Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (6): 358-366.doi: 10.11896/jsjkx.250400094

• Computer Network • Previous Articles     Next Articles

Fuzzy Clustering-based DTN Routing Algorithm for IoT

CHANG Yanan1, SUN Yi1, CUI Jianqun1,2, YAN Xianglong1, ZONG Chenglu1   

  1. 1 School of Computer Science,Central China Normal University,Wuhan 430079,China
    2 Hubei Key Laboratory of Artificial Intelligence and Intelligent Learning,Central China Normal University,Wuhan 430079,China
  • Received:2025-04-21 Revised:2025-09-14 Online:2026-06-15 Published:2026-06-09
  • About author:CHANG Yanan,born in 1984,Ph.D,associate professor,is a member of CCF(No.35671M).Her main research interests include wireless network,social network and Internet of Things.
    CUI Jianqun,born in 1974,Ph.D,professor,is a member of CCF(No.27790M).Her main research interests include opportunity network,Internet of Things,mobile network and application layer multicast.
  • Supported by:
    General Program of National Natural Science Foundation of China(62372206,62372206,62272189),Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning(2025AISL008) and Fundamental Research Funds for the Central Universities(CCNU25JCPT038).

PDF (PC)

38

Abstract: In IoT DTN networks,the sparse and highly dynamic distribution of nodes makes it difficult for source nodes to establish direct connections with destination nodes for data transmission.Therefore,how to design an efficient and stable routing algorithm is a critical challenge in IoT DTN networks.In recent years,the rapid development of machine learning has provided new insights into routing optimization,enabling algorithms to extract hidden patterns from complex network data and enhance decision-making efficiency and accuracy.This paper proposes FCMROP(An IoT DTN Routing Algorithm Based on the Fuzzy Clustering Model).The algorithm employs a multi-feature fusion-based dynamic clustering method,considering node buffer,activity le-vel,delivery probability,and structural stability to improve cluster partitioning and routing decision accuracy.Additionally,a fuzzy membership-weighted distance metric is introduced to optimize cluster selection.Simulation results demonstrate that compared to GMMR,DBSCAN-R,KROP,and Prophet routing algorithms,FCMROP achieves superior performance in delivery rate,average delay,and message drop rates.

Key words: DTN, Unsupervised learning, Internet of Things, Fuzzy clustering

CLC Number: 

  • TP393
[1]TAN L,WANG N.Future internet:The internet of things[C]//2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE).IEEE,2010:V5-376-V5-380.
[2]RAY P P.A survey on Internet of Things architectures[J].Journal of King Saud University-Computer and Information Sciences,2018,30(3):291-319.
[3]YAN Y,CHEN Z,WU J,et al.Effective data transmissionstrategy based on node socialization in opportunistic social networks[J].IEEE Access,2019,7:22144-22160.
[4]LE T.Multi-hop routing under short contact in delay tolerant networks[J].Computer Communications,2021,165:1-8.
[5]CHEN Y K.Challenges and opportunities of internet of things[C]//17th Asia and South Pacific Design Automation Confe-rence.IEEE,2012:383-388.
[6]BURLEIGH S,HOOKE A,TORGERSON L,et al.Delay-tole-rant networking:an approach to interplanetary internet[J].IEEE Communications Magazine,2003,41(6):128-136.
[7]FAWAZ W,ATALLAH R,ASSI C,et al.Unmanned aerial vehicles as store-carry-forward nodes for vehicular networks[J].IEEE Access,2017,5:23710-23718.
[8]MAO Y,ZHOU C,LING Y,et al.An optimized probabilistic delay tolerant network(DTN) routing protocol based on scheduling mechanism for internet of things(IoT)[J].Sensors,2019,19(2):243.
[9]GANDHI J,NARMAWALA Z.A comprehensive survey on machine learning techniques in opportunistic networks:Advances,challenges and future directions[J].Pervasive and Mobile Computing,2024,100:101917.
[10]CUI J Q,YAN H R,CHANG Y N,et al.Routing Algorithm Combining Collaborative Filtering and Encounter Probability Prediction in DTN[J].Journal of Chinese Computer Systems,2025,46(3):735-743.
[11]VASHISHTH V,CHHABRA A,SHARMA D K.A machinelearning approach using classifier cascades for optimal routing in opportunistic internet of things networks[C]//2019 16th AnnualIEEE International Conference on Sensing,Communication,and Networking(SECON).IEEE,2019:1-9.
[12]KANDHOUL N,DHURANDHER S K,WOUNGANG I.Random forest classifier－based safe and reliable routing foropportunistic IoT networks[J].International Journal of Communication Systems,2021,34(1):e4646.
[13]DUDUKOVICH R,HYLTON A,PAPACHRISTOU C.A machine learning concept for DTN routing[C]//2017 IEEE International Conference on Wireless for Space and Extreme Environments(WiSEE).IEEE,2017:110-115.
[14]SHARMA D K,RODRIGUES J J P C,VASHISHTH V,et al.RLProph:a dynamic programming based reinforcement learning approach for optimal routing in opportunistic IoT networks[J].Wireless Networks,2020,26:4319-4338.
[15]CHANG Y N,DUAN X Z,CUI J Q,et al.Opportunistic Network Routing Algorithm Based on Unsupervised Learning Model X-Means[J].Journal of Chinese Computer Systems,2025,46(7):1734-1744.
[16]SHARMA D K,DHURANDHER S K,AGARWAL D,et al.kROp:k-Means clustering based routing protocol for opportunistic networks[J].Journal of Ambient Intelligence and Humanized Computing,2019,10:1289-1306.
[17]VASHISHTH V,CHHABRA A,SHARMA D K.GMMR:AGaussian mixture model based unsupervised machine learning approach for optimal routing in opportunistic IoT networks[J].Computer Communications,2019,134:138-148.
[18]PILLAI R,RAO R,PRASAD C R,et al.DBSCAN-R:A Ma-chine Learning Approach for Routing in Opportunistic Networks[C]//2022 IEEE International Conference on Electronics,Computing and Communication Technologies(CONECCT).IEEE,2022:1-6.
[19]CONG L,DING H,XIE N,et al.Space delay-tolerant network routing algorithm based on node clustering and social attributes[J].Ad Hoc Networks,2024,155:103381.
[20]BEZDEK J C,EHRLICH R,FULL W.FCM:The fuzzy c-means clustering algorithm[J].Computers & Geosciences,1984,10(2/3):191-203.
[21]NAYAK J,NAIK B,BEHERA H S.Fuzzy C-means(FCM) clustering algorithm:a decade review from 2000 to 2014[C]//Computational Intelligence in Data Mining-Volume 2:Procee-dings of the International Conference on CIDM.Springer India,2015:133-149.
[22]LINDGREN A,DORIA A,SCHELÉN O.Probabilistic routingin intermittently connected networks[J].ACM SIGMOBILE Mobile Computing and Communications Review,2003,7(3):19-20.
[23]KERÄNEN A,OTT J,KÄRKKÄINEN T.The ONE simulator for DTN protocol evaluation[C]//Proceedings of the 2nd International Conference on Simulation Tools and Techniques.2009:1-10.
[1] HUANG Siyang, YAO Ye, ZHU Yian, HAI Duo, XIONG Zhihai. Anomaly Detection and Localization Technology for Gravity Wave Spectral Images Based onPre-trained Networks [J]. Computer Science, 2026, 53(5): 193-206.
[2] ZHU Feng, YE Zongguo, LI Peng, XU He. Transformer-based Domain Adaptation Method for IoT Traffic Intrusion Detection [J]. Computer Science, 2026, 53(3): 443-452.
[3] JIANG Rui, FAN Shuwen, WANG Xiaoming, XU Youyun. Clustering Algorithm Based on Improved SOM Model [J]. Computer Science, 2025, 52(8): 162-170.
[4] DING Zhengze, NIE Rencan, LI Jintao, SU Huaping, XU Hang. MTFuse:An Infrared and Visible Image Fusion Network Based on Mamba and Transformer [J]. Computer Science, 2025, 52(8): 188-194.
[5] AN Rui, LU Jin, YANG Jingjing. Deep Clustering Method Based on Dual-branch Wavelet Convolutional Autoencoder and DataAugmentation [J]. Computer Science, 2025, 52(4): 129-137.
[6] HE Liren, PENG Bo, CHI Mingmin. Unsupervised Multi-class Anomaly Detection Based on Prototype Reverse Distillation [J]. Computer Science, 2025, 52(2): 202-211.
[7] LI Tongliang, LI Qifeng, HOU Xia, CHEN Xiaoming, LI Zhoujun. Unsupervised Dialogue Topic Segmentation Method Based on Utterance Rewriting [J]. Computer Science, 2025, 52(12): 215-223.
[8] WEI Zihan, MA Rongkuan, LI Beibei, YANG Yahui, LI Zhuo, SONG Yunkai. Firmware Recovery Based Emulation and Testing Method for Industrial Gateway [J]. Computer Science, 2025, 52(12): 411-418.
[9] DENG Tao, DENG Ye. Review of Development and Application of Software Defect Prediction Techniques in IndustrialInternet of Things Environment [J]. Computer Science, 2025, 52(11A): 250200052-11.
[10] LIN Zukai, HOU Guojia, WANG Guodong, PAN Zhenkuan. Image Deraining Based on Union Attention Mechanism and Multi-stage Feature Extraction [J]. Computer Science, 2025, 52(11): 206-212.
[11] DING Xinyu, KONG Bing, CHEN Hongmei, BAO Chongming, ZHOU Lihua. Path-masked Autoencoder Guiding Unsupervised Attribute Graph Node Clustering [J]. Computer Science, 2025, 52(1): 160-169.
[12] CHEN Liang, LI Zhihua. Abnormal Traffic Detection Method for Multi-stage Attacks of Internet of Things Botnets [J]. Computer Science, 2024, 51(8): 379-386.
[13] LI Dongyang, NIE Rencan, PAN Linna, LI He. UMGN:An Infrared and Visible Image Fusion Network Based on Unsupervised Significance MaskGuidance [J]. Computer Science, 2024, 51(6A): 230600170-5.
[14] LOU Ren, HE Renqiang, ZHAO Sanyuan, HAO Xin, ZHOU Yueqi, WANG Xinyuan, LI Fangfang. Single Stage Unsupervised Visible-infrared Person Re-identification [J]. Computer Science, 2024, 51(6A): 230600138-7.
[15] ZANG Hongrui, YANG Tingting, LIU Hongbo, MA Kai. Study on Cryptographic Verification of Distributed Federated Learning for Internet of Things [J]. Computer Science, 2024, 51(6A): 230700217-5.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.