信息网络安全 ›› 2018, Vol. 18 ›› Issue (8): 8-9.doi: 10.3969/j.issn.1671-1122.2018.08.002

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WSANs中基于生物免疫机制的A-A智能协同方法

王艳, 潘琛()   

  1. 江南大学物联网技术应用教育部工程研究中心,江苏无锡 214122
  • 收稿日期:2018-03-10 出版日期:2018-08-20 发布日期:2020-05-11
  • 作者简介:

    作者简介:王艳(1978—),女,江苏,教授,博士,主要研究方向为网络化控制系统、无线传感器网络;潘琛(1988—),女,河南,硕士研究生,主要研究方向为无线传感器网络协同控制。

  • 基金资助:
    国家自然科学基金[61572238];国家高技术研究发展计划(863计划)[2014AA041505];江苏省杰出青年基金[BK20160001]

An A-A Intelligent Collaborative Method Based on Biological Immune Mechanism in WSANs

Yan WANG, Chen PAN()   

  1. Engineering Research Center of Internet of Things Technology Applications, Ministry of Education, Jiangnan University, Wuxi Jiangsu 214122, China
  • Received:2018-03-10 Online:2018-08-20 Published:2020-05-11

摘要:

文章受生物免疫机理启发,提出一种基于生物免疫机制的A-A智能协同方法。该方法以求解系统最大约束时间及参与协同的执行器节点数为目标,在任务协作时间模型中引入状态预测函数得出系统最大约束时间。在选择节点协同处理任务时,运用生物免疫机理求解最终参与协同的执行器节点数。在功率控制技术协助下,执行器节点可动态改变协同范围并自主决策优势执行器节点参与任务处理,让优势候选执行器节点有更多的机会参与协同工作,实现了任务与执行器节点的高效匹配,解决了能耗不均衡问题,延长了网络寿命。仿真结果表明,文中算法相比典型的RC和MOTS算法,任务平均完成时间明显减少,网络寿命明显提高。

关键词: 无线传感执行网络, 生物免疫, 状态预测, 协同, 任务

Abstract:

Inspired by the biological immune mechanism, this paper proposes an A-A intelligent collaborative method based on biological immune mechanism. The method aims to solve the system’s maximum constraint time and the number of participating actor nodes. In the task cooperation time model, the state prediction function is introduced to obtain the maximum constraint time of the system; when the node is co-processed, the biological immune mechanism is used to solve the number of actor nodes that ultimately participate in the coordination. With the help of power control technology, the actor node can dynamically change the collaborative range and autonomously decide the dominant actor node to participate in the task processing, so that the superior candidate actor node has more opportunities to participate in the collaborative work, achieving efficient matching of the task and the actor node. It solves the problem of uneven energy consumption and prolongs the network life as much as possible. The simulation results show that the proposed algorithm has better performance than typical RC and MOTS algorithms in terms of the average completion time and the network lifetime.

Key words: wireless sensor and actor networks, biological immune, state prediction, collaboration, task

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