高级检索

  • ISSN 1006-3080
  • CN 31-1691/TQ

模糊群智能驱动的软件定义型传感网路由优化

杨昊晨 黄如

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). 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). 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. 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. 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

doi: 10.14135/j.cnki.1006-3080.20220302001
基金项目: 国家自然科学基金(61673178,61922063);上海市自然科学基金(20ZR1413800)
详细信息
    作者简介:

    杨昊晨(1996—),男,江苏人,硕士生,主要研究方向为无线传感器网络。E-mail:y30180631@mail.ecust.edu.cn

    通讯作者:

    黄 如, E-mail:huangrabbit@ecust.edu.cn

  • 中图分类号: TN92

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,并且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,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。

     

  • 图  1  SDN-WISE的结构

    Figure  1.  The structure of SDN-WISE

    图  2  FABCR路由系统结构

    Figure  2.  FABCR routing system structural

    图  3  食物源与传感器网络路由的映射

    Figure  3.  Mapping of food sources to sensor network routes

    图  4  链路初始化与邻域搜索

    Figure  4.  Link initialization and Neighborhood search

    图  5  模糊逻辑结构

    Figure  5.  Fuzzy logic structure

    图  6  群智能模糊模块控制曲面

    Figure  6.  Swarm intelligent fuzzy module control surface

    图  7  SD-WSN 300与800个节点分布图

    Figure  7.  Distribution of SD-WSN 300 and 800 nodes

    图  8  节点规模自适应算法性能对比

    Figure  8.  Comparison of performance of algorithms with adaption node number

    图  9  节点数量和通信距离变化下的平均链路长度

    Figure  9.  Average link length under the change of node number and communication distance

    图  10  通信距离自适应算法性能对比

    Figure  10.  Comparison of performance of algorithms with adaption communication distance

    图  11  智能体数量可调性算法性能对比

    Figure  11.  Comparison of performance of algorithms with tunable swarm size

    图  12  智能体数量可调性FABCR性能指标对比

    Figure  12.  Comparison of performance of FABCR with tunable swarm size

    图  13  隶属函数可变性算法性能指标对比

    Figure  13.  Comparison of performance of algorithms with variable membership function

    图  14  隶属函数可变性FABCR性能指标对比

    Figure  14.  Comparison of performance of FABCR with variable membership function

    表  1  SDN流表内容

    Table  1.   SDN Flow table content

    Match Rule 1Op.Match Rule 2Op.Match Rule 3ActionStatistic
    TypeValueTTLCounter
    Sink node=1&&Source node=15&&Current node=6ForwardNode 1122
    Sink node=1&&Source node=20&&Current node=7FowardNode 2103
    Neighbour node=0||Buffer=10//Drop-86
    Energy vulnerability<5Time vulnerability>8Space vulnerability>20SetReport93
    下载: 导出CSV

    表  2  模糊控制规则库核心内容

    Table  2.   The core content of fuzzy control rule base

    RuleEnergy vulnerabilityTime vulnerabilitySpace vulnerabilityArea vulnerability
    1HighHighHighVery High
    2HighLowLowLow
    3LowMediumHighMedium
    4LowHighHighHigh
    5LowLowLowVery low
    下载: 导出CSV

    表  3  实验参数

    Table  3.   Experiment parameters

    SDNSwarm IntelligenceFuzzy control
    ParameterValueParameterValueParameterValue
    Number of network nodes300-800Swarm size10Number of fuzzy rules27
    Network size100 m*100 mFood source abandonment criteria50 cyclesFuzzy membership functionTriangle-shape
    SDN Controller position(50,50)Search termination criteria250 cyclesFuzzy output space partition number5
    SDN Collection
    packet size
    14 ByteField scale1Fuzzy input space partition number3
    Communication range7-12 mNumber of fitness function parameters3Fuzzy Inference SystemsMamdani
    Node initial energy10 JDimension of the solution[5,12]Defuzzification methodCentroid
    Data packet size12 ByteEnergy parameter range[0,10]Fuzzy rule connectionAnd
    Node buffer size128 ByteCongestion parameter range[0,10]Energy vulnerability range[0,10]
    Number of
    data packet
    800Link parameter range[5,12]Time vulnerability range[0,10]
    Buffer typeDrop tailVulnerable parameter range[0,10]Space vulnerability range[0,50]
    下载: 导出CSV
  • [1] JINO RAMSON S R, MONI D J. Applications of wireless sensor networks: A survey[C]//2017 International Conference on Innovations in Electrical, Electronics, Instrumentation and Media Technology (ICEEIMT). 2017: 325-329
    [2] MOHAMED R E, SALEH A I, ABDELRAZZAK M, et al. Survey on Wireless Sensor Network Applications and Energy Efficient Routing Protocols[J]. Wireless Personal Communications, 2018, 101(2): 1019-1055. doi: 10.1007/s11277-018-5747-9
    [3] WANG J, GAO Y, LIU W, et al. An Improved Routing Schema with Special Clustering Using PSO Algorithm for Heterogeneous Wireless Sensor Network[J]. Sensors, 2019, 19(3): 1-17. doi: 10.1109/JSEN.2018.2879242
    [4] BONANNI M, CHITI F, FANTACCI R, et al. Dynamic Control Architecture Based on Software Defined Networking for the Internet of Things[J]. Future Internet, 2021, 13(5): 1-15.
    [5] NDIAYE M, HANCKE G P, ABU-MAHFOUZ A M. Software defined networking for improved wireless sensor network management: A survey[J]. sensors, 2017, 17(5): 1-32. doi: 10.1109/JSEN.2017.2655998
    [6] ALVES R C A, OLIVEIRA D A G, NUNEZ SEGURA G A, et al. The cost of software-defining things: a scalability study of software-defined sensor networks[J]. IEEE Access, 2019, 7: 115093-115108. doi: 10.1109/ACCESS.2019.2936127
    [7] BISWAS A, MISHRA K K, TIWARI S, et al. Physics-inspired optimization algorithms: A survey[J]. Journal of Optimization, 2013, 2013: 1-16.
    [8] MASOOD M, FOUAD M M, SEYEDZADEH S, et al. Energy efficient software defined networking algorithm for wireless sensor networks[J]. Transportation Research Procedia, 2019, 40: 1481-1488. doi: 10.1016/j.trpro.2019.07.205
    [9] GALLUCCIO L, MILARDO S, MORABITO G, et al. SDN-WISE: Design, prototyping and experimentation of a stateful SDN solution for WIreless SEnsor networks[C]//2015 IEEE Conference on Computer Communications (INFOCOM). 2015: 513-521
    [10] ABDOLMALEKI N, AHMADI M, MALAZI H T, et al. Fuzzy topology discovery protocol for SDN-based wireless sensor networks[J]. Simulation Modelling Practice and Theory, 2017, 79: 54-68. doi: 10.1016/j.simpat.2017.09.004
    [11] ZHENG W, LUO D. Routing in wireless sensor network using artificial bee colony algorithm[C]//2014 International Conference on Wireless Communication and Sensor Network. 2014: 280-284
    [12] AMIRI E, KESHAVARZ H, ALIZADEH M, et al. Energy efficient routing in wireless sensor networks based on fuzzy ant colony optimization[J]. International Journal of Distributed Sensor Networks, 2014, 10(7): 1-17.
    [13] RAGAVAN P S, RAMASAMY K. Optimized routing in wireless sensor networks by establishing dynamic topologies based on genetic algorithm[J]. Cluster Computing, 2019, 22(5): 12119-12125.
    [14] SAMPATHKUMAR A, MULERIKKAL J, SIVARAM M. Glowworm swarm optimization for effectual load balancing and routing strategies in wireless sensor networks[J]. Wireless Networks, 2020, 26(6): 4227-4238. doi: 10.1007/s11276-020-02336-w
    [15] ZOU Z, QIAN Y. Wireless sensor network routing method based on improved ant colony algorithm[J]. Journal of Ambient Intelligence and Humanized Computing, 2019, 10(3): 991-998. doi: 10.1007/s12652-018-0751-1
    [16] GALLUCCIO L, MILARDO S, MORABITO G, et al. Reprogramming wireless sensor networks by using SDN-WISE: A hands-on demo[C]//2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). 2015: 19-20
    [17] MOSTAFAEI H, MENTH M. Software-defined wireless sensor networks: A survey[J]. Journal of Network and Computer Applications, 2018, 119: 42-56. doi: 10.1016/j.jnca.2018.06.016
    [18] KOBO H I, ABU-MAHFOUZ A M, HANCKE G P. A survey on software-defined wireless sensor networks: Challenges and design requirements[J]. IEEE Access, 2017, 5: 1872-1899. doi: 10.1109/ACCESS.2017.2666200
    [19] AKBARI R, HEDAYATZADEH R, ZIARATI K, et al. A multi-objective artificial bee colony algorithm[J]. Swarm and Evolutionary Computation, 2012, 2: 39-52. doi: 10.1016/j.swevo.2011.08.001
    [20] KARABOGA D, AKAY B. A comparative study of artificial bee colony algorithm[J]. Applied Mathematics and Computation, 2009, 214(1): 108-132. doi: 10.1016/j.amc.2009.03.090
    [21] KARABOGA D, BASTURK B. On the performance of artificial bee colony (ABC) algorithm[J]. Applied Soft Computing, 2008, 8(1): 687-697. doi: 10.1016/j.asoc.2007.05.007
    [22] CAI X, DUAN Y, HE Y, et al. Bee-Sensor-C: An energy-efficient and scalable multipath routing protocol for wireless sensor networks[J]. International Journal of Distributed Sensor Networks, 2015, 11(3): 1-14.
    [23] 宁爱平, 张雪英. 人工蜂群算法的收敛性分析[J]. 控制与决策. 2013, 28(10): 1554-1558.
    [24] BOHLOULZADEH A, RAJAEI M. A survey on congestion control protocols in wireless sensor networks[J]. International Journal of Wireless Information Networks, 2020, 27(3): 365-384. doi: 10.1007/s10776-020-00479-3
    [25] BARADARAN A A, NAVI K. HQCA-WSN: High-quality clustering algorithm and optimal cluster head selection using fuzzy logic in wireless sensor networks[J]. Fuzzy Sets and Systems, 2020, 389: 114-144. doi: 10.1016/j.fss.2019.11.015
    [26] DUTT S, AGRAWAL S, VIG R. Delay-sensitive, reliable, energy-efficient, adaptive and mobility-aware (dream) routing protocol for WSNs[J]. Wireless Personal Communications, 2021, 120(2): 1675-1703. doi: 10.1007/s11277-021-08528-7
    [27] KAUR T, KUMAR D. MACO-QCR: Multi-objective aco-based qos-aware cross-layer routing protocols in WSN[J]. IEEE Sensors Journal, 2021, 21(5): 6775-6783. doi: 10.1109/JSEN.2020.3038241
    [28] AGARKHED J, KADROLLI V, PATIL S. Fuzzy based multi-level multi-constraint multi-path reliable routing in wireless sensor network[J]. International Journal of Information Technology, 2020, 12(4): 1133-1146. doi: 10.1007/s41870-020-00476-y
  • 加载中
图(14) / 表(3)
计量
  • 文章访问数:  21
  • HTML全文浏览量:  42
  • PDF下载量:  3
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-03-02
  • 网络出版日期:  2022-05-13

目录

    /

    返回文章
    返回