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An energy-efficient fuzzy-based scheme for unequal multihop clustering in wireless sensor networks

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Abstract

Currently, wireless sensor networks (WSNs) are providing practical solutions for various applications, including smart agriculture and healthcare, and have provided essential support by wirelessly connecting the numerous nodes or sensors that function in sensing systems needed for transmission to backends via multiple hops for data analysis. One key limitation of these sensors is the self-contained energy provided by the embedded battery due to their (tiny) size, (in) accessibility, and (low) cost constraints. Therefore, a key challenge is to efficiently control the energy consumption of the sensors, or in other words, to prolong the overall network lifetime of a large-scale sensor farm. Studies have worked toward optimizing energy in communication, and one promising approach focuses on clustering. In this approach, a cluster of sensors is formed, and its representatives, namely, a cluster head (CH) and cluster members (CMs), with the latter transmitting the sensing data within a short range to the CH. The CH then aggregates the data and forwards it to the base station (BS) using a multihop method. However, maintaining equal clustering regardless of key parameters such as distance and density potentially results in a shortened network lifetime. Thus, this study investigates the application of fuzzy logic (FL) to determine various parameters and membership functions and thereby obtain appropriate clustering criteria. We propose an FL-based clustering architecture consisting of four stages: competition radius (CR) determination, CH election, CM joining, and determination of selection criteria for the next CH (relaying). A performance analysis was conducted against state-of-the-art distributed clustering protocols, i.e., the multiobjective optimization fuzzy clustering algorithm (MOFCA), energy-efficient unequal clustering (EEUC), distributed unequal clustering using FL (DUCF), and the energy-aware unequal clustering fuzzy (EAUCF) scheme. The proposed method displayed promising performance in terms of network lifetime and energy usage.

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References

  • Afsar MM, Tayarani-N MH (2014) Clustering in sensor networks: a literature survey. J Netw Comput Appl 46:198–226

    Article  Google Scholar 

  • Bagci H, Yazici A (2013) An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Appl Soft Comput 13:741–1749

    Article  Google Scholar 

  • Balakrishnan B, Balachandran S (2017) FLECH: fuzzy logic based energy efficient clustering hierarchy for nonuniform wireless sensor networks. Wirel Commun Mob Comput 2017:1–13

    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 

  • Bhola J, Soni S, Cheema GK (2020) Genetic algorithm based optimized leach protocol for energy efficient wireless sensor networks. J Ambient Intell Humaniz Comput 11:1281–1288

    Article  Google Scholar 

  • Chen J, Shen H (2007) MELEACH an energy-efficient routing protocol for WSNs. China J Sens Actuators 9(4):2089–2094

    Google Scholar 

  • Currya R, Smith J (2016) A survey of optimization algorithms for wireless sensor network lifetime maximization. Comput Ind Eng 101:145–166

    Article  Google Scholar 

  • Devadevan V, Suresh S (2016) Energy efficient routing protocol in forest fire detection system. In: Proceedings of the 6th international conference on advanced computing (IACC), pp 618–622

  • Ehsan S, Hamdaoui B (2012) A survey on energy-efficient routing techniques with QoS assurances for wireless multimedia sensor networks. IEEE Commun Surv Tutor 14(2):265–278

    Article  Google Scholar 

  • Elappila M, Chinara S, Parhi DR (2018) Survivable path routing in WSN for IoT applications. Pervasive Mob Comput 43:49–63

    Article  Google Scholar 

  • Faheem M, Gungor VC (2018) Energy efficient and QoS–aware routing protocol for wireless sensor network–based smart grid applications in the context of industry 4.0. Appl Soft Comput 68:910–922

    Article  Google Scholar 

  • GNSS (2017) RTK and satellite positioning concepts

  • Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670

    Article  Google Scholar 

  • Kamilaris A, Pitsillides A (2016) mobile phone computing and the internet of things: a survey. IEEE Internet Things J 3(6):885–898

    Article  Google Scholar 

  • Kang J, Sohn I, Lee SH (2019) Enhanced message-passing based LEACH protocol for wireless sensor networks. Sensors 19:1–17

    Article  Google Scholar 

  • Kim J, Park S, Han Y, Chung T (2008) CHEF: cluster head election mechanism using fuzzy logic in wireless sensor networks. Proceedings of the 10th international conference on advanced communication technology (ICACT), pp 654–659

  • Kim YH, Ahn SC, Kwon WH (2000) Computational complexity of general fuzzy logic control and its simplification for a loop controller. Fuzzy Sets Syst 111(2):215–224

    Article  MathSciNet  Google Scholar 

  • Kuila P, Jana PK (2014) A novel differential evolution based clustering algorithm for wireless sensor networks. Appl Soft Comput 25:414–425

    Article  Google Scholar 

  • Latré B, Braem B, Moerman I, Blondia C, Demeester P (2011) A survey on wireless body area networks. Wirel Netw 17(1):1–18

    Article  Google Scholar 

  • Li CF, Ye M, Chen GH, and Wu J (2005) An energy–efficient unequal clustering mechanism for wireless sensor networks. In: Proceedings of the 2nd international conference on mobile ad–hoc and sensor systems (MASS), pp 604–611

  • Liu Y, Gao J, Jia Y, Zhu L (2008) A cluster maintenance algorithm based on LEACH-DCHS protocol. In: Proceedings of the international conference on networking, architecture, and storage, pp 165–166

  • Mirzaie M, Mazinani SM (2017) Adaptive MCFL: an adaptive multi–clustering algorithm using fuzzy logic in wireless sensor network. Comput Commun 111:56–67

    Article  Google Scholar 

  • Open Automation (2020) MicaZ Wireless Measurement System [online]. http://www.openautomation.net/uploadsproductos/micaz_datasheet.pdf. Accessed 25 Apr 2020

  • Perkins CE, Royer EM (2001) The ad hoc on–demand distance vector protocol. Ad hoc networking. Addison-Wesley Longman Publishing Co. Inc, Boston, pp 173–219

    Google Scholar 

  • Phoemphon S, So-In C, Leelathakul N (2018a) Fuzzy weighted centroid localization with virtual node approximation in wireless sensor networks. IEEE Internet Things J 5(6):4728–4752

    Article  Google Scholar 

  • Phoemphon S, So-In C, Nguyen TG (2018b) An enhanced wireless sensor network localization scheme for radio irregularity models using hybrid fuzzy deep extreme learning machines. Wirel Netw 24(3):799–819

    Article  Google Scholar 

  • Phoemphon S, So-In C, Niyato D (2018c) A hybrid model using fuzzy logic and an extreme learning machine with vector particle swarm optimization for wireless sensor network localization. Appl Soft Comput 65(4):101–120

    Article  Google Scholar 

  • Phoemphon S, So-In C, Leelathakul N (2020) A hybrid localization model using node segmentation and improved particle swarm optimization with obstacle-awareness for wireless sensor networks. Expert Syst Appl 143:113044

    Article  Google Scholar 

  • Rajaram V, Kumaratharan N (2020) Multi-hop optimized routing algorithm and load balanced fuzzy clustering in wireless sensor networks. J Ambient Intell Humaniz Comput

  • Razzaque MA, Milojevic-Jevric M, Palade A, Clarke S (2016) Middleware for internet of things: a survey. IEEE Internet Things J 3(1):70–95

    Article  Google Scholar 

  • Sert SA, Bagci H, Yazici A (2015) MOFCA: multi–objective fuzzy clustering algorithm for wireless sensor networks. Appl Soft Comput 30:151–165

    Article  Google Scholar 

  • Shankara T, Shanmugavelb S, Rajesha A (2016) Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks. Swarm Evolut Comput 30:1–10

    Article  Google Scholar 

  • Verginadis Y, Michalas A, Gouvas P, Schiefer G, Hübsch G, Paraskakis I (2017) PaaSword: a holistic data privacy and security by design framework for cloud services. J Grid Comput 15(2):219–234

    Article  Google Scholar 

  • Wanga J, Caoa Y, Lia B, Kimb H, Lee S (2017) Particle swarm optimization based clustering algorithm with mobile sink for WSNs. Future Gener Comput Syst 76:452–457

    Article  Google Scholar 

  • Xiangning F, Yulin S (2007) Improvement on LEACH protocol of wireless sensor network. In: Proceedings of the international conference on sensor technologies and applications, pp 260–264

  • Xu Y, Ding O, Qu R, Li K (2018) Hybrid multi–objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization. Appl Soft Comput 68:268–282

    Article  Google Scholar 

  • Ye M, Li C, Chen G, Wu J (2005) EECS: an energy efficient clustering scheme in wireless sensor networks. In: Proceedings of the 24th international conference on performance, computing and communications (IPCCC), pp 535–540

  • Zimmermann HJ (2001) Fuzzy set theory and its applications, 4th edn. Kluwer Academic Publishers Group, Boston

    Book  Google Scholar 

Download references

Acknowledgements

This research was supported by a grant from the National Science and Technology Development Agency (NSTDA), the Coordinating Center for Thai Government Science and Technology Scholarship Students (CSTS); the Research Affairs and Graduate School, Khon Kaen University, Thailand, through the Post-Doctoral Training Program under Grant 59257; and the Thailand Research Fund (TRF) under the International Research Network Program (IRN61W0006).

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Authors and Affiliations

  1. Applied Network Technology (ANT) Laboratory, Department of Computer Science, Faculty of Science, Khon Kaen University, Khon Kaen, 40002, Thailand

    Songyut Phoemphon, Chakchai So-In, Phet Aimtongkham & Tri Gia Nguyen

  2. Faculty of Information Technology, Duy Tan University, Da Nang, 550000, Vietnam

    Tri Gia Nguyen

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  1. Songyut Phoemphon
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  2. Chakchai So-In
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  3. Phet Aimtongkham
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  4. Tri Gia Nguyen
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Correspondence to Chakchai So-In.

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Phoemphon, S., So-In, C., Aimtongkham, P. et al. An energy-efficient fuzzy-based scheme for unequal multihop clustering in wireless sensor networks. J Ambient Intell Human Comput 12, 873–895 (2021). https://doi.org/10.1007/s12652-020-02090-z

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  • DOI: https://doi.org/10.1007/s12652-020-02090-z

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