基于细胞趋化机理的无线传感执行网络节点任务协同算法
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摘要
针对无线传感执行网络中执行器节点协同工作问题,在细胞趋化机理的启发下,提出一种执行器节点协同算法,以减少任务执行过程中节点的移动距离,提高协同响应速度,改善网络能耗过大,节点能量不均的问题.首先,根据细胞趋化机理,在事件紧急度和事件发生频度基础上决定候选执行器节点的数量并动态改变广播范围,再根据候选节点与事件的距离和剩余能量建立协同响应概率序列,在满足事件完成时间的要求下,根据趋化因子自适应分泌机制,实现参与协同处理的执行器节点最优选择.仿真结果表明与现有算法相比,本文采用的算法使网络综合性能得到改善.
Motivated by the mechanism of cell chemotaxis,a collaborative algorithm for the cooperative work problem of actuator nodes in wireless sensor and actor network is proposed to reduce the movement distance of the actuator nodes and improve the speed of cooperation response,further to improve the condition of large energy consumption of network and uneven energy of actuator nodes in the implementation process. First of all,according to the mechanism of cell chemotaxis,the number of candidate actuator nodes is determined and radio range is dynamically changed based on the event emergency degree and event frequency. Then the sequence of collaborative response probability of candidate actuator nodes is established based on the distance from the candidate nodes to event and the nodes' residual energy. Under meeting the requirements of event completion time,the optimal actuator nodes are chosen realtimely to participate in the collaborative process based on the mechanism of chemokine secreting adaptively. The simulation results showthat the method adopted in this paper can improve comprehensive network performance compared with the existing methods.
Motivated by the mechanism of cell chemotaxis,a collaborative algorithm for the cooperative work problem of actuator nodes in wireless sensor and actor network is proposed to reduce the movement distance of the actuator nodes and improve the speed of cooperation response,further to improve the condition of large energy consumption of network and uneven energy of actuator nodes in the implementation process. First of all,according to the mechanism of cell chemotaxis,the number of candidate actuator nodes is determined and radio range is dynamically changed based on the event emergency degree and event frequency. Then the sequence of collaborative response probability of candidate actuator nodes is established based on the distance from the candidate nodes to event and the nodes' residual energy. Under meeting the requirements of event completion time,the optimal actuator nodes are chosen realtimely to participate in the collaborative process based on the mechanism of chemokine secreting adaptively. The simulation results showthat the method adopted in this paper can improve comprehensive network performance compared with the existing methods.
引文
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[8]易军,李太福,许磊.基于功率控制的传感反应网络任务招标策略[J].华中科技大学学报:自然科学版,2011,39(4):33-36.
[9]王艳,纪志成.生物启发的无线传感执行网络协同方法[J].控制理论与应用,2014,31(2):188-194.
[2]Megherbi D B,Malayia V.Cooperation in a distributed hybrid potential-field/reinforcement learning multi agents based path planning in a dynamic time-varying unstructured environment[J].IEEE International Multi Disciplinary Conference on Cognitive Methods in Situation Awareness&Decision Support,2012:80-87.
[3]LI R,LI J,ASAEDA H.A hybrid trust management framework for wireless sensor and actuator networks in cyber-physical systems[J].Leice Transactions on Information&Systems,2014,92(10):2586-2596.
[4]Payne A S,Cornelius L A.The role of chemokines in melanoma tumor growth and metastasis[J].Journal of Investigative Dermatology,2002,118(6):915-922.
[5]Yu Hui,Wang Yong-ji,Cheng Lei.A intelligent group cluster motion control method based on the directed network[C].Control Theory and Application,2007,24(1):79-83.
[6]Gerharz M,de Waal C,Frank M,et al.Link stability in mobile wireless ad hoc networks[C].IEEE Conference on Local Computer Networks,IEEE,2002:30-39.
[7]Yang P,Huang B.Qo S routing protocol based on link stability with dynamic delay prediction in MANET[J].Workshop on Computational Intelligence and Industrial Application,2008,21(1):515-518.
[8]Yi Jun,Li Tai-fu,Xu Lei.A sensor response network task bidding strategy based on power control[J].Journal of Huazhong University of Science and Technology:Natural Science,2011,39(4):33-36.
[9]Wang Yan,Ji Zhi-cheng.Bio-inspired collaborative method for wireless sensor and actor network[J].Control Theory and Application,2014,31(2):188-194.
[10]Wang W,Jiang Y.Community-aware task allocation for social networked multiagent systems[J].IEEE Transactions on Cybernetics,2014,44(9):1529-1543.
[11]Zeng Z,Liu A,Li D,et al.A gighly efficient DAG task scheduling algorithm for wireless sensor networks[C].International Conference for Young Computer Scientists.IEEE Computer Society,2008:570-575.
[12]Mazo M,Tabuada P.Decentralized event-triggered control over wireless sensor/actuator networks[J].Automatic Control,IEEE Transactions on,2011,56(10):2456-2461.
[13]Zhengquan Y,Qing Z,Zengqiang C.A novel adaptive flocking algorithm for multi-agents system with time delay and nonlinear dynamics[C].Control Conference.IEEE,2013:998-1001.
[5]俞辉,王永骥,程磊.基于有向网络的智能群体群集运动控制[J].控制理论与应用,2007,24(1):79-83.
[8]易军,李太福,许磊.基于功率控制的传感反应网络任务招标策略[J].华中科技大学学报:自然科学版,2011,39(4):33-36.
[9]王艳,纪志成.生物启发的无线传感执行网络协同方法[J].控制理论与应用,2014,31(2):188-194.
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