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Optimized fuzzy clustering using moth-flame optimization algorithm in wireless sensor networks

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Abstract

Energy consumption is one of the main concerns in wireless sensor networks (WSNs). In this context, congestion is one of the problems which by dropping the data packets, increases the energy consumption of WSN, and reduces its lifetime. In this paper, we deal with these problems and present a distributed fuzzy clustering scheme that uses two Fuzzy Logic Controllers (FLCs) to organize WSN into some clusters. Besides, in this scheme, we consider multiple mobile sink nodes and provide another FLC for fuzzy sink selection used by cluster heads (CHs). In this scheme, CHs cooperate in multi-hop routing of data packets to minimize the energy consumption of WSN. However, in the data routing step, congestion may happen in the data forwarding nodes. In this scheme, we deal with the congestion problem by proposing a distance-based version of the Random Early Detection (RED) congestion control method to drop the data packets more intelligently. Besides, to increase the effectiveness of the proposed FLCs, we tune them using the Moth-Flame Optimization algorithm and minimize their rules. Simulation results indicate the effectiveness of the proposed clustering and distance-based RED congestion control method in improving the WSN’s lifespan, reducing the number of retransmissions, and mitigating the percentage of packet loss.

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References

  • Aghdam SM, Khansari M, Rabiee HR, Salehi M (2014) WCCP: A congestion control protocol for wireless multimedia communication in sensor networks. Ad Hoc Netw 13:516–534

    Article  Google Scholar 

  • Ahmed AM, Paulus R (2017) Congestion detection technique for multipath routing and load balancing in WSN. Wireless Netw 23:881–888

    Article  Google Scholar 

  • Arjunan S, Sujatha P (2018) Lifetime maximization of wireless sensor network using fuzzy based unequal clustering and ACO based routing hybrid protocol. Appl Intell 48:2229–2246

    Article  Google Scholar 

  • Bagci H, Yazici A (2013) An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Appl Soft Comput 13:1741–1749

    Article  Google Scholar 

  • Baranidharan B, Santhi B (2016) DUCF: Distributed load balancing unequal clustering in wireless sensor networks using fuzzy approach. Appl Soft Comput 40:495–506

    Article  Google Scholar 

  • Beulah Jayakumari R, Jawahar Senthilkumar V (2015) Priority based congestion control dynamic clustering protocol in mobile wireless sensor networks. The Sci World J vol 2015

  • Chen W, Niu Y, Zou Y (2016) Congestion control and energy-balanced scheme based on the hierarchy for WSNs. IET Wireless Sensor Syst 7:1–8

    Google Scholar 

  • Ding W, Tang L, Ji S (2016) Optimizing routing based on congestion control for wireless sensor networks. Wireless Netw 22:915–925

    Article  Google Scholar 

  • El Alami H, Najid A (2018) MS-routing-G i: routing technique to minimise energy consumption and packet loss in WSNs with mobile sink. IET Netw 7:422–428

    Article  Google Scholar 

  • El Alami H, Najid A (2019) ECH: An enhanced clustering hierarchy approach to maximize lifetime of wireless sensor networks. IEEE Access 7:107142–107153

    Article  Google Scholar 

  • Fang W-W, Chen J-M, Shu L, Chu T-S, Qian D-P (2010) Congestion avoidance, detection and alleviation in wireless sensor networks. J Zhejiang Univ Sci C 11:63–73

    Article  Google Scholar 

  • Feng C-W, Huang L-F, Xu C, Chang Y-C (2015) Congestion control scheme performance analysis based on nonlinear RED. IEEE Syst J 11:2247–2254

    Article  Google Scholar 

  • Gajjar S, Sarkar M, Dasgupta K (2016) FAMACROW: Fuzzy and ant colony optimization based combined mac, routing, and unequal clustering cross-layer protocol for wireless sensor networks. Appl Soft Comput 43:235–247

    Article  Google Scholar 

  • Ghaffari A (2015) Congestion control mechanisms in Wireless Sensor networks: a survey. J Netw Comput Appl 52:101–115

    Article  Google Scholar 

  • Gherbi C, Aliouat Z, Benmohammed M (2016) An adaptive clustering approach to dynamic load balancing and energy efficiency in wireless sensor networks. Energy 114:647–662

    Article  Google Scholar 

  • Godbole V (2012) FCA-an approach on LEACH protocol of wireless sensor networks using fuzzy logic. Int J Comput Commun Netw (IJCCN) 3:1–13

    Google Scholar 

  • Heinzelman WB (2000) Application-specific protocol architectures for wireless networks. Massachusetts Inst Technol

  • Isik S, Donmez MY, Ersoy C (2012) Multi-sink load balanced forwarding with a multi-criteria fuzzy sink selection for video sensor networks. Comput Netw 56:615–627

    Article  Google Scholar 

  • Jain TK, Saini DS, Bhooshan SV (2015) Lifetime optimization of a multiple sink wireless sensor network through energy balancing. J Sensors, vol 2015s

  • Lee J-S, Cheng W-L (2012) Fuzzy-logic-based clustering approach for wireless sensor networks using energy predication. IEEE Sens J 12:2891–2897

    Article  Google Scholar 

  • Lee J-S, Kao T-Y (2016) An improved three-layer low-energy adaptive clustering hierarchy for wireless sensor networks. IEEE Int Things J 3:951–958

    Article  Google Scholar 

  • Lee J-S, Teng C-L (2017) An enhanced hierarchical clustering approach for mobile sensor networks using fuzzy inference systems. IEEE Int Things J 4:1095–1103

    Article  Google Scholar 

  • Lin L, Shi Y, Chen J, Ali S (2020) A Novel Fuzzy PID Congestion Control Model Based on Cuckoo Search in WSNs. Sensors 20:1862

    Article  Google Scholar 

  • Logambigai R, Kannan A (2016) Fuzzy logic based unequal clustering for wireless sensor networks. Wireless Netw 22:945–957

    Article  Google Scholar 

  • Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7:1–13

    Article  MATH  Google Scholar 

  • Mao S, Zhao C, Zhou Z, Ye Y (2013) An improved fuzzy unequal clustering algorithm for wireless sensor network. Mobile Netw Appl 18:206–214

    Article  Google Scholar 

  • Masdari M, Ahmadzadeh S (2017) A survey and taxonomy of the authentication schemes in telecare medicine information systems. J Netw Comput Appl 87:1–19

    Article  Google Scholar 

  • Masdari M, Jalali M (2016) A survey and taxonomy of DoS attacks in cloud computing. Secur Commun Netw

  • Masdari M, Khezri H (2020) A survey and taxonomy of the fuzzy signature-based intrusion detection systems. Appl Soft Comput, p 106301

  • Masdari M, Naghiloo F (2017) Fuzzy logic-based sink selection and load balancing in multi-sink wireless sensor networks. Wireless Pers Commun 97:2713–2739

    Article  Google Scholar 

  • Masdari M, Bazarchi SM, Bidaki M (2013) Analysis of secure LEACH-based clustering protocols in wireless sensor networks. J Netw Comput Appl 36:1243–1260

    Article  Google Scholar 

  • Masdari M, Nabavi SS, Ahmadi V (2016a) An overview of virtual machine placement schemes in cloud computing. J Netw Comput Appl 66:106–127

    Article  Google Scholar 

  • Masdari M, ValiKardan S, Shahi Z, Azar SI (2016b) Towards workflow scheduling in cloud computing: a comprehensive analysis. J Netw Comput Appl 66:64–82

    Article  Google Scholar 

  • Masdari M, Salehi F, Jalali M, Bidaki M (2017) A survey of PSO-based scheduling algorithms in cloud computing. J Netw Syst Manag 25:1–37

    Article  Google Scholar 

  • Masdari M, Barshande S, Ozdemir S (2019) CDABC: chaotic discrete artificial bee colony algorithm for multi-level clustering in large-scale WSNs. J Supercomput 75:1–35

    Article  Google Scholar 

  • Mhemed R, Aslam N, Phillips W, Comeau F (2012) An energy efficient fuzzy logic cluster formation protocol in wireless sensor networks. Proc Comput Sci 10:255–262

    Article  Google Scholar 

  • Nayak P, Devulapalli A (2016) A fuzzy logic-based clustering algorithm for WSN to extend the network lifetime. IEEE Sens J 16:137–144

    Article  Google Scholar 

  • Nguyen T-T, Shieh C-S, Dao T-K, Wu J-S, Hu W-C (2013) Prolonging of the network lifetime of WSN using fuzzy clustering topology. In: 2013 Second international conference on robot, vision and signal processing, 2013, pp 13–16s

  • Peng Q, Enqing D, Juan X, Xing L, Wei L, Wentao C (2014) Multipath routing protocol based on congestion control mechanism implemented by cross-layer design concept for WSN. In: 2014 IEEE 17th international conference on computational science and engineering, 2014, pp 378–384.

  • Rajan AU, Kasmir Raja A, Jeyasekar A, Lattanze AJ (2015) Energy-efficient predictive congestion control for wireless sensor networks. IET Wireless Sensor Syst 5:115–123

    Article  Google Scholar 

  • Rezaee AA, Pasandideh F (2018) A fuzzy congestion control protocol based on active queue management in wireless sensor networks with medical applications. Wireless Pers Commun 98:815–842

    Article  Google Scholar 

  • Santos AC, Duhamel C, Belisário LS (2016) Heuristics for designing multi-sink clustered WSN topologies. Eng Appl Artif Intell 50:20–31

    Article  Google Scholar 

  • Silva AP, Burleigh S, Hirata CM, Obraczka K (2015) A survey on congestion control for delay and disruption tolerant networks. Ad Hoc Netw 25:480–494

    Article  Google Scholar 

  • Singh AK, Purohit N, Varma S (2013) Fuzzy logic based clustering in wireless sensor networks: a survey. Int J Electron 100:126–141

    Article  Google Scholar 

  • Singh S, Singh S, Banga VK (2020) Design of fuzzy logic system framework using evolutionary techniques. Soft Comput 24:4455–4468

    Article  Google Scholar 

  • Soro S, Heinzelman WB (2005) Prolonging the lifetime of wireless sensor networks via unequal clustering. In: Proceedings of 19th IEEE international parallel and distributed processing symposium, 2005

  • Tam NT, Hai DT (2018) Improving lifetime and network connections of 3D wireless sensor networks based on fuzzy clustering and particle swarm optimization. Wireless Netw 24:1477–1490

    Article  Google Scholar 

  • Wang J, Gao Y, Liu W, Sangaiah AK, Kim H-J (2019) An improved routing schema with special clustering using PSO algorithm for heterogeneous wireless sensor network. Sensors 19:671

    Article  Google Scholar 

  • Xu Y, Qi H, Xu T, Hua Q, Yin H, Hua G (2019) Queue models for wireless sensor networks based on random early detection. Peer-to-Peer Netw Appl 12:1539–1549

    Article  Google Scholar 

  • Yang L, Lu Y, Zhong Y, Wu X, Yang SX (2016) A multi-hop energy neutral clustering algorithm for maximizing network information gathering in energy harvesting wireless sensor networks. Sensors 16:26

    Article  Google Scholar 

  • Younis O, Fahmy S (2004) HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans Mob Comput 3:366–379

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Artificial Intelligence Laboratory, Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, 700000, Vietnam

    Cuong Trinh

  2. Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, 700000, Vietnam

    Bao Huynh

  3. Department of Computer Engineering, Neyshabur Branch, Islamic Azad University, Tehran, Iran

    Moazam Bidaki

  4. Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, 64002, Yunlin, Taiwan

    Amir Masoud Rahmani

  5. Pattern Recognition and Machine Learning Lab, Gachon University, 1342 Seongnamdaero, Sujeonggu, Seongnam, 13120, Republic of Korea

    Mehdi Hosseinzadeh

  6. Department of Computer Engineering, Urmia Branch, Islamic Azad University, Tehran, Iran

    Mohammad Masdari

Authors
  1. Cuong Trinh
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  2. Bao Huynh
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  3. Moazam Bidaki
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  4. Amir Masoud Rahmani
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  5. Mehdi Hosseinzadeh
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  6. Mohammad Masdari
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Corresponding author

Correspondence to Mehdi Hosseinzadeh.

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Trinh, C., Huynh, B., Bidaki, M. et al. Optimized fuzzy clustering using moth-flame optimization algorithm in wireless sensor networks. Artif Intell Rev 55, 1915–1945 (2022). https://doi.org/10.1007/s10462-021-09957-3

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