面向SDWSN的分布式高效熵节能分簇路由算法

doi:10.11959/j.issn.1000-0801.2023024

电信科学 ›› 2023, Vol. 39 ›› Issue (3): 100-114.doi: 10.11959/j.issn.1000-0801.2023024

面向SDWSN的分布式高效熵节能分簇路由算法

马巧巧, 董黎刚, 蒋献

浙江工商大学信息与电子工程学院（萨塞克斯人工智能学院），浙江杭州 310018

修回日期:2023-02-02 出版日期:2023-03-20 发布日期:2023-03-01
作者简介:马巧巧（1998– ），女，浙江工商大学硕士生，主要研究方向为软件定义网络
董黎刚（1973– ），男，博士，浙江工商大学信息与电子工程学院院长、教授、硕士生导师，中国电子学会高级会员，浙江省计算机学会理事，浙江省高校学科带头人，主要研究方向为新一代网络和分布式系统
蒋献（1988– ），男，浙江工商大学实验师，主要从事数字电路实验和模拟电路实验、电子设计竞赛指导方面的工作
基金资助:
国家自然科学基金资助项目(61871468);浙江省重点研发计划项目(2021C01036);浙江省自然科学基金资助项目(LY18F010006);浙江省新型网络标准与应用技术重点实验室基金资助项目(2013E10012)

Distributed high-efficiency entropy energy-saving clustering routing algorithm for SDWSN

Qiaoqiao MA, Ligang DONG, Xian JIANG

School of Information and Electronic Engineering (Sussex Artificial Intelligence Institute), Zhejiang Gongshang University, Hangzhou 310018, China

Revised:2023-02-02 Online:2023-03-20 Published:2023-03-01
Supported by:
The National Natural Science Foundation of China(61871468);Zhejiang Province Key Research and Devel-opment Program(2021C01036);Zhejiang Provincial Natural Science Foundation of China(LY18F010006);Zhejiang Provincial Key Laboratory of New Network Standards and Application Technology(2013E10012)

摘要/Abstract

摘要：

为提高软件定义无线传感器网络（software defined wireless sensor network，SDWSN）的寿命并降低数据传输能耗，设计一种基于萤火虫算法（firefly algorithm，FA）和生物地理学优化（biogeography-based optimization，BBO）的混合优化算法，并基于该混合优化算法，设计一种分布式高效熵节能分簇路由算法（distributed high-efficiency entropy energy-saving cluster routing algorithm，DHEEC），将能量熵和混合优化算法用于簇头选择。仿真结果表明，混合优化算法在标准函数上的表现优于其他算法，DHEEC的10%节点死亡轮数比IFCEER提高了约41.05%，比DEEC-FA提高了约13.89%，并提高了能量利用率。

关键词: 分簇路由, 萤火虫算法, 生物地理学优化, 能量效率

Abstract:

In order to improve the life of software defined wireless sensor network (SDWSN) and reduce the energy consumption of data transmission, a hybrid optimization algorithm based on firefly algorithm (FA) and biogeography-based optimization (BBO) were designed, and based on the hybrid optimization algorithm, a distributed high-efficiency entropy energy-saving clustering routing algorithm (DHEEC) was designed, using energy entropy and hybrid optimization algorithm for cluster head selection.The simulation results show that the hybrid optimization algorithm outperforms other algorithms on standard functions.The number of death rounds of 10% nodes of DHEEC is about 13.89% higher than that of IFCEER, and about 41.05% higher than that of DEEC-FA, and the energy utilization is improved.

Key words: cluster routing, firefly algorithm, biogeographic-based optimization, energy efficiency

马巧巧, 董黎刚, 蒋献. 面向SDWSN的分布式高效熵节能分簇路由算法[J]. 电信科学, 2023, 39(3): 100-114.

Qiaoqiao MA, Ligang DONG, Xian JIANG. Distributed high-efficiency entropy energy-saving clustering routing algorithm for SDWSN[J]. Telecommunications Science, 2023, 39(3): 100-114.

图/表 14

图1

图2

图3

图4

表1

图5

表2

用来测试的标准函数"

函数名称	数学表达式	范围
Sphere	$f_{1} (x) = \sum_{i = 1}^{n} x_{i}^{2}$	[-100,100]
Schwefel	$f_{2} (X) = \sum_{i = 1}^{n} \| x_{i} \| + \prod_{i = 1}^{n} \| x_{i} \|$	[-10,10]
Rastrigin	$f_{3} (x) = \sum_{i = 1}^{n} [x_{i}^{2} - 10 \cos (2 π x_{i}) + 10]$	[-5.12,5.12]
Ackley	$f_{4} (x) = - 20 \exp [- 0.2 \sqrt{\frac{1}{n} \sum_{i = 1}^{n} x_{i}^{2}}] -$	[-32,32]
	$\exp (\frac{1}{n} \sum_{i = 1}^{n} \cos (2 π x_{i})) + 20 + e$
Step	$f_{5} (x) = \sum_{i = 1}^{n} {(⌊ x_{i} + 0.5 ⌋)}^{2}$	[-100,100]

表2

图6

图7

表3

图8

表4

网络仿真实验的部分参数"

参数	参数值
网络区域	100 m ×100 m
传感器节点个数	100
基站位置	（50,50）
传输数据包/bit	4 000
$E_{0} / J$	0.5
$E_{elec} /(nJ \cdot {bit}^{- 1})$	50
$ε_{fs} / (nJ \cdot {bit}^{- 1} \cdot m^{- 2})$	10
$ε_{mp} / (pJ \cdot {bit}^{- 1} \cdot m^{- 4})$	0.0013
$E_{DA} / (nJ \cdot {bit}^{- 1} \cdot {signal}^{- 1})$	5

表4

图9

图10

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数据传输方式	条件
直接传输	d<dis
簇头传输	d≥dis

函数	PSOGWO		HFPSO		FGB
函数	最优值	运行时间/s	最优值	运行时间/s	最优值	运行时间/s
f₁	1.433 5×10^-26	2.21	4.779 5×10^-9	2.39	9.082 4 ×10^-17	1.94
f₂	9.465 3×10^-9	2.05	5.632 9×10^-5	2.30	3.715 4×10^-8	1.97
f₃	23.624 7	1.98	36.813 5	2.11	20.894 1	1.91
f₄	1.333 4×10^-7	1.85	2.852 2×10^-5	1.86	7.876 9×10^-9	2.27
f₅	374.867	1.19	0	1.47	0	2.08

面向SDWSN的分布式高效熵节能分簇路由算法

Distributed high-efficiency entropy energy-saving clustering routing algorithm for SDWSN

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