无线传感器/执行器网络协作算法研究
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摘要
无线传感器网络(WSNs)是控制学科的前沿研究方向,其理论与技术发展极大的受到应用驱动。近年来,大量应用场景需要WSNs在具有事件监测能力的同时,还具有任务执行、事件控制功能。为了满足应用需求,典型的WSNs衍生出一种由大量传感器节点与少数执行器节点组成的新型网络结构,学术界称之为无线传感器/执行器网络(WSANs)。
无线传感器/执行器网络是一种新型信息获取和处理技术。在无线传感器/执行器网络中,传感器节点需要与执行器节点进行大量的紧密协作,共同完成对环境的监测和对事件的处理,因此出现了一种新的网络现象:传感器节点-执行器节点(SA)协作和执行器节点-执行器节点(AA)协作。如何通过节点间的协同合作,共同完成对环境事件的处理,同时保证网络的实时性和能耗均衡,是无线传感器/执行器网络研究面临的一大挑战。
本文面向无线传感器/执行器网络所涉及的理论与技术,针对传统的无线传感器网络相关协议和算法无法完全满足无线传感器/执行器网络新的需求,以节点之间的协同合作为研究手段,以提高网络实时性和能耗均衡为目的,构建SA协作和AA协作模型,基于最优化理论、群体智能优化、图论等计算方法展开理论与算法研究。主要研究成果包括如下四个方面:
①针对现有协作算法大多只对下一跳中继节点的选择进行研究,没有考虑网络能耗均衡问题,提出一种基于SA协作模型的分簇算法(CASA)。算法从全网的能耗和时延影响的角度,建立基于SA协作的能耗模型,综合考虑时延、连通度等约束条件,以网络能量优化为目标,构造非线性优化函数,求解网络理想执行器节点个数和传感器节点传输半径等网络分簇所需参数,并在此基础上完成节点的部署和成簇。网络平均时延和生存期等仿真结果表明,相比典型算法,该策略能够在满足一定连通度的前提下,优化网络部署,增强网络实时性和能量均衡性。
②针对AA协作中,事件频发区域内节点能耗过快问题,基于分簇结构,提出一种AA实时协作框架(RC)。通过将任务分解成若干任务单元,应用招投标理论、信息论,建立基于熵权的执行代价评价标准,根据网络中执行器节点的实际情况分派任务元,使得多个任务元可以并发执行,缩短执行时间;同时利用多个执行器节点间的协作,将执行能耗分摊到邻居执行器节点,延长网络生存期。仿真、分析结果表明,相比典型算法,RC框架同时兼顾了任务执行的实时性和网络能耗均衡,较好地解决了事件频发区域内节点能耗过快问题。
③针对多个有执行顺序限制条件的任务同时在多个执行器节点执行的任务分派问题,提出一种基于AA协作的单目标任务分派算法(SOTS)。建立以能量为约束条件,任务最大完成时间为目标函数的优化模型,并提出结合NEH方法与微粒群优化算法的混合优化算法进行目标函数求解。该算法将微粒群优化算法加以改造,用于任务分派的全局搜索;同时利用NEH方法增加算法局部搜索能力,确保解空间的充分搜索。仿真、分析结果表明,相比典型算法,SOTS搜索速度快,收敛性好,适合解决同时发生多个复杂事件的场合。
④针对多个有执行顺序限制条件的任务同时在多个执行器节点执行的任务分派问题,以最大完成时间、能耗均衡指标、存储成本为优化目标,提出一种基于AA协作的多目标任务分派算法(MOTS)。利用理想点法将多目标优化规范化为单目标优化,通过执行器节点角色确定降低问题复杂程度,提出结合基于自适应学习策略的多邻域搜索策略与微粒群优化算法的混合优化算法进行目标函数求解,确保解空间的充分搜索。仿真、分析结果表明,MOTS算法搜索速度快,收敛性好,各执行器节点的实时性、能耗等性能指标均优于比较算法,为解决同时发生多个复杂事件提供了新的思路。
The wireless sensor network (WSNs), whose theory and technology development are mostly driven by application, is an emerging research direction in the area of control. In recent years, there are a large number of applications that require the coordination between sensors and higher capability devices to support not only environmental monitoring but also the proper execution of specific tasks. As a result, wireless sensor and actor networks (WSANs) which is integrated by a large quantity of sensor nodes and a few number of actor nodes has been proposed as a new extension of WSNs.
WSANs is a new type of information acquisition and processing technology. In WSANs, coordination mechanisms are required among sensors and actors to gather information about the physical world and then perform appropriate actions upon the environment. In particular, new networking phenomena called sensor-actor (SA) and actor-actor (AA) coordination may occur. Currently research in WSANs face a serious challenge that is how to provide distributed information sensing and processing by means of coordination among nodes, the requirement of real-time and energy-balancing characteristic can be ensure at the same time.
Most of typical protocols and algorithms for WSNs may not be well-suited for the unique features and application requirements of WSANs. Facing these limitations, based on optimization theory, swarm intelligence optimization, graph theory and other calculating methods, the coordination mechanism of SA and AA is carried out in this thesis to meet the requirement of real-time and energy-balancing characteristic. The important research results are as follows:
①Most of the existing collaborative algorithms had been proposed for the next relay node without considering network energy balance. Considering that in WSANs the cluster size, the cluster head number and the residual energy of the node are key indicators of energy-efficient clustering algorithms, the article proposes the CASA, a clustering algorithm based on sensor-actor coordination model, in order to make the whole network energy consumption more balanced. The algorithm establishes the energy consumption model to obtain the optimized number of cluster heads which determine the cluster size. The sensor-actor communication energy consumption is modeled as a nonlinear program to obtain optimal transmission range of sensors and number of actors. Some methods are used to deploy actors and form cluster of heterogeneous sensor and actor network. Considering a few application requirements such as low-latency, connectivity and energy-efficient, performances of the proposed approaches are validated through simulations.
②Based on clustering networks strut, a real-time actor-actor coordination framework is proposed to solve“hot zone”problem. In this article, Tasks are partitioned into different task units and the cost of taking action is computed among different actors by using auction method. The actor-actor coordination is formulated as a balanced or non-balanced task assignment optimization problem to achieve more energy-balance. In addition, different task units are executed in parallel to enhance higher real-time response. The result of simulation shows that the algorithm could provide more balance in energy consumption and higher real-time performance.
③A single-objective task scheduling approach based on actor-actor coordination for WSANs is proposed to solve the execution problem of ordered execution tasks collaboratively among actors. The purpose of approach is minimizing the maximum response time in the actuators subject to residual energy constraints and schedule execution period of each task operation within given time. The algorithm is based on the principle of particle swarm optimization (PSO), an evolutionary computation technique, which is simple and has fewer adjustable parameters and possesses high search efficiency. Nawaz-Enscore-Ham (NEH) as a local search algorithm employs certain probability to avoid becoming trapped in a local optimum and has been proved to be effective for a variety of situations. Simulation results have shown that the proposed hybrid approach is of high convergence speed and good performance between task response time and balancing the energy dissipation among actors.
④In view of the actor-actor task coordination in WSANs, a multi-objective task scheduling approach is proposed. Considering the maximum response time, energy-balanced metric and storage cost, the task assignment among actuators is formulated as a multi-objective optimization problem. A modified ideal point algorithm is used to solve the dimension problem caused by different targets. By translating the multi-object optimization problem into a single-object one, the near-optimum execution period of each task operation would be scheduled in our approach. The algorithm is based on the principle of particle swarm optimization (PSO), an evolutionary computation technique, which is simple and have fewer adjustable parameters and possesses high search efficiency. Multi-neighboring experience as a local search algorithm employs certain probability to avoid becoming trapped in a local optimum and has been proved to be effective for a variety of situations. Simulation results have shown that the proposed algorithm is effective in terms of three performances.
无线传感器/执行器网络是一种新型信息获取和处理技术。在无线传感器/执行器网络中,传感器节点需要与执行器节点进行大量的紧密协作,共同完成对环境的监测和对事件的处理,因此出现了一种新的网络现象:传感器节点-执行器节点(SA)协作和执行器节点-执行器节点(AA)协作。如何通过节点间的协同合作,共同完成对环境事件的处理,同时保证网络的实时性和能耗均衡,是无线传感器/执行器网络研究面临的一大挑战。
本文面向无线传感器/执行器网络所涉及的理论与技术,针对传统的无线传感器网络相关协议和算法无法完全满足无线传感器/执行器网络新的需求,以节点之间的协同合作为研究手段,以提高网络实时性和能耗均衡为目的,构建SA协作和AA协作模型,基于最优化理论、群体智能优化、图论等计算方法展开理论与算法研究。主要研究成果包括如下四个方面:
①针对现有协作算法大多只对下一跳中继节点的选择进行研究,没有考虑网络能耗均衡问题,提出一种基于SA协作模型的分簇算法(CASA)。算法从全网的能耗和时延影响的角度,建立基于SA协作的能耗模型,综合考虑时延、连通度等约束条件,以网络能量优化为目标,构造非线性优化函数,求解网络理想执行器节点个数和传感器节点传输半径等网络分簇所需参数,并在此基础上完成节点的部署和成簇。网络平均时延和生存期等仿真结果表明,相比典型算法,该策略能够在满足一定连通度的前提下,优化网络部署,增强网络实时性和能量均衡性。
②针对AA协作中,事件频发区域内节点能耗过快问题,基于分簇结构,提出一种AA实时协作框架(RC)。通过将任务分解成若干任务单元,应用招投标理论、信息论,建立基于熵权的执行代价评价标准,根据网络中执行器节点的实际情况分派任务元,使得多个任务元可以并发执行,缩短执行时间;同时利用多个执行器节点间的协作,将执行能耗分摊到邻居执行器节点,延长网络生存期。仿真、分析结果表明,相比典型算法,RC框架同时兼顾了任务执行的实时性和网络能耗均衡,较好地解决了事件频发区域内节点能耗过快问题。
③针对多个有执行顺序限制条件的任务同时在多个执行器节点执行的任务分派问题,提出一种基于AA协作的单目标任务分派算法(SOTS)。建立以能量为约束条件,任务最大完成时间为目标函数的优化模型,并提出结合NEH方法与微粒群优化算法的混合优化算法进行目标函数求解。该算法将微粒群优化算法加以改造,用于任务分派的全局搜索;同时利用NEH方法增加算法局部搜索能力,确保解空间的充分搜索。仿真、分析结果表明,相比典型算法,SOTS搜索速度快,收敛性好,适合解决同时发生多个复杂事件的场合。
④针对多个有执行顺序限制条件的任务同时在多个执行器节点执行的任务分派问题,以最大完成时间、能耗均衡指标、存储成本为优化目标,提出一种基于AA协作的多目标任务分派算法(MOTS)。利用理想点法将多目标优化规范化为单目标优化,通过执行器节点角色确定降低问题复杂程度,提出结合基于自适应学习策略的多邻域搜索策略与微粒群优化算法的混合优化算法进行目标函数求解,确保解空间的充分搜索。仿真、分析结果表明,MOTS算法搜索速度快,收敛性好,各执行器节点的实时性、能耗等性能指标均优于比较算法,为解决同时发生多个复杂事件提供了新的思路。
The wireless sensor network (WSNs), whose theory and technology development are mostly driven by application, is an emerging research direction in the area of control. In recent years, there are a large number of applications that require the coordination between sensors and higher capability devices to support not only environmental monitoring but also the proper execution of specific tasks. As a result, wireless sensor and actor networks (WSANs) which is integrated by a large quantity of sensor nodes and a few number of actor nodes has been proposed as a new extension of WSNs.
WSANs is a new type of information acquisition and processing technology. In WSANs, coordination mechanisms are required among sensors and actors to gather information about the physical world and then perform appropriate actions upon the environment. In particular, new networking phenomena called sensor-actor (SA) and actor-actor (AA) coordination may occur. Currently research in WSANs face a serious challenge that is how to provide distributed information sensing and processing by means of coordination among nodes, the requirement of real-time and energy-balancing characteristic can be ensure at the same time.
Most of typical protocols and algorithms for WSNs may not be well-suited for the unique features and application requirements of WSANs. Facing these limitations, based on optimization theory, swarm intelligence optimization, graph theory and other calculating methods, the coordination mechanism of SA and AA is carried out in this thesis to meet the requirement of real-time and energy-balancing characteristic. The important research results are as follows:
①Most of the existing collaborative algorithms had been proposed for the next relay node without considering network energy balance. Considering that in WSANs the cluster size, the cluster head number and the residual energy of the node are key indicators of energy-efficient clustering algorithms, the article proposes the CASA, a clustering algorithm based on sensor-actor coordination model, in order to make the whole network energy consumption more balanced. The algorithm establishes the energy consumption model to obtain the optimized number of cluster heads which determine the cluster size. The sensor-actor communication energy consumption is modeled as a nonlinear program to obtain optimal transmission range of sensors and number of actors. Some methods are used to deploy actors and form cluster of heterogeneous sensor and actor network. Considering a few application requirements such as low-latency, connectivity and energy-efficient, performances of the proposed approaches are validated through simulations.
②Based on clustering networks strut, a real-time actor-actor coordination framework is proposed to solve“hot zone”problem. In this article, Tasks are partitioned into different task units and the cost of taking action is computed among different actors by using auction method. The actor-actor coordination is formulated as a balanced or non-balanced task assignment optimization problem to achieve more energy-balance. In addition, different task units are executed in parallel to enhance higher real-time response. The result of simulation shows that the algorithm could provide more balance in energy consumption and higher real-time performance.
③A single-objective task scheduling approach based on actor-actor coordination for WSANs is proposed to solve the execution problem of ordered execution tasks collaboratively among actors. The purpose of approach is minimizing the maximum response time in the actuators subject to residual energy constraints and schedule execution period of each task operation within given time. The algorithm is based on the principle of particle swarm optimization (PSO), an evolutionary computation technique, which is simple and has fewer adjustable parameters and possesses high search efficiency. Nawaz-Enscore-Ham (NEH) as a local search algorithm employs certain probability to avoid becoming trapped in a local optimum and has been proved to be effective for a variety of situations. Simulation results have shown that the proposed hybrid approach is of high convergence speed and good performance between task response time and balancing the energy dissipation among actors.
④In view of the actor-actor task coordination in WSANs, a multi-objective task scheduling approach is proposed. Considering the maximum response time, energy-balanced metric and storage cost, the task assignment among actuators is formulated as a multi-objective optimization problem. A modified ideal point algorithm is used to solve the dimension problem caused by different targets. By translating the multi-object optimization problem into a single-object one, the near-optimum execution period of each task operation would be scheduled in our approach. The algorithm is based on the principle of particle swarm optimization (PSO), an evolutionary computation technique, which is simple and have fewer adjustable parameters and possesses high search efficiency. Multi-neighboring experience as a local search algorithm employs certain probability to avoid becoming trapped in a local optimum and has been proved to be effective for a variety of situations. Simulation results have shown that the proposed algorithm is effective in terms of three performances.
引文
[1] Corson S. Macker J. MobileAd hoc Networking (MANET): Routing Protocol Performance[S]. RFC 2501. 1999.
[2] Romer Kay Mattern F. The Design Space of Wireless Sensor Networks[J]. IEEE Wireless Communications. 2004,11 (6): 54–61.
[3] Haenselmann. Thomas. Sensor networks. GFDL Wireless Sensor Network textbook. [EB/OL] http://www.informatik.uni-mannheim.de/～haensel/sn_book.
[4] Hadim Salem Nader Mohamed. Middleware challenges and approaches for wireless sensor networks[J]. IEEE Distributed Systems Online. 2006,7 (3): 603-3001.
[5] Vassis D. Kormentzas G. Skianis C. Performance evaluation of single and multi-channel actuator to actuator communication for wireless sensor actuator networks[J]. Ad Hoc Networks. 2006,4(4):487-498.
[6] Petriu E. M. Georganas N. D. Petriu. Sensor-based information appliances[J]. IEEE Instrumentation and Measurement Magazine. 2000,3(4):31-35.
[7] Akyildiz Ian F. SuW. Sankarasubramaniam Y. Cayirci. A Survey on Sensor Networks.[J] IEEE Communications Magazine. 2002,40(8):102-114.
[8] Akyldiz F. Kasimoglu I. Wireless sensor and actuator networks: Research challenges[J]. Ad Hoc Networks Journal Elsevier. 2004,2(4):351-367.
[9] Melodia T. Pompili D. Gungor V. A distributed coordination framework for wireless sensor and actuator networks[C]. In Proceedings of the 6th ACM international symposium on mobile ad hoc networking and computing. 2005.341-352.
[10] Keini Ozaki Kenichi Watanabe Satoshi Itaya. A Fault-Tolerant Model of Wireless Sensor-actuator Systems[C]. In Proceedings of the 20th International Conference on Advanced Information Networking and Applications (AINA’06). 2006:303-307.
[11] Arjan Durresi Vamsi Paruchuri. Geometric Broadcast Protocol for Heterogenous Sensor Networks[C]. In Proceedings of the 19th International Conference on Advanced Information Networking and Applications, AINA. 2005:1-343.
[12] S. Thrun M. Diel D. Ha. Scan alignment and 3-D surface modeling with a helicopter platform[C]. In: Proc. of the Int. Conf. on Field and Service Robotic. 2003.4:287-297
[13] H. R. Everett D. W. Gage. A Third Generation Security Robot.[J] SPIE Mobile Robot and Automated Vehicle Control Systems. Boston, MA 1996. 29(03):118-126.
[14] Robotics DARPA Tactical Mobile. [EB/OL]http://www.darpa.mil/ato/programs/tmr.htm2004.
[15] Sub-kilogram intelligent tele-robots (SKITs). [EB/OL]http://www.robotics.usc.edu/~behar/SKIT.html 2004.
[16] Ian F. Akyildiz Ismail H. Kasimoglu. Wireless sensor and actor networks: research challenges[J]. Ad Hoc Networks. 2004,2(4):351-367.
[17] Lesser V. Reflections on the nature of multi-agent coordination and its implications for an agent architecture[J]. Autonomous Agents and Multi-Agent Systems. 1998,1(1 ): 89-111.
[18] I. F. Akyildiz W. Su Y. Sankarasubramaniam. Wireless sensor networks: A survey[J]. Computer Networks. 2002, 38 (4):393-422.
[19] I. Chlamtac M. Conti J. N. Mobile ad-hoc networking:imperatives and challenges[J]. Ad Hoc Networks. 2003,1(1 ):13-64.
[20] A. J. Goldsmith S. Wicker. Design challenges for energycontrained ad-hoc wireless networks[J]. IEEE Wireless Communications. 2002,9 (4): 8-27.
[21] H. S. Kim T. F. Abdelzaher W. Minimumenergy asynchronous dissemination to mobile sinks in wireless sensor networks[C]. In Proc. of the First ACM Int.Conf. on Embedded Networked Sensor Systems. 2003:193-204.
[22] M. Conti S. Giordano G. Maselli. Cross-layering in mobile ad-hoc network design[J]. IEEE Computer, Special Issue on AdHoc Networks. 2004, 37 (2): 48-51.
[23] B. Tavli W. Heinzelman. TRACE: Time reservation using adaptive control for energy efficiency[J]. IEEE Journal on Selected Areas of Communication. 2003,21 (10):1506-1515.
[24] M. Caccamo L. Y. Zhang L. An implicit prioritized access protocol for wireless sensor networks[C]. In: Proc. IEEE Real-Time Systems Symp. 2002:39-48.
[25] J. A. Stankovic T. F. Abdelzaher C. Real-time communication and coordination in embedded sensor networks[C]. Proceedings of the IEEE. 2003:1002-1022.
[26] Cardell-Oliver R. Smettem K. Kranz. Field testing a wireless sensor network for reactive environmental monitoring[C]. In Proceedings of the Intelligent Sensors, Sensor Networks and Information Processing Conference. 2004:14–17. 7-12.
[27] Szewczyk R. Osterweil E. Polastre. Habitat monitoring with sensor networks[J]. Communications of the ACM. 2004. 47 (6): 34-40.
[28] Santoni T. A. Santucci J. Using wireless sensor network for wildfi re detection.A discrete event approach of environmental monitoring tool[C]. Proceeding of ISEIMA 06. 2006:15-120.
[29] Szewczyk R Osterweil E. Polastre J. Habitat monitoring with sensor networks[J]. Wireless sensor networks .2004,47(6):34-40.
[30] R. Polastre J. Design and implementation of wireless sensor networks for habitat monitoring[D]. 2003.
[31] Mainwaring A Polastre J. Szewczyk R. Wireless Sensor Networks for Habitat Monitoring[C]. Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications. 2002:88-97.
[32] Cerpa A Elson J. Estrin D. Habitat monitoring: Application driver for wireless communications technology[J]. ACM SIGCOMM Computer Communication Review. 2001. 31:20-41.
[33] Fidelity and yield in a volcano monitoring sensor network.[EB/OL]. https://www.usenix.org/events/osdi06/tech/full_papers/werner-allen/werner-allen.pdf
[34] Hart J K Martinez K. Environmental Sensor Networks: A revolution in the earth system science[J]. Earth Science Reviews. 2006. 78(3-4): 177-191.
[35] Hu W Bulusu N. Chou C. Design and evaluation of a hybrid sensor network for cane toad monitoring[J]. ACM Transactions on Sensor Networks (TOSN) .2009,5(1):132-136.
[36] Milenkovi C A Otto C. Jovanov E. Wireless sensor networks for personal health monitoring: Issues and an implementation[J]. Computer Communications. 2006. 29(13-14):2521-2533.
[37] Bhargava A Zoltowski M. Sensors and wireless communication for medical care[C]. Proceedings of the 14th International Workshop on Database and Expert Systems Applications. 2003:956-962.
[38] Kim S Pakzad S. Culler D. Health monitoring of civil infrastructures using wireless sensor networks[C]. Proceedings of the 6th international conference on Information processing in sensor networks. 2007:254-263.
[39] Stankovic J A Cao Q. Doan T. Wireless sensor networks for in-home healthcare: potential and challenges[C]. High Confidence Medical Device Software and Systems Workshop. 2005:1312-1320.
[40] Otto C Milenkovic A. Sanders C. System architecture of a wireless body area sensor network for ubiquitous health monitoring[J]. Journal of Mobile Multimedia. 2006, 1(4);307-326.
[41] Shnayder V Chen B. Lorincz K. Sensor networks for medical care[C]. Proceedings of the 3rd international conference on Embedded networked sensor systems. 2005:314.
[42]刘建.无线传感器网络在军事的应用. [EB/OL]http://www.21eic.com/autocontrol/pdf/sensor091024m.pdf
[43] SMART DUST Autonomous sensing and communication in a cubic millimeter [EB/OL]http:// robotics.eecs.berkeley.edu/~pister/SmartDust/
[44] Cyrano利用IBM的无线基础设施嗅出化学品的气味. [EB/OL]. https://www-900.ibm.com/cn/smb/solutions/wireless/othliv_cyrano_c.shtml
[45] Sensor Webs. [EB/OL] http://sensorwebs.jpl.nasa.gov/.
[46] Tabar A M Keshavarz A. Aghajan H. Smart home care network using sensor fusion and distributed vision-based reasoning[C]. Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks. 2006:145-154.
[47] Chung W Y Oh S. J. Remote monitoring system with wireless sensors module for room environment[J]. Sensors & Actuators: B. Chemical. 2006. 113(1): 64-70.
[48] Ivanov B Zhelondz O. Borodulkin L. Distributed smart sensor system for indoor climate monitoring[C]. KONNEX Scientific Conference. 2002:10-11.
[49] Ferrari G Medagliani P. di Piazza. Wireless sensor networks: Performance analysis in indoor scenarios[J]. EURASIP Journal on Wireless Communications and Networking. 2007. 2007(1): 41.
[50] Pan M S Tsai C. H. Tseng. Emergency guiding and monitoring applications in indoor 3D environments by wireless sensor networks[J]. International Journal of Sensor Networks. 2006. 1(1): 2-10.
[51] S. Mahlknecht. Energy-self-sufficient wireless sensor networks for the home and building environment[M]. Institute of Computer Technology, Technical University of Vienna. Vienna, Austria. Dissertation thesis. 2004.
[52] Epstein. A. H. Milimeter-Scale,MEMS Gas Turbine Engines[C]. In Proceeedings of the ASME Turbo Expo 2003-Power for Land,Sea,and Air. 2003.
[53]任丰原,黄海宁,林闯.无线传感器网络[J].软件学报Journal of Software. 2003. 14(07):1282-1291.
[54] Shih E Cho S. Ickes N. Physical layer driven protocol and algorithm design for energy-efficient wireless sensor networks[C]. In: Proceedings of the ACM MobiCom 2001. Rome: ACM Press. 2001:272-286.
[55] Akyildiz I. F Su W. Sankarasubramaniam Y. Wireless sensor network: A survey[J]. Computer Networks. 2002,38(4):393-422.
[56] Werner-Allen G Swieskowski P. Welsh M. Motelab: A wireless sensor network testbed[C]. Information Processing in Sensor Networks IPSN 2005. Fourth International Symposium on. 2005:483-488.
[57] Ertin E Arora A. Ramnath R. A testbed for sensing at scale[C]. Information Processing in Sensor Networks, 2006. IPSN 2006. The Fifth International Conference on. 2006:399-406.
[58] Simulator-NS-2, The Network. [EB/OL]http://www.isi.edu/nsnam/ns/
[59]陈敏. OPNET网络仿真[M]. 2004.
[60] Park S Savvides A. Srivastava M. SensorSim: a simulation framework for sensor networks[C]. Proceedings of the 3rd ACM international workshop on Modeling, analysis and simulation ofwireless and mobile systems. 2000:104-111.
[61] Girod L Ramanathan N. Elson J. A software environment for developing and deploying heterogeneous sensor-actuator networks[J]. ACM Transactions on Sensor Networks. 2007,3(3):1-34.
[62] OMNET++Community Site. [EB/OL]In http://www.omnetpp.org/external/whatis.php.
[63] Zeng X Bagrodia R. Gerla M. a library for parallel simulation of large-scale wireless networks[J]. ACM SIGSIM Simulation Digest. 1998,28(1):154-161.
[64] Levis P Lee N. Welsh M. TOSSIM: Accurate and scalable simulation of entire TinyOS applications[C]. Proceedings of the 1st international conference on Embedded networked sensor systems. 2003:126-137.
[65] Haenggi. Martin. Mobile Sensor-Actuator Networks:Opportunities and Challenges[C]. Proc.of 7th IEEE Intl.Workshop. 2002:283-291.
[66] Shah G. A. Bozyigit M. Akan O. Real Time Coordination and Routing in Wireless Sensor and Actuator Networks[C]. In Proceedings of the 6th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networking NEW2AN. 2006:365-383.
[67] Ngai C. H. Edith Lyu R. Michael Liu. A Real Time Communication Framework forWireless Sensor-Actor Networks[C]. IEEE Aerospace Conference. 2006.2021-2029.
[68] Haidong Yuan Huadong Ma Hongyu Liao. Coordination Mechanism in Wireless Sensor and Actor Networks[C]. Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences (IMSCCS'06). 2006, 2:627-634.
[69]胡四泉李方敏.徐文君.刘新华.无线传感器/执行器网络中能量有效的实时分簇路由协议[J].计算机研究与发展. 2008,45(1):26-33.
[70]张丽.无线传感执行网络中协作机制及算法的研究[D].硕士学位论文, 2007.
[71] Ortiz C. L Eric H. Structured Negotiation[C]. In ICMAS'02. 2002:1215 - 1222.
[72] Ortiz C. L Hsu E. Jardins M. Incremental Negotiation and Coalition Formation for Resource-bounded Agents[C]. In Proceedings of the AAAI Fall Symposium. 2001:116-122.
[73] Yadgar O Kraus S. Ortiz C. Hierarchical organizations for Real-time Large-scale Task and Team Environments[C]. In Proceedings of AAMAS'02. 2002: 1147 - 1148.
[74] Sandholm T Suri S. Improved Algorithms for Optimal Winner Determination in Combinatorial Auctions and Generalizations[C]. In National Conference on Artificial Intelligence(AAAI). 2002: 90-97.
[75] Soh L. K Tsatsoulis C. Satisficing Coalition Formation among Agents[C]. In Proceedings of the International Conference on Autonomous Agents and Multiagent System. 2001: 1062 - 1063.
[76] Soh L. k Tsatsoulis C. Real-time Satisficing Multiagent Coalition Formaiton[C]. In Working Notes of AAAI Workshop on Coalition Formaiton in Dynamic Multiagent Environments. 2002:7-15.
[77] Lesser V Horling B. Klassner F. BIG:An Agent for Resource-Bounded Information Gathering and Decision Making[J]. Artificial Intelligent Journal. 2000. 118:197-244.
[78] Horling B Lesser V. Vincent R. The Soft Real-time Agent Control Architecture[J]. Computer Science Technical Report. 2002:2-14.
[79] Tommaso Melodia Dario Pompili Vehbi C. A Distributed Coordination Framework for Wireless Sensor and Actor Networks. International Symposium on Mobile Ad Hoc Networking & Computing[C]. Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing. 2005:99-110.
[80] Melodia T. Pompili D. Gungor V. Communication and Coordination in Wireless Sensor and Actor Networks[J]. IEEE Transactions on Mobile Computing. 2007.6(10): 1116-1129.
[81] Melodia T. Pompili D. Akyldiz I. A Communication Architecture for Mobile Wireless Sensor and Actor Networks[C]. In Proceeding of IEEE International Conference on Sensor Mesh and Ad hoc Communications and Networks SECON. 2006:142-151.
[82] Dimitris Vassis George Kormentzas Charalabos Skianis. Performance evaluation of single and multichannel actor to actor communication for wireless sensor actor networks[J]. Elsevier B.V. 2005,4(4): 487-498.
[83] Keiji Ozaki Naohiro Hayashibara and Makoto. Coordination Protocols of Multiple Actuator Nodes in a Multi-Actuator/Multi-Sensor Model[C]. 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07). 2007: 62-67.
[84] Keiji Ozakia Tomoya Enokidob Makoto Takizawaa. Coordination protocols for a reliable sensor,actuator, and device network (SADN)[C]. In Proceeding of International Conference on Complex, Intelligent and Software Intensive Systems. 2008: 193-199.
[85] M Lopez-Nores J. Garcia-Duque JJ Pazos. Qualitative assessment of approaches to coordinate activities of mobile hosts in ad hoc networks [J]. IEEE Communications Magazine. 2008. 12. 108-111.
[86] Ameer Ahmed Abbasi Mohamed Younis. Movement-Assisted Connectivity Restoration in Wireless Sensor and Actor Networks [J]. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS. 2009. 20(9): 1366-1379.
[87] Li Fangmin. Real-time energy-aware cluster-based routing protocol for wireless sensor and actor networks[J]. Jisuanji Yanjiu yu Fazhan/Computer Research and Development. 2008,45(1):26-33.
[88] N. Trivedi G. Elangovan S. S. 'Ripples': A message-efficient, distributed clustering algorithm for wireless sensor and actor networks[C]. IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. 2006:53-58.
[89] kkaya Kemal A. Clustering of wireless sensor and actor networks based on sensor distribution and connectivity[J]. Journal of Parallel and Distributed Computing. 2009, 69(6):573-587.
[90] K. Akkaya M. Younis Coverage and delay aware actor placement mechanisms for wireless sensor and actor networks[J]. International Journal of sensor networks. 2008. 3 (3):11-25.
[91] Rappaport. T. Wireless Communication Principles and Practice(2nd Edition)(M). Upper Saddle River, NJ. : London: Prentice Hall PTR. 2002.
[92] V Mhatre C. Rosenberg. Design guidelines for wireless sensor networks: communication, clustering and aggregation[J]. Ad Hoc Networks, Elsevier. 2004,2(1): 45-63.
[93] Zou Y Chakrabarty K. Sensor deployment and target localization based on virtual forces[A]. IEEE INFOCOM[C]. Piscataway,NJ,USA;IEEE Press. 2003. 1293-1303.
[94] Wang Xue. An Improved Co-evolutionary Particle Swarm Optimization for Wireless Sensor Networks with Dynamic Deployment[J]. Sensors. 2007,7(3): 354-370.
[95] K Akkaya F. Senel. Detecting and connecting disjoint sub-networks in wireless sensor and actor networks [J]. Ad Hoc Networks, Elsevier. 2009, 46(7):1330-1346.
[96]崔逊学方红雨朱徐来.传感器网络定位问题的概率特征[J].计算机研究与发展, 2007. (4):630-635.
[97] Nar PC Cayirci E. Pcsmac A power controlled sensor-MAC protocol for wireless sensor networks[C]. In Cayirci E ed Proc Of the IEEE EWSN Piscataway IEEE Computer Society.2005.
[98] Ferenets R. Tarmo Lipping. Comparison of entropy and complexity measures for the assessment of depth of sedation[J]. IEEE Transactions on Biomedical Engineering. 2006. 53(6):1067-1077.
[99] BURKARD RAINER, M. DELL'AMICO S. MARTELLO. Assignment Problems[M]. SIAM. 2009.
[100] Howard Hassig Randy Clark et al. SCADA system makes CSO incidents a thing of the past. [EB/OL]http://ww.pennnet.com/display_article/308489/41/ARTCL/none/none/1/SCADA-System-Makes-CSO-Incidents-a-Thing-of-the-Past/.
[101] J. Kennedy R. C. Eberhart. Particle swarm optimization[C]. Proceedings of the IEEE Int. Conf. Neural Networks IV. 1995:1942-1948.
[102] C. Bean J. Genetic algorithm and random keys for sequencing and optimization[J]. ORSA Journal of Computing. 1994. 6(2):154-160.
[103] Wang L Zheng D. Z. An effective hybrid heuristic for flow shop scheduling problems[J]. Int.J.Adv.Manuf.Technol. 2003. 21: 38-44.
[104] R Reeves C. A genetic algorithm for flowshop sequencing[J]. Computers & Operations Research Oper.Res. 1995,22(1):5-13.
[105] Deb, K. Pratap. A Agarwal, S. and Meyarivan T. A fast and elitist multi-objective genetic algorithm: NSGA-II[J]. IEEE Transaction on Evolutionary Computation, 2002.6(2):181-197.
[106] Ishibuchi H Murata T. A. multi-objective genetic local search algorithm and its application to flowshop scheduling[J]. IEEE Trans. Syst. Man.Cy.B. 1998, 28 (3):392-402.
[107] Ong Y S Keane A. J. Meta-Lamarckian learning in memetic algorithms[J]. IEEE Trans. Evolut. Comput. 2004,8(2):99-110.
[108] Xia Weijun, Wu Zhiming. An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems[J]. Computers and Industrial Engineering 2005. 48(2): 409-425.
[109] Kacem. Imed Genetic algorithm for the flexible job-shop scheduling problem [C]. Proceedings of the IEEE International Conference on Systems. Man and Cybernetics. 2003.4:3464-3469.
[110] AR Rahimi-Vahed SM Mirghorbani J. Comb. A multi-objective particle swarm for a flow shop scheduling problem[J]. Journal of combinatorial optimization. 2007.13(1):79-102.
[111] Tan K C Goh C. K. Yang. Evolving better population distribution and exploration in evolutionsary multi-abjective optimization[J]. Eur. J. Oper. Res. 2006.171(2):463-495.
[2] Romer Kay Mattern F. The Design Space of Wireless Sensor Networks[J]. IEEE Wireless Communications. 2004,11 (6): 54–61.
[3] Haenselmann. Thomas. Sensor networks. GFDL Wireless Sensor Network textbook. [EB/OL] http://www.informatik.uni-mannheim.de/～haensel/sn_book.
[4] Hadim Salem Nader Mohamed. Middleware challenges and approaches for wireless sensor networks[J]. IEEE Distributed Systems Online. 2006,7 (3): 603-3001.
[5] Vassis D. Kormentzas G. Skianis C. Performance evaluation of single and multi-channel actuator to actuator communication for wireless sensor actuator networks[J]. Ad Hoc Networks. 2006,4(4):487-498.
[6] Petriu E. M. Georganas N. D. Petriu. Sensor-based information appliances[J]. IEEE Instrumentation and Measurement Magazine. 2000,3(4):31-35.
[7] Akyildiz Ian F. SuW. Sankarasubramaniam Y. Cayirci. A Survey on Sensor Networks.[J] IEEE Communications Magazine. 2002,40(8):102-114.
[8] Akyldiz F. Kasimoglu I. Wireless sensor and actuator networks: Research challenges[J]. Ad Hoc Networks Journal Elsevier. 2004,2(4):351-367.
[9] Melodia T. Pompili D. Gungor V. A distributed coordination framework for wireless sensor and actuator networks[C]. In Proceedings of the 6th ACM international symposium on mobile ad hoc networking and computing. 2005.341-352.
[10] Keini Ozaki Kenichi Watanabe Satoshi Itaya. A Fault-Tolerant Model of Wireless Sensor-actuator Systems[C]. In Proceedings of the 20th International Conference on Advanced Information Networking and Applications (AINA’06). 2006:303-307.
[11] Arjan Durresi Vamsi Paruchuri. Geometric Broadcast Protocol for Heterogenous Sensor Networks[C]. In Proceedings of the 19th International Conference on Advanced Information Networking and Applications, AINA. 2005:1-343.
[12] S. Thrun M. Diel D. Ha. Scan alignment and 3-D surface modeling with a helicopter platform[C]. In: Proc. of the Int. Conf. on Field and Service Robotic. 2003.4:287-297
[13] H. R. Everett D. W. Gage. A Third Generation Security Robot.[J] SPIE Mobile Robot and Automated Vehicle Control Systems. Boston, MA 1996. 29(03):118-126.
[14] Robotics DARPA Tactical Mobile. [EB/OL]http://www.darpa.mil/ato/programs/tmr.htm2004.
[15] Sub-kilogram intelligent tele-robots (SKITs). [EB/OL]http://www.robotics.usc.edu/~behar/SKIT.html 2004.
[16] Ian F. Akyildiz Ismail H. Kasimoglu. Wireless sensor and actor networks: research challenges[J]. Ad Hoc Networks. 2004,2(4):351-367.
[17] Lesser V. Reflections on the nature of multi-agent coordination and its implications for an agent architecture[J]. Autonomous Agents and Multi-Agent Systems. 1998,1(1 ): 89-111.
[18] I. F. Akyildiz W. Su Y. Sankarasubramaniam. Wireless sensor networks: A survey[J]. Computer Networks. 2002, 38 (4):393-422.
[19] I. Chlamtac M. Conti J. N. Mobile ad-hoc networking:imperatives and challenges[J]. Ad Hoc Networks. 2003,1(1 ):13-64.
[20] A. J. Goldsmith S. Wicker. Design challenges for energycontrained ad-hoc wireless networks[J]. IEEE Wireless Communications. 2002,9 (4): 8-27.
[21] H. S. Kim T. F. Abdelzaher W. Minimumenergy asynchronous dissemination to mobile sinks in wireless sensor networks[C]. In Proc. of the First ACM Int.Conf. on Embedded Networked Sensor Systems. 2003:193-204.
[22] M. Conti S. Giordano G. Maselli. Cross-layering in mobile ad-hoc network design[J]. IEEE Computer, Special Issue on AdHoc Networks. 2004, 37 (2): 48-51.
[23] B. Tavli W. Heinzelman. TRACE: Time reservation using adaptive control for energy efficiency[J]. IEEE Journal on Selected Areas of Communication. 2003,21 (10):1506-1515.
[24] M. Caccamo L. Y. Zhang L. An implicit prioritized access protocol for wireless sensor networks[C]. In: Proc. IEEE Real-Time Systems Symp. 2002:39-48.
[25] J. A. Stankovic T. F. Abdelzaher C. Real-time communication and coordination in embedded sensor networks[C]. Proceedings of the IEEE. 2003:1002-1022.
[26] Cardell-Oliver R. Smettem K. Kranz. Field testing a wireless sensor network for reactive environmental monitoring[C]. In Proceedings of the Intelligent Sensors, Sensor Networks and Information Processing Conference. 2004:14–17. 7-12.
[27] Szewczyk R. Osterweil E. Polastre. Habitat monitoring with sensor networks[J]. Communications of the ACM. 2004. 47 (6): 34-40.
[28] Santoni T. A. Santucci J. Using wireless sensor network for wildfi re detection.A discrete event approach of environmental monitoring tool[C]. Proceeding of ISEIMA 06. 2006:15-120.
[29] Szewczyk R Osterweil E. Polastre J. Habitat monitoring with sensor networks[J]. Wireless sensor networks .2004,47(6):34-40.
[30] R. Polastre J. Design and implementation of wireless sensor networks for habitat monitoring[D]. 2003.
[31] Mainwaring A Polastre J. Szewczyk R. Wireless Sensor Networks for Habitat Monitoring[C]. Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications. 2002:88-97.
[32] Cerpa A Elson J. Estrin D. Habitat monitoring: Application driver for wireless communications technology[J]. ACM SIGCOMM Computer Communication Review. 2001. 31:20-41.
[33] Fidelity and yield in a volcano monitoring sensor network.[EB/OL]. https://www.usenix.org/events/osdi06/tech/full_papers/werner-allen/werner-allen.pdf
[34] Hart J K Martinez K. Environmental Sensor Networks: A revolution in the earth system science[J]. Earth Science Reviews. 2006. 78(3-4): 177-191.
[35] Hu W Bulusu N. Chou C. Design and evaluation of a hybrid sensor network for cane toad monitoring[J]. ACM Transactions on Sensor Networks (TOSN) .2009,5(1):132-136.
[36] Milenkovi C A Otto C. Jovanov E. Wireless sensor networks for personal health monitoring: Issues and an implementation[J]. Computer Communications. 2006. 29(13-14):2521-2533.
[37] Bhargava A Zoltowski M. Sensors and wireless communication for medical care[C]. Proceedings of the 14th International Workshop on Database and Expert Systems Applications. 2003:956-962.
[38] Kim S Pakzad S. Culler D. Health monitoring of civil infrastructures using wireless sensor networks[C]. Proceedings of the 6th international conference on Information processing in sensor networks. 2007:254-263.
[39] Stankovic J A Cao Q. Doan T. Wireless sensor networks for in-home healthcare: potential and challenges[C]. High Confidence Medical Device Software and Systems Workshop. 2005:1312-1320.
[40] Otto C Milenkovic A. Sanders C. System architecture of a wireless body area sensor network for ubiquitous health monitoring[J]. Journal of Mobile Multimedia. 2006, 1(4);307-326.
[41] Shnayder V Chen B. Lorincz K. Sensor networks for medical care[C]. Proceedings of the 3rd international conference on Embedded networked sensor systems. 2005:314.
[42]刘建.无线传感器网络在军事的应用. [EB/OL]http://www.21eic.com/autocontrol/pdf/sensor091024m.pdf
[43] SMART DUST Autonomous sensing and communication in a cubic millimeter [EB/OL]http:// robotics.eecs.berkeley.edu/~pister/SmartDust/
[44] Cyrano利用IBM的无线基础设施嗅出化学品的气味. [EB/OL]. https://www-900.ibm.com/cn/smb/solutions/wireless/othliv_cyrano_c.shtml
[45] Sensor Webs. [EB/OL] http://sensorwebs.jpl.nasa.gov/.
[46] Tabar A M Keshavarz A. Aghajan H. Smart home care network using sensor fusion and distributed vision-based reasoning[C]. Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks. 2006:145-154.
[47] Chung W Y Oh S. J. Remote monitoring system with wireless sensors module for room environment[J]. Sensors & Actuators: B. Chemical. 2006. 113(1): 64-70.
[48] Ivanov B Zhelondz O. Borodulkin L. Distributed smart sensor system for indoor climate monitoring[C]. KONNEX Scientific Conference. 2002:10-11.
[49] Ferrari G Medagliani P. di Piazza. Wireless sensor networks: Performance analysis in indoor scenarios[J]. EURASIP Journal on Wireless Communications and Networking. 2007. 2007(1): 41.
[50] Pan M S Tsai C. H. Tseng. Emergency guiding and monitoring applications in indoor 3D environments by wireless sensor networks[J]. International Journal of Sensor Networks. 2006. 1(1): 2-10.
[51] S. Mahlknecht. Energy-self-sufficient wireless sensor networks for the home and building environment[M]. Institute of Computer Technology, Technical University of Vienna. Vienna, Austria. Dissertation thesis. 2004.
[52] Epstein. A. H. Milimeter-Scale,MEMS Gas Turbine Engines[C]. In Proceeedings of the ASME Turbo Expo 2003-Power for Land,Sea,and Air. 2003.
[53]任丰原,黄海宁,林闯.无线传感器网络[J].软件学报Journal of Software. 2003. 14(07):1282-1291.
[54] Shih E Cho S. Ickes N. Physical layer driven protocol and algorithm design for energy-efficient wireless sensor networks[C]. In: Proceedings of the ACM MobiCom 2001. Rome: ACM Press. 2001:272-286.
[55] Akyildiz I. F Su W. Sankarasubramaniam Y. Wireless sensor network: A survey[J]. Computer Networks. 2002,38(4):393-422.
[56] Werner-Allen G Swieskowski P. Welsh M. Motelab: A wireless sensor network testbed[C]. Information Processing in Sensor Networks IPSN 2005. Fourth International Symposium on. 2005:483-488.
[57] Ertin E Arora A. Ramnath R. A testbed for sensing at scale[C]. Information Processing in Sensor Networks, 2006. IPSN 2006. The Fifth International Conference on. 2006:399-406.
[58] Simulator-NS-2, The Network. [EB/OL]http://www.isi.edu/nsnam/ns/
[59]陈敏. OPNET网络仿真[M]. 2004.
[60] Park S Savvides A. Srivastava M. SensorSim: a simulation framework for sensor networks[C]. Proceedings of the 3rd ACM international workshop on Modeling, analysis and simulation ofwireless and mobile systems. 2000:104-111.
[61] Girod L Ramanathan N. Elson J. A software environment for developing and deploying heterogeneous sensor-actuator networks[J]. ACM Transactions on Sensor Networks. 2007,3(3):1-34.
[62] OMNET++Community Site. [EB/OL]In http://www.omnetpp.org/external/whatis.php.
[63] Zeng X Bagrodia R. Gerla M. a library for parallel simulation of large-scale wireless networks[J]. ACM SIGSIM Simulation Digest. 1998,28(1):154-161.
[64] Levis P Lee N. Welsh M. TOSSIM: Accurate and scalable simulation of entire TinyOS applications[C]. Proceedings of the 1st international conference on Embedded networked sensor systems. 2003:126-137.
[65] Haenggi. Martin. Mobile Sensor-Actuator Networks:Opportunities and Challenges[C]. Proc.of 7th IEEE Intl.Workshop. 2002:283-291.
[66] Shah G. A. Bozyigit M. Akan O. Real Time Coordination and Routing in Wireless Sensor and Actuator Networks[C]. In Proceedings of the 6th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networking NEW2AN. 2006:365-383.
[67] Ngai C. H. Edith Lyu R. Michael Liu. A Real Time Communication Framework forWireless Sensor-Actor Networks[C]. IEEE Aerospace Conference. 2006.2021-2029.
[68] Haidong Yuan Huadong Ma Hongyu Liao. Coordination Mechanism in Wireless Sensor and Actor Networks[C]. Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences (IMSCCS'06). 2006, 2:627-634.
[69]胡四泉李方敏.徐文君.刘新华.无线传感器/执行器网络中能量有效的实时分簇路由协议[J].计算机研究与发展. 2008,45(1):26-33.
[70]张丽.无线传感执行网络中协作机制及算法的研究[D].硕士学位论文, 2007.
[71] Ortiz C. L Eric H. Structured Negotiation[C]. In ICMAS'02. 2002:1215 - 1222.
[72] Ortiz C. L Hsu E. Jardins M. Incremental Negotiation and Coalition Formation for Resource-bounded Agents[C]. In Proceedings of the AAAI Fall Symposium. 2001:116-122.
[73] Yadgar O Kraus S. Ortiz C. Hierarchical organizations for Real-time Large-scale Task and Team Environments[C]. In Proceedings of AAMAS'02. 2002: 1147 - 1148.
[74] Sandholm T Suri S. Improved Algorithms for Optimal Winner Determination in Combinatorial Auctions and Generalizations[C]. In National Conference on Artificial Intelligence(AAAI). 2002: 90-97.
[75] Soh L. K Tsatsoulis C. Satisficing Coalition Formation among Agents[C]. In Proceedings of the International Conference on Autonomous Agents and Multiagent System. 2001: 1062 - 1063.
[76] Soh L. k Tsatsoulis C. Real-time Satisficing Multiagent Coalition Formaiton[C]. In Working Notes of AAAI Workshop on Coalition Formaiton in Dynamic Multiagent Environments. 2002:7-15.
[77] Lesser V Horling B. Klassner F. BIG:An Agent for Resource-Bounded Information Gathering and Decision Making[J]. Artificial Intelligent Journal. 2000. 118:197-244.
[78] Horling B Lesser V. Vincent R. The Soft Real-time Agent Control Architecture[J]. Computer Science Technical Report. 2002:2-14.
[79] Tommaso Melodia Dario Pompili Vehbi C. A Distributed Coordination Framework for Wireless Sensor and Actor Networks. International Symposium on Mobile Ad Hoc Networking & Computing[C]. Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing. 2005:99-110.
[80] Melodia T. Pompili D. Gungor V. Communication and Coordination in Wireless Sensor and Actor Networks[J]. IEEE Transactions on Mobile Computing. 2007.6(10): 1116-1129.
[81] Melodia T. Pompili D. Akyldiz I. A Communication Architecture for Mobile Wireless Sensor and Actor Networks[C]. In Proceeding of IEEE International Conference on Sensor Mesh and Ad hoc Communications and Networks SECON. 2006:142-151.
[82] Dimitris Vassis George Kormentzas Charalabos Skianis. Performance evaluation of single and multichannel actor to actor communication for wireless sensor actor networks[J]. Elsevier B.V. 2005,4(4): 487-498.
[83] Keiji Ozaki Naohiro Hayashibara and Makoto. Coordination Protocols of Multiple Actuator Nodes in a Multi-Actuator/Multi-Sensor Model[C]. 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07). 2007: 62-67.
[84] Keiji Ozakia Tomoya Enokidob Makoto Takizawaa. Coordination protocols for a reliable sensor,actuator, and device network (SADN)[C]. In Proceeding of International Conference on Complex, Intelligent and Software Intensive Systems. 2008: 193-199.
[85] M Lopez-Nores J. Garcia-Duque JJ Pazos. Qualitative assessment of approaches to coordinate activities of mobile hosts in ad hoc networks [J]. IEEE Communications Magazine. 2008. 12. 108-111.
[86] Ameer Ahmed Abbasi Mohamed Younis. Movement-Assisted Connectivity Restoration in Wireless Sensor and Actor Networks [J]. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS. 2009. 20(9): 1366-1379.
[87] Li Fangmin. Real-time energy-aware cluster-based routing protocol for wireless sensor and actor networks[J]. Jisuanji Yanjiu yu Fazhan/Computer Research and Development. 2008,45(1):26-33.
[88] N. Trivedi G. Elangovan S. S. 'Ripples': A message-efficient, distributed clustering algorithm for wireless sensor and actor networks[C]. IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. 2006:53-58.
[89] kkaya Kemal A. Clustering of wireless sensor and actor networks based on sensor distribution and connectivity[J]. Journal of Parallel and Distributed Computing. 2009, 69(6):573-587.
[90] K. Akkaya M. Younis Coverage and delay aware actor placement mechanisms for wireless sensor and actor networks[J]. International Journal of sensor networks. 2008. 3 (3):11-25.
[91] Rappaport. T. Wireless Communication Principles and Practice(2nd Edition)(M). Upper Saddle River, NJ. : London: Prentice Hall PTR. 2002.
[92] V Mhatre C. Rosenberg. Design guidelines for wireless sensor networks: communication, clustering and aggregation[J]. Ad Hoc Networks, Elsevier. 2004,2(1): 45-63.
[93] Zou Y Chakrabarty K. Sensor deployment and target localization based on virtual forces[A]. IEEE INFOCOM[C]. Piscataway,NJ,USA;IEEE Press. 2003. 1293-1303.
[94] Wang Xue. An Improved Co-evolutionary Particle Swarm Optimization for Wireless Sensor Networks with Dynamic Deployment[J]. Sensors. 2007,7(3): 354-370.
[95] K Akkaya F. Senel. Detecting and connecting disjoint sub-networks in wireless sensor and actor networks [J]. Ad Hoc Networks, Elsevier. 2009, 46(7):1330-1346.
[96]崔逊学方红雨朱徐来.传感器网络定位问题的概率特征[J].计算机研究与发展, 2007. (4):630-635.
[97] Nar PC Cayirci E. Pcsmac A power controlled sensor-MAC protocol for wireless sensor networks[C]. In Cayirci E ed Proc Of the IEEE EWSN Piscataway IEEE Computer Society.2005.
[98] Ferenets R. Tarmo Lipping. Comparison of entropy and complexity measures for the assessment of depth of sedation[J]. IEEE Transactions on Biomedical Engineering. 2006. 53(6):1067-1077.
[99] BURKARD RAINER, M. DELL'AMICO S. MARTELLO. Assignment Problems[M]. SIAM. 2009.
[100] Howard Hassig Randy Clark et al. SCADA system makes CSO incidents a thing of the past. [EB/OL]http://ww.pennnet.com/display_article/308489/41/ARTCL/none/none/1/SCADA-System-Makes-CSO-Incidents-a-Thing-of-the-Past/.
[101] J. Kennedy R. C. Eberhart. Particle swarm optimization[C]. Proceedings of the IEEE Int. Conf. Neural Networks IV. 1995:1942-1948.
[102] C. Bean J. Genetic algorithm and random keys for sequencing and optimization[J]. ORSA Journal of Computing. 1994. 6(2):154-160.
[103] Wang L Zheng D. Z. An effective hybrid heuristic for flow shop scheduling problems[J]. Int.J.Adv.Manuf.Technol. 2003. 21: 38-44.
[104] R Reeves C. A genetic algorithm for flowshop sequencing[J]. Computers & Operations Research Oper.Res. 1995,22(1):5-13.
[105] Deb, K. Pratap. A Agarwal, S. and Meyarivan T. A fast and elitist multi-objective genetic algorithm: NSGA-II[J]. IEEE Transaction on Evolutionary Computation, 2002.6(2):181-197.
[106] Ishibuchi H Murata T. A. multi-objective genetic local search algorithm and its application to flowshop scheduling[J]. IEEE Trans. Syst. Man.Cy.B. 1998, 28 (3):392-402.
[107] Ong Y S Keane A. J. Meta-Lamarckian learning in memetic algorithms[J]. IEEE Trans. Evolut. Comput. 2004,8(2):99-110.
[108] Xia Weijun, Wu Zhiming. An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems[J]. Computers and Industrial Engineering 2005. 48(2): 409-425.
[109] Kacem. Imed Genetic algorithm for the flexible job-shop scheduling problem [C]. Proceedings of the IEEE International Conference on Systems. Man and Cybernetics. 2003.4:3464-3469.
[110] AR Rahimi-Vahed SM Mirghorbani J. Comb. A multi-objective particle swarm for a flow shop scheduling problem[J]. Journal of combinatorial optimization. 2007.13(1):79-102.
[111] Tan K C Goh C. K. Yang. Evolving better population distribution and exploration in evolutionsary multi-abjective optimization[J]. Eur. J. Oper. Res. 2006.171(2):463-495.