, P. Wang. Machine-to-machine communications for home energy management system in smart grid[J]. IEEE Communications Magazine,2011,49(4), pp. 53-59.
[26]J. Wu, F. Dai, M. Gao. On calculating power-aware connected dominating sets for efficient routing in ad hoc wireless networks [J]. Journal of Communications and Networks,2002,4(1), pp.59-70.
[27]Y. Y. Zeng, X. H. Jia, Y. X. He. Energy efficient distributed connected dominating sets construction in wireless sensor networks[C]. ACM International Conference on Wireless Communications and Mobile Computing, Vancouver, Canada,2006, pp. 797-802.
[28]L. H. Bao, J. J. Garcia-Luna-Aceves. Topology management in ad hoc networks[C]. ACM International Symposium on Mobile ad hoc Networking & Computing, Annapolis, Maryland, USA,2003, pp.129-140.
9] B. J. Chen, K. Jamieson, H. Balakrishnan. Span:An energy-efficient coordination algorithm for topology maintenance in ad hoc wireless networks[J]. Wireless networks, 2002,8(5), pp.481-494.
0] V. Kawadia, P. R. Kumar. Power control and clustering in ad hoc networks[C]. INFOCOM, IEEE Societies, IEEE Annual Joint Conference of Computer and Communications,2003,1, pp.459-469.
1] X. H. Wu; J. B. Chen. A Partition-Based Hybrid Clustering Routing Protocol for WSN[C]. IEEE International Conference on Internet Technology and Applications (iTAP), Wuhan, China,2011, pp.1-4.
2] C. Diallo, M. Marot, M. Becker. Single-node cluster reduction in WSN and energy-efficiency during cluster formation[C]. IEEE Ad Hoc Networking Workshop (Med-Hoc-Net), IF IP Annual Mediterranean, Juan Les Pons, France,2010, pp.1-10.
3] D. Sathian, R. Baskaran, P. Dhavachelvan. Lifetime enhancement by Cluster Head Cooperative Trustworthy Energy Efficient MIMO routing algorithm based on game theory for WSN[C]. International Conference on Computing Communication & Networking Technologies (ICCCNT), Coimbatore, Tamilnadu, India,2012, pp.1-5.
4] L. Kong, Q. P. Wang; Y. Zhao. Time Synchronization algorithm based on Cluster for WSN[C]. IEEE International Conference on Information Management and Engineering (ICIME), Cape Town, South Africa,2010, pp.126-130
5] H. Zhang, S. Y. L. A practical design of multi-channel MAC for cluster-tree WSN[C]. IEEE International Forum on Strategic Technology (IFOST), Harbin, Heilongjiang, China,2011,2, pp.761-764.
6] J. Wu, H. L. Li. On calculating connected dominating set for efficient routing in ad hoc wireless networks[C]. International Workshop on Discrete Algorithms and Nmethods for Mobile Computing and Communications, WA, USA,1999, pp.7-14.
7] W. R. Heinzelman, A. Chandrakasan, H. Balakrishnan. Energy-efficient communication protocol for wireless microsensor networks [C]. IEEE Annual Hawaii International Conference on System Sciences, Waikoloa, Hawaii, USA,2000, pp.1-10.
8] J. X. Guo. Sink mobility schemes in wireless sensor networks for network lifetime extension[J]. Electronic Theses and Dissertations Paper 103,2012, pp.1-73.
9] H. Nakayama, Z. M. Fadlullah, N. Ansar. A novel scheme for wsan sink mobility based on clustering and set packing techniques[J]. IEEE Transactions on Automatic Control, 2011,56(10), pp.2381-2389.
0]陈涛,郭得科,罗雪山.一种基于移动基站的无线传感器网络数据收集方法[J].国防科技大学学报,2011,33(2),pp.49-53.
1] P. Makris, D. N. Skoutas, C. Skianis. A survey on context-aware mobile and wireless networking:On networking and computing environments integration[J]. IEEE Communications Surveys & Tutorials,2013,15(1), pp.362-386.
2] H. Nishiyama, T. Ngo, N. Ansari. On minimizing the impact of mobility on topology control in mobile ad hoc networks[J]. IEEE Transactions on Wireless Communications, 2012,11(3), pp.1158-1166.
[43]R. Olfati-Saber, P. Jalalkamali. Coupled Distributed Estimation and Control for Mobile Sensor Networks[J]. IEEE Transactions on Automatic Control,2012,57(10), pp. 2609-2614.
[44]D. Raychaudhuri, N. B. Mandayam. Frontiers of Wireless and Mobile Communications[J]. Proceedings of the IEEE,2012,100(4), pp.824-840.
[45]D. G. Reina, S. L. Toral, P. Jonhson, et al. Hybrid Flooding Scheme for Mobile Ad Hoc Networks[J]. IEEE Communications Letters,2013,17(3), pp.592-595.
[46]S. S, Kashi, M. Sharifi. Connectivity Weakness Impacts on Coordination in Wireless Sensor and Actor Networks[J]. IEEE Communications Surveys & Tutorials,2013,15(1), pp.145-166.
[47]Yi Shi, Y. T. Hou. Some Fundamental Results on Base Station Movement Problem for Wireless Sensor Networks[J]. IEEE/ACM Transactions on Networking,2012,20(4), pp. 1054-1067.
[48]梁俊斌.无线传感网中低能耗数据收集协议研究[D].中南大学,2010.DOI：10.7666/d.y1918559.
[49]E. P. D. Freitas, T. Heimfarth, A Vinel. Cooperation among wirelessly connected static and mobile sensor nodes for surveillance applications[J]. Sensors,2013,13(10), pp 12903-12928.
[50]G. L. Xing, M. M Li, T. Wang. Efficient rendezvous algorithms for mobility-enabled wireless sensor networks[J]. IEEE Transactions on Mobile Computing,2012,11(1), pp. 47-60.
[51]袁凌云,王兴超,徐天伟.基于移动Agent和WSN的突发事件场景数据收集算法研究[J].电子与信息学报,2010,32(8),pp.1974-1979.
[52]G. L. Xing, T. Wang, Z. H. Xie. Rendezvous planning in wireless sensor networks with mobile elements[J]. IEEE Transactions on Mobile Computing,2008,7(12), pp. 1430-1443.
[53]W. Liu, K. J. Lu, J. P. Wang. On the Throughput Capacity of Wireless Sensor Networks With Mobile Relays[J]. IEEE Transactions on Vehicular Technology,2012,61(4), pp. 1801-1809.
[54]W. Liu, K. J. Lu, J. P. Wang. Performance Analysis of Wireless Sensor Networks With Mobile Sinks[J]. IEEE Transactions on Vehicular Technology,2012,61(6), pp. 2777-2788.
[55]朱金奇,刘明,龚海刚.延迟容忍移动传感器网络中基于选择复制的数据传输[J].软件学报,2009,20(8),pp.2227-2240.
[56]J. Luo, D. Wang, Q. Zhang. On the Double Mobility Problem for Water Surface Coverage with Mobile Sensor Networks [J]. IEEE Transactions on Parallel and Distributed Systems,2012,23(1), pp.146-159.
[57]孙利民,熊永平,马建.机会移动传感器网络中的自适应数据收集机制[J].通信学报,2008,29(11),pp.186-193.
[58]A. S. Arifin, T. Ohtsuki. Linear Precoding for Distributed Estimation of Correlated Sources in WSN MIMO System[C]. IEEE Vehicular Technology Conference (VTC), Las Vegas, USA,2013, pp.1-5.
[59]G. D. Teng, K. G. Zheng, W. Dong. SDMA:A Simulation-Driven Dynamic Memory Allocator for Wireless Sensor Networks [C]. International Conference on Sensor Technologies and Applications (SENSORCOMM), Cap Esterel, France,2008. pp. 462-467.
[60]Y. Y. Gu, D. Bozdag, E. Ekici. Mobile element based differentiated message delivery in wireless sensor networks[C]. International Symposium on World of Wireless on Mobile and Multimedia Networks, New York,2006, pp.83-92.
[61]E. Ekici, Y. Y. Gu, D. Bozdag. Mobility-based communication in wireless sensor networks[J]. IEEE Communications Magazine,2006,44(7), pp.56-52.
[62]S. Gao, H. K. Zhang, K. D. Sajal. Efficient data collection in wireless sensor networks with path-constrained mobile sinks[J]. IEEE Transactions on Mobile Computing,2011, 10(4), pp.592-608.
[63]李洪峻,卜彦龙,薛晗.面向无线传感器网络节点定位的移动锚节点路径规划[J].计算机研究与发展,2009,46(1),pp.129-136.
[64]C. Rahul, S. S. Roy, S. Jain. Data mules:Modeling and analysis of a three-tier architecture for sparse sensor networks[J]. Ad Hoc Networks,2003,1(2), pp.215-233.
[65]C. Konstantopoulos, G. Pantziou, D. Gavalas. A rendezvous-based approach enabling energy-efficient sensory data collection with mobile sinks [J]. IEEE Transactions on Parallel and Distributed Systems,2012,23(5), pp.809-817.
[66]J. Luo, J. P. Hubaux. Joint sink mobility and routing to maximize the lifetime of wireless sensor networks:the case of constrained mobility[J]. IEEE/ACM Transactions on Networking (TON),2010,18(3), pp.871-884.
[67]周小佳,吴侠,闫斌.基于移动基站的动态无线传感器网络[J].西南交通大学学报,2011,46(5),pp.793-796.
[68]M. Gatzianas, L. Georgiadis. A distributed algorithm for maximum lifetime routing in sensor networks with mobile sink[J]. IEEE Transactions on Wireless Communications, 2008,7(3), pp.984-994.
[69]G. Y. Keung, B. Li, Q. Zhang. Message delivery capacity in delay-constrained mobile sensor networks:Bounds and realization[J]. IEEE Transactions on Wireless Communications,2011,10(5), pp.1552-1559.
[70]熊永平,孙利民,马建.VSR：多sink机会移动传感器网络的数据收集[J].计算机研究与发展,2010,47(8),pp.1450-1458.
[71]M. Zhao, Y. Y. Yang. Bounded relay hop mobile data gathering in wireless sensor networks[J]. IEEE Transactions on Computers,2012,61(2), pp.265-277.
2] S. Basagni, A. Carosi, C. Petrioli. Coordinated and controlled mobility of multiple sinks for maximizing the lifetime of wireless sensor networks [J]. Wireless Networks,2011, 17(3), pp.759-778.
3] A. T. Erman, P. Havinga. Data dissemination of emergency messages in mobile multi-sink wireless sensor networks[C]. IFIP Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net), Juan Les Pins, France,2010, pp.1-8.
4]石高涛,廖明宏.传感器网络中具有负载平衡的移动协助数据收集模式[J].软件学报,2007,18(9),pp.2235-2244.
5] M. Zhao, M. Ma, Y. Y. Yang. Efficient data gathering with mobile collectors and space-division multiple access technique in wireless sensor networks [J]. IEEE Transactions on Computers,2011,60(3), pp.400-417.
6] F. El-Moukaddem, E. Torng, G. L. Xing. Mobile relay configuration in data-intensive wireless sensor networks[J]. IEEE Transactions on Mobile Computing,2013,12(2), pp. 261-273.
7] Z. H. Xu, C. L. Chen, B. Cheng. Sharing Mobility Strategy Improves Location Service in Wireless Sensor and Actor Networks[J]. IEEE Communications Letters,2012,16(6), pp.858-861.
8]易军,石为人,许磊.无线传感器/执行器网络多目标任务调度策略[J].控制与决策,2011,26(2),pp.191-195.
9] M. S. Stankovic, K. H. Johansson, D. M. Stipanovic. Distributed Seeking of Nash Equilibria With Applications to Mobile Sensor Networks [J]. IEEE Transactions on Automatic Control,2012,57(4), pp.904-919.
0] A. A. Taleb, T. A. Alhmiedat, R. A. Taleb. Sink Mobility Model for Wireless Sensor Networks[J]. Arabian Journal for Science and Engineering,2014,39(3), pp. 1775-1784.
1]袁远,彭宇行,李姗姗.高效的移动sink路由问题的启发式算法[J].通信学报,2011,32(10),pp.107-117.
2] P. Kabelikova. Graph Partitioning Using Spectral Methods. VSB-Technical University of Ostrava,2006.
3] J. Chlebikova. Approximating the maximally balanced connected partition problem in graphs[J]. Information Processing Letters,1996,60(5), pp.225-230.
4]孙永进,孙雨耕,房朝晖.无线传感器网络的连通与覆盖,天津大学学报,38(1),14-17.2005.
5] M. Zhao, Y. Y. Yang. Optimization-Based Distributed Algorithms for Mobile Data Gathering in Wireless Sensor Networks[J]. IEEE Transactions on Mobile Computing, 2012,11(10), pp.1464-1477.
6]李哲涛,朱更明,王志强.低占空比、低碰撞的异步无线传感器网络MAC协议[J].通信学报,2013,(10),pp.9-16.
7] N. Ahmed, S. S. Kanhere, S. Jha. Mitigating the effect of interference in wireless sensor networks[C]. IEEEConference on Local Computer Networks (LCN), Denver, Colorado, U.S.A,2010,pp.160-167.
[88]Q. Danyang, M. Lin, W. Erfu. An interference suppression mechanism for WSN[C]. International Conference on Sensor Network Security Technology and Privacy Communication System (SNS & PCS), Harbin, China,2013, pp.28-33.
[89]S. S. Liu, G. L. Xing, H. W. Zhang,. Passive interference measurement in wireless sensor networks[C]. IEEE International Conference on Network Protocols (ICNP), Kyoto, Japan,2010, pp.52-61.
[90]F. Vanheel, J. Verhaevert, Ingrid Moerman. Study on distance of interference sources on wireless sensor network[C]. European Microwave Conference (EuMC), Amsterdam, The Netherlands,2008, pp.175-178.
[91]严国强,王福豹,马超.一种干扰分离多路径路由协议的设计与实现[J].计算机研究与发展,2010,47(z2),pp.200-204.
[92]D. Manivannan, P. Neelamegam. WSN:Key Issues in Key Management Schemes-A Review[J]. Research Journal of Applied Science, Engineering and Technology,2012, 4(18), pp.3188-3200.
[93]T. Wang, C. R. D. Lamare, A. Schmeink. Joint linear receiver design and power allocation using alternating optimization algorithms for wireless sensor networks[J]. IEEE Transactions on Vehicular Technology,2012,61(9), pp.4129-4141.
[94]K. Rajendiran, R. Sankararajan, R. Palaniappan. A Secure Key Predistribution Scheme for WSN Using Elliptic Curve Cryptography [J]. ETRI Journal,2011,33(5), pp. 791-801.
[95]S. Sugiura, Decentralized-precoding aided rateless codes for wireless sensor networks[J]. IEEE Communications Letters,2012,16(4), pp.506-509.
[96]M. Petrova, J. Riihijarvi, P. Mahonen. Performance study of IEEE 802.15.4 using measurements and simulations [C]. IEEE Wireless Communications and Networking Conference (WCNC), Las Vegas, NV, USA,2006,1, pp.487-492.
[97]S. Y. Shin, H. S. Park. Wook Hyun Kwon. Mutual interference analysis of IEEE 802.15. 4 and IEEE 802.11 b[J]. Computer Networks,2007,51(12), pp.3338-3353.
[98]W. Yuan, X. Y. Wang, J. P. M. G. Linnartz. A Coexistence Model of IEEE 802.15.4 and IEEE 802.11 b/g[C]. IEEE Symposium on Communications and Vehicular Technology in the Benelux (SCVT), Delft, The Netherlands,2007, pp.1-5.
[99]F. Yao, S. H. Yang, W. Zheng. Mitigating interference caused by IEEE 802.11 b in the IEEE 802.15.4 WSN within the environment of smart house[C]. IEEE International Conference on Systems Man and Cybernetics (SMC), Istanbul, Turkey,2010, pp. 2800-2807.
[100]C. R. Stevenson, G. Chouinard, Z. D. Lei. IEEE 802.22:The first cognitive radio wireless regional area network standard[J]. IEEE Communications Magazine,2009, 47(1), pp.130-138.
[101]J. Mitola. Cognitive Radio--An Integrated Agent Architecture for Software Defined Radio [J]. PhD Dissertation, Royal Inst. Technol. (KTH), Stockholm, Sweden 2000, pp. 1-313.
[102]T. K. Yucek, H. Arslan. A survey of spectrum sensing algorithms for cognitive radio applications[J]. IEEE Communications Surveys & Tutorials,2009,11(1), pp.116-130.
[103]邓延安,沈连丰,许波.一种应用于多跳网络的可调占空比固定时延MAC协议[J].东南大学学报(自然科学版),2008,38(6),pp.942-948.
[104]S. Gao, L. J. Qian, Dhadesugoor R. Vaman. Distributed energy efficient spectrum access in wireless cognitive radio sensor networks[C]. IEEE Wireless communications and networking Conference (WCNC), Las Vegas, Nevada, USA,2008, pp.1442-1447.
[105]K. Letaief, W. Zhang. Cooperative communications for cognitive radio networks[J]. Proceedings of the IEEE,2009,97(5), pp.878-893.
[106]J. N. Laneman, D. N. C. Tse, G. W. Wornell. Cooperative diversity in wireless networks: Efficient protocols and outage behavior[J]. IEEE Transactions on Information Theory, 2004,50(12), pp.3062-3080.
[107]W. F. Su, A. K. Sadek, K. J. R. Liu. Cooperative communication protocols in wireless networks:Performance analysis and optimum power allocation[J]. Wireless Personal Communications,2008,44(2), pp.181-217.
[108]A. K. Sadek, W. F Su, K.J. R. Liu. Multinode cooperative communications in wireless networks[J]. IEEE Transactions on Signal Processing,2007,55(1), pp.341-355.
[109]刘云璐,蒲菊华,方维维.一种无线传感器网络MAC协议优化算法[J].计算机学报,2012,35(3),pp.529-539.
[110]S. I. Johnsi, S. Radha. Investigations on distributed detection performance of Neyman-Pearson detection scheme using constructive interference technique for WSN[C]. International Conference on Wireless Communication and Sensor Computing (ICWCSC), Genoa, Italy,2010, pp.1-5.
[111]M. H. Tan, R. L.Yu, S. J. Li, X. D. Wang. Multipath routing protocol with load balancing in WSN considering interference[C]. IEEE Conference on Industrial Electronics and Applications (ICIEA), Singapore,2011, pp.1062-1067
[112]A.V. Kini, N. Singhal, S. Weber. Performance of a WSN in the Presence of Channel Variations and Interference[C]. IEEE Wireless Communications and Networking Conference (WCNC), Las Vegas, Nevada, USA,2008, pp.2899-2904.
[113]T. F. Wykret, L. H. A. Correia, D. F. Macedo, J. C. Giacomin, L.T. Andrade Evaluation and avoidance of interference in WSN:A multi-radio node prototype using Dynamic Spectrum Allocation[C]. IEEE Wireless Days (WD), Rio de Janeiro, Brazil, 2013,pp.1-3.
[114]J. Lee, W. C. Jeong. Performance analysis of IEEE 802.15.4e DSME MAC protocol under WLAN interference[C]. International Conference on ICT Convergence (ICTC), Hangzhou,China,2012, pp.741-746.
[115]N. Ahmed, S. Kanhere, J. Sanjay. Poster abstract:Multi-channel interference in wireless sensor networks [C]. International Conference on Information Processing in Sensor Networks (IPSN), San Francisco, California, USA,2009, pp.367-368.
[116]I. F. Akyildiz, W. Y. Lee, M. C. Vuran. Next generation/dynamic spectrum access/cognitive radio wireless networks:a survey [J]. Computer Networks,2006, 50(13), pp.2127-2159.
[117]蒋富龙,刘昊.一种无线传感器网络多信道动态路径转发协议[J].东南大学学报(自然科学版),2014,(1),pp.28-33.
[118]K. Oikonomou, S. Aissa. Dynamic sink assignment for efficient energy consumption in wireless sensor networks[C]. IEEE Wireless Communications and Networking Conference (WCNC), Paris, France,2012, pp.1876-1881.
[119]P. B. Mirchandani, R. L. Francis, J. Wiley & Sons. Discrete Location Theory[J]., Networks,1990,23(2), pp.124-125.
[120]O. B. Akan, O. Karli, O. Ergul. Cognitive Radio Sensor Network[J].IEEE Network, 2009,23(4), pp.34-40.
[121]Z. Li, F. R. Yu, M. Y. Huang. A Distributed Consensus-Based Cooperative Spectrum-Sensing Scheme in Cognitive Radios[J]. IEEE Transactions on Vehicular Technology,2010,59(1), pp.383-393.
[122]H. Z. Zhang, Z. Y Zhang, H. Y. Dai, R. Yin, X. M. Chen. Distributed Spectrum-Aware Clustering in Cognitive Radio Sensor Networks[C]. IEEE Global Telecommunications Conference (GLOBECOM), Houston, TX, USA,2011, pp.1-6.
[123]G. R. Ding, J. L. Wang, Q. H. Wu, F. Song, Y. Y. Chen. Spectrum Sensing in Opportunity-Heterogeneous Cognitive Sensor Networks:How to Cooperate? [J]. IEEE Sensors Journal,2013,13(11), pp.4247-4255.
[124]S. L. Xie, Y. Liu, Y. Zhang, R. Yu. A Parallel Cooperative Spectrum Sensing in Cognitive Radio Networks[J]. IEEE Transactions on Vehicular Technology,2010,59(8), pp.4079-4092.
[125]S. Sridharan, S. Vishwanath, S. A. Jafar and S. Shamai. On the capacity of cognitive relay assisted gaussian interference channel[C]. IEEE International Symposium on Information Theory (ISTT), Toronto,2008, pp.549-553.
[126]O. Sahin, E Erkip. Achievable rates for the Gaussian interference relay channel[C] IEEE Global Telecommunications Conference on Communication Theory Symposium (GLOBECOM), Washington D.C.,2007, pp.1627-1631.
] C. Y. Chong, S. P. Kumar. Sensor networks:evolution, opportunities, and challenges[J]. Proceedings of the IEEE,2003,91(8), pp.1247,1256.
] 任丰原,黄海宁,林闯.无线传感器网络[J].软件学报,2003,14(7),pp.1282-1291.
] K. Sohrabi, J. Gao, V. Ailawadhi, G. J. Pottie. Protocols for self-organization of a wireless sensor network[J]. Personal Communications, IEEE,2000,7(5), pp.16-27.
] J. H. Chang, L.Tassiulas. Maximum lifetime routing in wireless sensor networks[J]. IEEE/ACM Transactions on Networking,2004,12(4), pp.609-619..
] M. Zhao, Y. Y Yang. A framework for mobile data gathering with load balanced clustering and MIMO uploading[C]. IEEE INFOCOM, Shanghai, China,2011, pp. 2759-2767.
] D. Ruffieux, M. Contaldo, C. Enz. MEMS-based all-digital frequency synthesis for ultralow-power radio for WBAN and WSN applications [C]. IEEE International Symposium on Circuits and Systems (ISCAS), Rio de Janeiro,2011, pp.157-160.
] A. A. Aziz, Y. A. Sekercioglu, P. Fitzpatrick. A survey on distributed topology control techniques for extending the lifetime of battery powered wireless sensor networks[J]. IEEE Communications Surveys & Tutorials,2013,15(1), pp.121-144.
] M. A. Labrador, P. M. Wightman. Topology Control in Wireless Sensor Networks with a companion simulation tool for teaching and research [M]. Springer Science and Business Medi, Heidelberg,2009.
0] H. Jaleel, A. Rahmani, M. Egerstedt. Duty cycle scheduling in dynamic sensor networks for controlling event detection probabilities[C]. American Control Conference (ACC), San Francisco, California, USA,2011, pp.3233-3238.
1]丁一鸣,吴昊,李承恕.无线传感器网络中一种基于簇头预测和功率控制的节能路由算法[J].铁道学报,2010,32(1),pp.43-48.
2]马奎,黄河清,沈杰等.基于混合汇聚节点的无线传感器网络数据收集方法[J].光学精密工程,2008,16(9),pp.1752-1758.
3] V. Rodoplu, T. H. Meng. Minimum energy mobile wireless networks[J]. IEEE Journal on Selected Areas in Communications,1999,17(8), pp.1333-1344.
4] L. Li, J. Y. Halpern. Minimum-energy mobile wireless networks revisited[C]. IEEE International Conference on Communications (ICC), Helsinki, Finland,2001,1, pp. 278-283.
5] S. Narayanaswamy, V. Kawadia, R. S. Sreenivasl. Power control in ad-hoc networks: Theory, architecture, algorithm and implementation of the COMPOW protocol [C]. European wireless conference, Florence, Italy,2002, pp.156-162.
[16]易军,石为人,唐云建.无线传感器/执行器网络任务动态调度策略[J].电子学报.2010,38(6),pp.1239-1244.
[17]Y. Xu, J. Heidemann, D. Estrin. Geography-informed energy conservation for ad hoc routing[C]. ACM Annual International Conference on Mobile Computing and Networking, Rome, Italy,2001, pp.70-84.
[18]C. Schurgers, V. Tsiatsis, M. B. Srivastava. STEM:Topology management for energy efficient sensor networks[C]. IEEE Aerospace Conference Proceedings, Big Sky, Montana,2002, pp.78-89.
[19]A. Cerpa, D. Estrin. ASCENT:Adaptive self-configuring sensor networks topologies [J]. IEEE Transactions on Mobile Computing,2004,3(3), pp.272-285.
[20]K. P. Naveen, A. Kumar. Relay Selection for Geographical Forwarding in Sleep-Wake Cycling Wireless Sensor Networks[J]. IEEE Transactions on Mobile Computing,2013, 12(3), pp.475-488.
[21]K. Ozaki, K. Watanabe, T. Enokido. A fault-tolerant model of wireless sensor-actuator network[J]. International Journal of Distributed Sensor Networks,2008,4(2), pp. 110-128.
[22]J. Kim, H. E Jeon, J. Lee. Network management framework and lifetime evaluation method for wireless sensor networks[J]. Integrated Computer-Aided Engineering,2012, 19(2), pp,165-178.
[23]李晓鸿,王文艳,王东.一种最大化Ad Hoc网络生存期的拓扑控制算法[J].计算机研究与发展,2013,50(3),pp.461-471.
[24]L. M. Feeney, M. Nilsson. Investigating the energy consumption of a wireless network interface in an ad hoc networking environment[C]. INFOCOM, twentieth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings, Anchorage, Alaska, USA,2001,3, pp.1548-1557.
[25]D.t Niyato, L. Xiao, P. Wang. Machine-to-machine communications for home energy management system in smart grid[J]. IEEE Communications Magazine,2011,49(4), pp. 53-59.
[26]J. Wu, F. Dai, M. Gao. On calculating power-aware connected dominating sets for efficient routing in ad hoc wireless networks [J]. Journal of Communications and Networks,2002,4(1), pp.59-70.
[27]Y. Y. Zeng, X. H. Jia, Y. X. He. Energy efficient distributed connected dominating sets construction in wireless sensor networks[C]. ACM International Conference on Wireless Communications and Mobile Computing, Vancouver, Canada,2006, pp. 797-802.
[28]L. H. Bao, J. J. Garcia-Luna-Aceves. Topology management in ad hoc networks[C]. ACM International Symposium on Mobile ad hoc Networking & Computing, Annapolis, Maryland, USA,2003, pp.129-140.
9] B. J. Chen, K. Jamieson, H. Balakrishnan. Span:An energy-efficient coordination algorithm for topology maintenance in ad hoc wireless networks[J]. Wireless networks, 2002,8(5), pp.481-494.
0] V. Kawadia, P. R. Kumar. Power control and clustering in ad hoc networks[C]. INFOCOM, IEEE Societies, IEEE Annual Joint Conference of Computer and Communications,2003,1, pp.459-469.
1] X. H. Wu; J. B. Chen. A Partition-Based Hybrid Clustering Routing Protocol for WSN[C]. IEEE International Conference on Internet Technology and Applications (iTAP), Wuhan, China,2011, pp.1-4.
2] C. Diallo, M. Marot, M. Becker. Single-node cluster reduction in WSN and energy-efficiency during cluster formation[C]. IEEE Ad Hoc Networking Workshop (Med-Hoc-Net), IF IP Annual Mediterranean, Juan Les Pons, France,2010, pp.1-10.
3] D. Sathian, R. Baskaran, P. Dhavachelvan. Lifetime enhancement by Cluster Head Cooperative Trustworthy Energy Efficient MIMO routing algorithm based on game theory for WSN[C]. International Conference on Computing Communication & Networking Technologies (ICCCNT), Coimbatore, Tamilnadu, India,2012, pp.1-5.
4] L. Kong, Q. P. Wang; Y. Zhao. Time Synchronization algorithm based on Cluster for WSN[C]. IEEE International Conference on Information Management and Engineering (ICIME), Cape Town, South Africa,2010, pp.126-130
5] H. Zhang, S. Y. L. A practical design of multi-channel MAC for cluster-tree WSN[C]. IEEE International Forum on Strategic Technology (IFOST), Harbin, Heilongjiang, China,2011,2, pp.761-764.
6] J. Wu, H. L. Li. On calculating connected dominating set for efficient routing in ad hoc wireless networks[C]. International Workshop on Discrete Algorithms and Nmethods for Mobile Computing and Communications, WA, USA,1999, pp.7-14.
7] W. R. Heinzelman, A. Chandrakasan, H. Balakrishnan. Energy-efficient communication protocol for wireless microsensor networks [C]. IEEE Annual Hawaii International Conference on System Sciences, Waikoloa, Hawaii, USA,2000, pp.1-10.
8] J. X. Guo. Sink mobility schemes in wireless sensor networks for network lifetime extension[J]. Electronic Theses and Dissertations Paper 103,2012, pp.1-73.
9] H. Nakayama, Z. M. Fadlullah, N. Ansar. A novel scheme for wsan sink mobility based on clustering and set packing techniques[J]. IEEE Transactions on Automatic Control, 2011,56(10), pp.2381-2389.
0]陈涛,郭得科,罗雪山.一种基于移动基站的无线传感器网络数据收集方法[J].国防科技大学学报,2011,33(2),pp.49-53.
1] P. Makris, D. N. Skoutas, C. Skianis. A survey on context-aware mobile and wireless networking:On networking and computing environments integration[J]. IEEE Communications Surveys & Tutorials,2013,15(1), pp.362-386.
2] H. Nishiyama, T. Ngo, N. Ansari. On minimizing the impact of mobility on topology control in mobile ad hoc networks[J]. IEEE Transactions on Wireless Communications, 2012,11(3), pp.1158-1166.
[43]R. Olfati-Saber, P. Jalalkamali. Coupled Distributed Estimation and Control for Mobile Sensor Networks[J]. IEEE Transactions on Automatic Control,2012,57(10), pp. 2609-2614.
[44]D. Raychaudhuri, N. B. Mandayam. Frontiers of Wireless and Mobile Communications[J]. Proceedings of the IEEE,2012,100(4), pp.824-840.
[45]D. G. Reina, S. L. Toral, P. Jonhson, et al. Hybrid Flooding Scheme for Mobile Ad Hoc Networks[J]. IEEE Communications Letters,2013,17(3), pp.592-595.
[46]S. S, Kashi, M. Sharifi. Connectivity Weakness Impacts on Coordination in Wireless Sensor and Actor Networks[J]. IEEE Communications Surveys & Tutorials,2013,15(1), pp.145-166.
[47]Yi Shi, Y. T. Hou. Some Fundamental Results on Base Station Movement Problem for Wireless Sensor Networks[J]. IEEE/ACM Transactions on Networking,2012,20(4), pp. 1054-1067.
[48]梁俊斌.无线传感网中低能耗数据收集协议研究[D].中南大学,2010.DOI：10.7666/d.y1918559.
[49]E. P. D. Freitas, T. Heimfarth, A Vinel. Cooperation among wirelessly connected static and mobile sensor nodes for surveillance applications[J]. Sensors,2013,13(10), pp 12903-12928.
[50]G. L. Xing, M. M Li, T. Wang. Efficient rendezvous algorithms for mobility-enabled wireless sensor networks[J]. IEEE Transactions on Mobile Computing,2012,11(1), pp. 47-60.
[51]袁凌云,王兴超,徐天伟.基于移动Agent和WSN的突发事件场景数据收集算法研究[J].电子与信息学报,2010,32(8),pp.1974-1979.
[52]G. L. Xing, T. Wang, Z. H. Xie. Rendezvous planning in wireless sensor networks with mobile elements[J]. IEEE Transactions on Mobile Computing,2008,7(12), pp. 1430-1443.
[53]W. Liu, K. J. Lu, J. P. Wang. On the Throughput Capacity of Wireless Sensor Networks With Mobile Relays[J]. IEEE Transactions on Vehicular Technology,2012,61(4), pp. 1801-1809.
[54]W. Liu, K. J. Lu, J. P. Wang. Performance Analysis of Wireless Sensor Networks With Mobile Sinks[J]. IEEE Transactions on Vehicular Technology,2012,61(6), pp. 2777-2788.
[55]朱金奇,刘明,龚海刚.延迟容忍移动传感器网络中基于选择复制的数据传输[J].软件学报,2009,20(8),pp.2227-2240.
[56]J. Luo, D. Wang, Q. Zhang. On the Double Mobility Problem for Water Surface Coverage with Mobile Sensor Networks [J]. IEEE Transactions on Parallel and Distributed Systems,2012,23(1), pp.146-159.
[57]孙利民,熊永平,马建.机会移动传感器网络中的自适应数据收集机制[J].通信学报,2008,29(11),pp.186-193.
[58]A. S. Arifin, T. Ohtsuki. Linear Precoding for Distributed Estimation of Correlated Sources in WSN MIMO System[C]. IEEE Vehicular Technology Conference (VTC), Las Vegas, USA,2013, pp.1-5.
[59]G. D. Teng, K. G. Zheng, W. Dong. SDMA:A Simulation-Driven Dynamic Memory Allocator for Wireless Sensor Networks [C]. International Conference on Sensor Technologies and Applications (SENSORCOMM), Cap Esterel, France,2008. pp. 462-467.
[60]Y. Y. Gu, D. Bozdag, E. Ekici. Mobile element based differentiated message delivery in wireless sensor networks[C]. International Symposium on World of Wireless on Mobile and Multimedia Networks, New York,2006, pp.83-92.
[61]E. Ekici, Y. Y. Gu, D. Bozdag. Mobility-based communication in wireless sensor networks[J]. IEEE Communications Magazine,2006,44(7), pp.56-52.
[62]S. Gao, H. K. Zhang, K. D. Sajal. Efficient data collection in wireless sensor networks with path-constrained mobile sinks[J]. IEEE Transactions on Mobile Computing,2011, 10(4), pp.592-608.
[63]李洪峻,卜彦龙,薛晗.面向无线传感器网络节点定位的移动锚节点路径规划[J].计算机研究与发展,2009,46(1),pp.129-136.
[64]C. Rahul, S. S. Roy, S. Jain. Data mules:Modeling and analysis of a three-tier architecture for sparse sensor networks[J]. Ad Hoc Networks,2003,1(2), pp.215-233.
[65]C. Konstantopoulos, G. Pantziou, D. Gavalas. A rendezvous-based approach enabling energy-efficient sensory data collection with mobile sinks [J]. IEEE Transactions on Parallel and Distributed Systems,2012,23(5), pp.809-817.
[66]J. Luo, J. P. Hubaux. Joint sink mobility and routing to maximize the lifetime of wireless sensor networks:the case of constrained mobility[J]. IEEE/ACM Transactions on Networking (TON),2010,18(3), pp.871-884.
[67]周小佳,吴侠,闫斌.基于移动基站的动态无线传感器网络[J].西南交通大学学报,2011,46(5),pp.793-796.
[68]M. Gatzianas, L. Georgiadis. A distributed algorithm for maximum lifetime routing in sensor networks with mobile sink[J]. IEEE Transactions on Wireless Communications, 2008,7(3), pp.984-994.
[69]G. Y. Keung, B. Li, Q. Zhang. Message delivery capacity in delay-constrained mobile sensor networks:Bounds and realization[J]. IEEE Transactions on Wireless Communications,2011,10(5), pp.1552-1559.
[70]熊永平,孙利民,马建.VSR：多sink机会移动传感器网络的数据收集[J].计算机研究与发展,2010,47(8),pp.1450-1458.
[71]M. Zhao, Y. Y. Yang. Bounded relay hop mobile data gathering in wireless sensor networks[J]. IEEE Transactions on Computers,2012,61(2), pp.265-277.
2] S. Basagni, A. Carosi, C. Petrioli. Coordinated and controlled mobility of multiple sinks for maximizing the lifetime of wireless sensor networks [J]. Wireless Networks,2011, 17(3), pp.759-778.
3] A. T. Erman, P. Havinga. Data dissemination of emergency messages in mobile multi-sink wireless sensor networks[C]. IFIP Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net), Juan Les Pins, France,2010, pp.1-8.
4]石高涛,廖明宏.传感器网络中具有负载平衡的移动协助数据收集模式[J].软件学报,2007,18(9),pp.2235-2244.
5] M. Zhao, M. Ma, Y. Y. Yang. Efficient data gathering with mobile collectors and space-division multiple access technique in wireless sensor networks [J]. IEEE Transactions on Computers,2011,60(3), pp.400-417.
6] F. El-Moukaddem, E. Torng, G. L. Xing. Mobile relay configuration in data-intensive wireless sensor networks[J]. IEEE Transactions on Mobile Computing,2013,12(2), pp. 261-273.
7] Z. H. Xu, C. L. Chen, B. Cheng. Sharing Mobility Strategy Improves Location Service in Wireless Sensor and Actor Networks[J]. IEEE Communications Letters,2012,16(6), pp.858-861.
8]易军,石为人,许磊.无线传感器/执行器网络多目标任务调度策略[J].控制与决策,2011,26(2),pp.191-195.
9] M. S. Stankovic, K. H. Johansson, D. M. Stipanovic. Distributed Seeking of Nash Equilibria With Applications to Mobile Sensor Networks [J]. IEEE Transactions on Automatic Control,2012,57(4), pp.904-919.
0] A. A. Taleb, T. A. Alhmiedat, R. A. Taleb. Sink Mobility Model for Wireless Sensor Networks[J]. Arabian Journal for Science and Engineering,2014,39(3), pp. 1775-1784.
1]袁远,彭宇行,李姗姗.高效的移动sink路由问题的启发式算法[J].通信学报,2011,32(10),pp.107-117.
2] P. Kabelikova. Graph Partitioning Using Spectral Methods. VSB-Technical University of Ostrava,2006.
3] J. Chlebikova. Approximating the maximally balanced connected partition problem in graphs[J]. Information Processing Letters,1996,60(5), pp.225-230.
4]孙永进,孙雨耕,房朝晖.无线传感器网络的连通与覆盖,天津大学学报,38(1),14-17.2005.
5] M. Zhao, Y. Y. Yang. Optimization-Based Distributed Algorithms for Mobile Data Gathering in Wireless Sensor Networks[J]. IEEE Transactions on Mobile Computing, 2012,11(10), pp.1464-1477.
6]李哲涛,朱更明,王志强.低占空比、低碰撞的异步无线传感器网络MAC协议[J].通信学报,2013,(10),pp.9-16.
7] N. Ahmed, S. S. Kanhere, S. Jha. Mitigating the effect of interference in wireless sensor networks[C]. IEEEConference on Local Computer Networks (LCN), Denver, Colorado, U.S.A,2010,pp.160-167.
[88]Q. Danyang, M. Lin, W. Erfu. An interference suppression mechanism for WSN[C]. International Conference on Sensor Network Security Technology and Privacy Communication System (SNS & PCS), Harbin, China,2013, pp.28-33.
[89]S. S. Liu, G. L. Xing, H. W. Zhang,. Passive interference measurement in wireless sensor networks[C]. IEEE International Conference on Network Protocols (ICNP), Kyoto, Japan,2010, pp.52-61.
[90]F. Vanheel, J. Verhaevert, Ingrid Moerman. Study on distance of interference sources on wireless sensor network[C]. European Microwave Conference (EuMC), Amsterdam, The Netherlands,2008, pp.175-178.
[91]严国强,王福豹,马超.一种干扰分离多路径路由协议的设计与实现[J].计算机研究与发展,2010,47(z2),pp.200-204.
[92]D. Manivannan, P. Neelamegam. WSN:Key Issues in Key Management Schemes-A Review[J]. Research Journal of Applied Science, Engineering and Technology,2012, 4(18), pp.3188-3200.
[93]T. Wang, C. R. D. Lamare, A. Schmeink. Joint linear receiver design and power allocation using alternating optimization algorithms for wireless sensor networks[J]. IEEE Transactions on Vehicular Technology,2012,61(9), pp.4129-4141.
[94]K. Rajendiran, R. Sankararajan, R. Palaniappan. A Secure Key Predistribution Scheme for WSN Using Elliptic Curve Cryptography [J]. ETRI Journal,2011,33(5), pp. 791-801.
[95]S. Sugiura, Decentralized-precoding aided rateless codes for wireless sensor networks[J]. IEEE Communications Letters,2012,16(4), pp.506-509.
[96]M. Petrova, J. Riihijarvi, P. Mahonen. Performance study of IEEE 802.15.4 using measurements and simulations [C]. IEEE Wireless Communications and Networking Conference (WCNC), Las Vegas, NV, USA,2006,1, pp.487-492.
[97]S. Y. Shin, H. S. Park. Wook Hyun Kwon. Mutual interference analysis of IEEE 802.15. 4 and IEEE 802.11 b[J]. Computer Networks,2007,51(12), pp.3338-3353.
[98]W. Yuan, X. Y. Wang, J. P. M. G. Linnartz. A Coexistence Model of IEEE 802.15.4 and IEEE 802.11 b/g[C]. IEEE Symposium on Communications and Vehicular Technology in the Benelux (SCVT), Delft, The Netherlands,2007, pp.1-5.
[99]F. Yao, S. H. Yang, W. Zheng. Mitigating interference caused by IEEE 802.11 b in the IEEE 802.15.4 WSN within the environment of smart house[C]. IEEE International Conference on Systems Man and Cybernetics (SMC), Istanbul, Turkey,2010, pp. 2800-2807.
[100]C. R. Stevenson, G. Chouinard, Z. D. Lei. IEEE 802.22:The first cognitive radio wireless regional area network standard[J]. IEEE Communications Magazine,2009, 47(1), pp.130-138.
[101]J. Mitola. Cognitive Radio--An Integrated Agent Architecture for Software Defined Radio [J]. PhD Dissertation, Royal Inst. Technol. (KTH), Stockholm, Sweden 2000, pp. 1-313.
[102]T. K. Yucek, H. Arslan. A survey of spectrum sensing algorithms for cognitive radio applications[J]. IEEE Communications Surveys & Tutorials,2009,11(1), pp.116-130.
[103]邓延安,沈连丰,许波.一种应用于多跳网络的可调占空比固定时延MAC协议[J].东南大学学报(自然科学版),2008,38(6),pp.942-948.
[104]S. Gao, L. J. Qian, Dhadesugoor R. Vaman. Distributed energy efficient spectrum access in wireless cognitive radio sensor networks[C]. IEEE Wireless communications and networking Conference (WCNC), Las Vegas, Nevada, USA,2008, pp.1442-1447.
[105]K. Letaief, W. Zhang. Cooperative communications for cognitive radio networks[J]. Proceedings of the IEEE,2009,97(5), pp.878-893.
[106]J. N. Laneman, D. N. C. Tse, G. W. Wornell. Cooperative diversity in wireless networks: Efficient protocols and outage behavior[J]. IEEE Transactions on Information Theory, 2004,50(12), pp.3062-3080.
[107]W. F. Su, A. K. Sadek, K. J. R. Liu. Cooperative communication protocols in wireless networks:Performance analysis and optimum power allocation[J]. Wireless Personal Communications,2008,44(2), pp.181-217.
[108]A. K. Sadek, W. F Su, K.J. R. Liu. Multinode cooperative communications in wireless networks[J]. IEEE Transactions on Signal Processing,2007,55(1), pp.341-355.
[109]刘云璐,蒲菊华,方维维.一种无线传感器网络MAC协议优化算法[J].计算机学报,2012,35(3),pp.529-539.
[110]S. I. Johnsi, S. Radha. Investigations on distributed detection performance of Neyman-Pearson detection scheme using constructive interference technique for WSN[C]. International Conference on Wireless Communication and Sensor Computing (ICWCSC), Genoa, Italy,2010, pp.1-5.
[111]M. H. Tan, R. L.Yu, S. J. Li, X. D. Wang. Multipath routing protocol with load balancing in WSN considering interference[C]. IEEE Conference on Industrial Electronics and Applications (ICIEA), Singapore,2011, pp.1062-1067
[112]A.V. Kini, N. Singhal, S. Weber. Performance of a WSN in the Presence of Channel Variations and Interference[C]. IEEE Wireless Communications and Networking Conference (WCNC), Las Vegas, Nevada, USA,2008, pp.2899-2904.
[113]T. F. Wykret, L. H. A. Correia, D. F. Macedo, J. C. Giacomin, L.T. Andrade Evaluation and avoidance of interference in WSN:A multi-radio node prototype using Dynamic Spectrum Allocation[C]. IEEE Wireless Days (WD), Rio de Janeiro, Brazil, 2013,pp.1-3.
[114]J. Lee, W. C. Jeong. Performance analysis of IEEE 802.15.4e DSME MAC protocol under WLAN interference[C]. International Conference on ICT Convergence (ICTC), Hangzhou,China,2012, pp.741-746.
[115]N. Ahmed, S. Kanhere, J. Sanjay. Poster abstract:Multi-channel interference in wireless sensor networks [C]. International Conference on Information Processing in Sensor Networks (IPSN), San Francisco, California, USA,2009, pp.367-368.
[116]I. F. Akyildiz, W. Y. Lee, M. C. Vuran. Next generation/dynamic spectrum access/cognitive radio wireless networks:a survey [J]. Computer Networks,2006, 50(13), pp.2127-2159.
[117]蒋富龙,刘昊.一种无线传感器网络多信道动态路径转发协议[J].东南大学学报(自然科学版),2014,(1),pp.28-33.
[118]K. Oikonomou, S. Aissa. Dynamic sink assignment for efficient energy consumption in wireless sensor networks[C]. IEEE Wireless Communications and Networking Conference (WCNC), Paris, France,2012, pp.1876-1881.
[119]P. B. Mirchandani, R. L. Francis, J. Wiley & Sons. Discrete Location Theory[J]., Networks,1990,23(2), pp.124-125.
[120]O. B. Akan, O. Karli, O. Ergul. Cognitive Radio Sensor Network[J].IEEE Network, 2009,23(4), pp.34-40.
[121]Z. Li, F. R. Yu, M. Y. Huang. A Distributed Consensus-Based Cooperative Spectrum-Sensing Scheme in Cognitive Radios[J]. IEEE Transactions on Vehicular Technology,2010,59(1), pp.383-393.
[122]H. Z. Zhang, Z. Y Zhang, H. Y. Dai, R. Yin, X. M. Chen. Distributed Spectrum-Aware Clustering in Cognitive Radio Sensor Networks[C]. IEEE Global Telecommunications Conference (GLOBECOM), Houston, TX, USA,2011, pp.1-6.
[123]G. R. Ding, J. L. Wang, Q. H. Wu, F. Song, Y. Y. Chen. Spectrum Sensing in Opportunity-Heterogeneous Cognitive Sensor Networks:How to Cooperate? [J]. IEEE Sensors Journal,2013,13(11), pp.4247-4255.
[124]S. L. Xie, Y. Liu, Y. Zhang, R. Yu. A Parallel Cooperative Spectrum Sensing in Cognitive Radio Networks[J]. IEEE Transactions on Vehicular Technology,2010,59(8), pp.4079-4092.
[125]S. Sridharan, S. Vishwanath, S. A. Jafar and S. Shamai. On the capacity of cognitive relay assisted gaussian interference channel[C]. IEEE International Symposium on Information Theory (ISTT), Toronto,2008, pp.549-553.
[126]O. Sahin, E Erkip. Achievable rates for the Gaussian interference relay channel[C] IEEE Global Telecommunications Conference on Communication Theory Symposium (GLOBECOM), Washington D.C.,2007, pp.1627-1631.