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    杨昊晨, 黄如. 模糊群智能驱动的软件定义型传感网路由优化[J]. 华东理工大学学报(自然科学版), 2023, 49(4): 562-575. DOI: 10.14135/j.cnki.1006-3080.20220302001
    引用本文: 杨昊晨, 黄如. 模糊群智能驱动的软件定义型传感网路由优化[J]. 华东理工大学学报(自然科学版), 2023, 49(4): 562-575. DOI: 10.14135/j.cnki.1006-3080.20220302001
    YANG Haochen, HUANG Ru. Routing Optimization Driven by Fuzzy Swarm Intelligence in Software-Defined Sensor Networks[J]. Journal of East China University of Science and Technology, 2023, 49(4): 562-575. DOI: 10.14135/j.cnki.1006-3080.20220302001
    Citation: YANG Haochen, HUANG Ru. Routing Optimization Driven by Fuzzy Swarm Intelligence in Software-Defined Sensor Networks[J]. Journal of East China University of Science and Technology, 2023, 49(4): 562-575. DOI: 10.14135/j.cnki.1006-3080.20220302001

    模糊群智能驱动的软件定义型传感网路由优化

    Routing Optimization Driven by Fuzzy Swarm Intelligence in Software-Defined Sensor Networks

    • 摘要: 无线传感器网络由于基础设施建设等固有因素,必须考虑网络资源有限和资源消耗不均匀的问题。基于群智能模糊控制,将模糊控制引入群智能人工蜂群路由协议,解决软件定义传感器网络下的多径路由规划寻优问题。基于无线传感器网络的软件定义网络(Software Defined Networking for Wireless Sensor Networks,SDN-WISE)架构和群智能算法,通过产生人工蜂群模拟蜜蜂采蜜的过程搜索最优链路。人工蜂群对不同数据传输链路进行调整,利用模糊逻辑判断区域状态,并通过生成适应度函数评价出价值最高的数据链路,产生一个优化路由解决方案。实验结果表明,与经典路由算法对比,本文基于软件定义无线传感器网络(Software Defined Wireless Sensor Network, SD-WSN)的模糊人工蜂群优化路由(Fuzzy Artificial Bee Colony Routing,FABCR)机制,采用SDN-WISE在松耦合的软件定义网络架构下,融合人工蜂群的代理自适应能力与模糊控制的容错逻辑,使得优化路由问题求解过程在能量管理、网络利用率、传输时延和数据包传达率上均有明显的优势。

       

      Abstract: Due to inherent factors such as infrastructure construction, wireless sensor networks must consider the limited network resources and uneven resource consumption. In this paper, the fuzzy control technique is introduced into the artificial bee swarm routing protocol based on swarm intelligence to solve the optimization problem of multipath routing planning for the software-defined sensor networks. Based on the SDN-WISE software defined network architecture and swarm intelligence algorithm, the optimal link is searched by generating artificial bees to simulate the process of honey gathering. By adjusting different data transmission links via artificial bees, judging the regional state via fuzzy logic, and generating fitness function to evaluate the data link with the highest value, an optimized routing solution can be obtained. Compared with the classical routing algorithms, the proposed method in this work can optimize the solving process of the routing problem in the framework of loosely coupled software-defined network by integrating the agent adaptive ability of artificial bees and the fault-tolerant logic of fuzzy control and has obvious advantages in residual energy management, network utilization, transmission delay and packet delivery rate.

       

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