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

Abstract

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

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.

Figures/Tables 14

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]

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

References 24

[1]	丁铖, 陈锦荣, 曹小冬 ,等. 基于服务质量的层次化结构资源分配算法[J]. 电信科学, 2022,38(1): 102-111.
	DING C , CHEN J R , CAO X D ,et al. Hierarchical structure resource allocation algorithm based on quality of service[J]. Telecommunication Science, 2022,38(1): 102-111.
[2]	汤季, 谭小波, 朱腾 . SNDQ-WISE:一种新的基于SDWSN的QoS路由协议[J]. 沈阳理工大学学报, 2019,38(4): 18-24,51.
	TANG J , TAN X B , ZHU T . SNDQ-WISE:a new QoS routing protocol based on SDWSN[J]. Journal of Shenyang Ligong University, 2019,38(4): 18-24,51.
[3]	HAWBANI A , WANG X F , ZHAO L ,et al. Novel architecture and heuristic algorithms for software-defined wireless sensor networks[J]. IEEE/ACM Transactions on Networking, 2020,28(6): 2809-2822.
[4]	黄美根, 黄一才, 郁滨 ,等. 软件定义无线传感器网络研究综述[J]. 软件学报, 2018,29(9): 2733-2752.
	HUANG M G , HUANG Y C , YU B ,et al. Software-defined wireless sensor networks:a research survey[J]. Journal of Software, 2018,29(9): 2733-2752.
[5]	MAHMUDDIN M , ALABADLEH W A , KAMARUDIN L M . A comparative study on hoping mechanism of LEACH protocol in wireless sensor networks:a survey[J]. IOP Conference Series:Materials Science and Engineering, 2019,551(1): 012056.
[6]	GHOSH R , MOHANTY S , PATTNAIK P K ,et al. A performance assessment of power-efficient variants of distributed energy-efficient clustering protocols in WSN[J]. International Journal of Interactive Communication Systems and Technologies, 2020,10(2): 1-14.
[7]	曹宇 . 基于改进SEP协议的WSN能量优化方案研究[D]. 南京:南京邮电大学, 2019.
	CAO Y . Research on WSN energy optimization scheme based on improved SEP protocol[D]. Nanjing:Nanjing University of Posts and Telecommunications, 2019.
[8]	刘奕岑, 徐蔚鸿, 陈沅涛 ,等. 具有适应度选择调整策略的混沌遗传算法及其应用[J]. 计算技术与自动化, 2019,38(2): 8-14.
	LIU Y C , XU W H , CHEN Y T ,et al. Chaos genetic algorithm with fitness selection adjustment strategy and its application[J]. Computing Technology and Automation, 2019,38(2): 8-14.
[9]	RAYENIZADEH M , RAFSANJANI M K , SAEID A B . Cluster head selection using hesitant fuzzy and firefly algorithm in wireless sensor networks[J]. Evolving Systems, 2022,13(1): 65-84.
[10]	REN Z C , ZHANG L Y , TANG J Y ,et al. Improved artificial bee colony algorithm based on cauchy OBL[J]. Journal of Physics:Conference Series, 2021,1920(1): 012108.
[11]	SINGH S , MALIK A , KUMAR R . Energy efficient heterogeneous DEEC protocol for enhancing lifetime in WSNs[J]. Engineering Science and Technology,an International Journal, 2017,20(1): 345-353.
[12]	BHOLA J , SONI S , CHEEMA G K . Genetic algorithm based optimized leach protocol for energy efficient wireless sensor networks[J]. Journal of Ambient Intelligence and Humanized Computing, 2020,11(3): 1281-1288.
[13]	POLURU R K , REDDY P K M , BASHA S M ,et al. Enhanced adaptive distributed energy-efficient clustering (EADEEC) for wireless sensor networks[J]. Recent Advances in Computer Science and Communications, 2020,13(2): 190-194.
[14]	LU S X , WU M Q , ZHAO M . Particle swarm optimization and artificial bee colony algorithm for clustering and mobile based software-defined wireless sensor networks[J]. Wireless Networks, 2022,28(4): 1671-1688.
[15]	罗剑, 毕晓东 . 基于改进萤火虫聚类的异构 WSN 能耗优化路由算法[J]. 传感技术学报, 2018,31(10): 1584-1591.
	LUO J , BI X D . Optimized energy efficient routing algorithm based on improved firefly clustering for heterogeneous WSN[J]. Chinese Journal of Sensors and Actuators, 2018,31(10): 1584-1591.
[16]	余修武, 秦晓坤, 刘永 . 基于萤火虫算法优化 FCM 的 WSN路由算法[J]. 北京邮电大学学报, 2022,45(2): 50-56.
	YU X W , QIN X K , LIU Y . WSN routing algorithm based on firefly algorithm to optimize FCM[J]. Journal of Beijing University of Posts and Telecommunications, 2022,45(2): 50-56.
[17]	ZHANG L M , TANG Y G , HUA C C ,et al. A new particle swarm optimization algorithm with adaptive inertia weight based on Bayesian techniques[J]. Applied Soft Computing, 2015(28): 138-149.
[18]	AYDILEK ? B . A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems[J]. Applied Soft Computing, 2018(66): 232-249.
[19]	PITCHAIMANICKAM B , MURUGABOOPATHI G . A hybrid firefly algorithm with particle swarm optimization for energy efficient optimal cluster head selection in wireless sensor networks[J]. Neural Computing and Applications, 2020,32(12): 7709-7723.
[20]	BASITH K A , SHANKAR T N . Hybrid state analysis with improved firefly optimized linear congestion models of WSN for DDoS ＆ CRA attacks[J]. PeerJ Computer Science, 2022(8): e845.
[21]	KUMARI C , YADAV H , CHHIKARA R . Hybrid model using firefly and BBO for feature selection in software product line[J]. Recent Advances in Computer Science and Communications, 2021,14(9): 2754-2760.
[22]	SHRIVASTAVA A , SHARMA A , PANDIT M ,et al. Hybrid protection scheme based optimal overcurrent relay coordination strategy for RE integrated power distribution grid[J]. Energies, 2021,14(21): 7192.
[23]	张芬 . 基于软件定义的 WSN 分簇路由算法研究[D]. 兰州:西北师范大学, 2021.
	ZHANG F . Research on clustering routing algorithm based on software-defined in WSN[D]. Lanzhou:Northwest Normal University, 2021.
[24]	尚静, 董增寿, 康琳 . 异构网络中基于 DEEC 的非均匀分簇路由算法[J]. 太原科技大学学报, 2018,39(2): 90-94.
	SHANG J , DONG Z S , KANG L . An non-uniform clustering routing algorithm based on DEEC in heterogeneous networks[J]. Journal of Taiyuan University of Science and Technology, 2018,39(2): 90-94.

Metrics

Recommended 0

No Suggested Reading articles found!

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

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

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 14

References 24

Related Articles 15

Metrics

Recommended 0

[1]	Yueyu JIANG, Haoxin CHENG, Kang WANG, Wenjun DAI, Xiaoyu LIU. Design method of energy efficiency optimization in PLC-RF wireless transmission networks [J]. Telecommunications Science, 2023, 39(4): 111-119.
[2]	Yahui XUE, Zecai SHAO, Chunfeng CUI, Jing JIN, Hanning WANG. Optimal strategy for maximizing energy efficiency of 4G and 5G coexistence network [J]. Telecommunications Science, 2022, 38(9): 187-192.
[3]	Haiyan CAO, Zhongliang WANG, Hao XU, Qianhong CHEN, Fangmin XU. Energy efficiency joint optimization algorithm for mixed-ADC massive MIMO systems [J]. Telecommunications Science, 2022, 38(8): 65-74.
[4]	Dan XU, Yu ZENG, Weiye MENG, Lika LI. AI-enabled 5G energy-saving technology [J]. Telecommunications Science, 2021, 37(5): 32-41.
[5]	Haiyan CAO,Wenjuan HU,Yingjuan ZHI,Fangmin XU,Xin FANG. Energy efficiency joint optimization algorithm for low-precision ADC massive MIMO systems [J]. Telecommunications Science, 2020, 36(6): 90-96.
[6]	Lin XU,Zhijin ZHAO. A distributed CRN resource allocation algorithm based on CBR and cooperative Q-learning [J]. Telecommunications Science, 2019, 35(2): 35-42.
[7]	Jieliang LOU. Data center energy efficiency case and energy saving operation [J]. Telecommunications Science, 2019, 35(2): 95-104.
[8]	Haijiang GE, Zhanwei YU, Kaikai CHI. Energy-efficiency maximization scheme for data collection in wireless power communication networks [J]. Telecommunications Science, 2019, 35(12): 49-56.
[9]	Xiuying NIE. Measures and optimization methods of communications network energy efficiency [J]. Telecommunications Science, 2019, 35(11): 84-87.
[10]	Ying ZHOU,Lihua YANG,Longxiang YANG,Meng NI. Energy-efficient data gathering scheme based on Kalman prediction and compressed sensing [J]. Telecommunications Science, 2019, 35(1): 74-80.
[11]	Yang CHEN,Zhenzhu LEI,Bin SHEN. Small cell discovery based on TOA fingerprint database [J]. Telecommunications Science, 2017, 33(9): 163-173.
[12]	Yunchuan WANG,Xiaorong XU,Yingbiao YAO,Weiwei WU. Design and performance analysis of energy efficiency based CR primary user emulation attack defensive scheme [J]. Telecommunications Science, 2017, 33(8): 100-106.
[13]	Yuzhou LI,Tao JIANG,Yang CAO,Zan LI. Green 5G ultra-dense wireless heterogeneous networks:guidelines,techniques,and challenges [J]. Telecommunications Science, 2017, 33(6): 34-40.
[14]	Liang GE,Zhixu WANG. Optimal deployment of energy-efficient ultra-dense network [J]. Telecommunications Science, 2017, 33(3): 44-51.
[15]	Ting LI,Linjie QIU,Wei JI. Energy-efficient optimization algorithm based on vertical beamforming for 3D MIMO-OFDMA system [J]. Telecommunications Science, 2017, 33(11): 17-26.