Optimized clustering using soft computing approaches in wireless sensor networks: research dimensions and contributions | International Journal of System Assurance Engineering and Management Skip to main content

Advertisement

Springer Nature Link
Log in

Optimized clustering using soft computing approaches in wireless sensor networks: research dimensions and contributions

  • Review papers
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

Presently, Wireless Sensor Network (WSN) is considered as the most prominent technologies employed in commercial as well as in industrial sector. The WSN comprises of battery-operated nodes that are used to monitor the surroundings in order to keep record of the physical phenomenon like temperature, pressure, position, vibration, humidity, sound etc. These nodes can be utilized in several real-time application domains to perform different tasks like target tracking, home surveillance, pollution monitoring, structural monitoring etc. Depending on the type of nodes deployment and the application areas, WSNs can be exploited underground, underwater, terrestrial, wearable or environment embedded. Firstly, this paper categorizes research dimensions in wireless sensor networks primary and secondary research domains and the research contributions in those fields. Secondly, it discusses different soft computing-based clustering schemes from past two decades to deal with energy conservation issue in sensor networks. This paper provides a clear insight to the beginners of this field by covering literature review from 2008 to 2020.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Ab Aziz NAB, Mohemmed AW, Alias MY (2009) A wireless sensor network coverage optimization algorithm based on particle swarm optimization and Voronoi diagram. In: 2009 international conference on networking, sensing and control, IEEE, pp 602–607

  • Adnan MA, Razzaque MA, Abedin MA, Reza SS, Hussein MR (2016) A novel cuckoo search based clustering algorithm for wireless sensor networks. Advanced computer and communication engineering technology. Springer, pp 621–634

    Chapter  Google Scholar 

  • Agrawal D, Pandey S (2017) FLIHSBC: fuzzy logic and improved harmony search based clustering algorithm for wireless sensor networks to prolong the network lifetime. International conference on ubiquitous computing and ambient intelligence. Springer, pp 570–578

    Google Scholar 

  • Akkaya K, Younis M, Youssef W (2007) Positioning of base stations in wireless sensor networks. IEEE Commun Mag 45(4):96–102

    Article  Google Scholar 

  • Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38(4):393–422

    Article  Google Scholar 

  • Al-Karaki JN, Ul-Mustafa R, Kamal AE (2004) Data aggregation in wireless sensor networks-exact and approximate algorithms. In: 2004 workshop on high performance switching and routing, HPSR, IEEE, pp 241–245

  • Alla SB, Ezzati A, Mohsen A (2012) Gateway and cluster head election using fuzzy logic in heterogeneous wireless sensor networks. In: Multimedia computing and systems (ICMCS), international conference, IEEE, pp 761–766

  • Alwan H, Agarwal A, (2009) A survey on fault tolerant routing techniques in wireless sensor networks. In: 2009 third international conference on sensor technologies and applications, IEEE, pp 366–371

  • Anker T, Bickson D, Dolev D, Hod B (2008) Efficient clustering for improving network performance in wireless sensor networks. In: European conference on wireless sensor networks. Springer, pp 221–236

  • Azharuddin M, Jana PK (2016) Particle swarm optimization for maximizing lifetime of wireless sensor networks. Comput Electr Eng 51:26–42

    Article  Google Scholar 

  • Aziz NAA, Ibrahim Z, Aziz NHA, Aziz KA (2019) Simulated Kalman filter optimization algorithm for maximization of wireless sensor networks coverage. In: 2019 international conference on computer and information sciences (ICCIS), IEEE, pp 1–6

  • Basagni S, Carosi A, Petrioli C, Boukerche A (2008) Mobility in wireless sensor networks. Wiley Series on Parallel and Distributed Computing. Wiley, pp 267–305

    Google Scholar 

  • Crosby GV, Pissinou N, Gadze J (2006) A framework for trust-based cluster head election in wireless sensor networks, pp 13–22

  • Dagar M, Mahajan S (2013) Data aggregation in wireless sensor network: a survey. Int J Inf Comp Technol 3(3):167–174

    Google Scholar 

  • Dai H, Han R (2004) TSync: a lightweight bidirectional time synchronization service for wireless sensor networks. ACM SIGMOBILE Mobile Comp Commun Rev 8(1):125–139

    Article  Google Scholar 

  • Dashkova E, Gurtov A (2012) Survey on congestion control mechanisms for wireless sensor networks. Internet of things, smart spaces, and next generation networking. Springer, pp 75–85

  • De Souza LMS, Vogt H, Beigl M (2007) A survey on fault tolerance in wireless sensor networks. Fakultät für Informatik, Universität Karlsruhe, Interner Bericht

  • Dhand G, Tyagi SS (2016) Data aggregation techniques in WSN: survey. Procedia Comp Sci 92:378–384

    Article  Google Scholar 

  • Ekici E, Gu Y, Bozdag D (2006) Mobility-based communication in wireless sensor networks. IEEE Commun Mag 44(7):56–62

    Article  Google Scholar 

  • Ergen SC, Varaiya P (2010) TDMA scheduling algorithms for wireless sensor networks. Wireless Netw 16(4):985–997

    Article  Google Scholar 

  • Esmaeeli M, Ghahroudi SAH (2015) An energy- efficiency protocol in wireless sensor networks using theory of games and fuzzy logic. Int J Comput Appl 126(1):8–13

    Google Scholar 

  • Fang W, Wen X, Xu J, Zhu J (2019) CSDA: a novel cluster-based secure data aggregation scheme for WSNs. Clust Comput 22(3):5233–5244

    Article  Google Scholar 

  • Fanian F, Rafsanjani MK (2018) Memetic fuzzy clustering protocol for wireless sensor networks: shuffled frog leaping algorithm. Appl Soft Comput 71:568–590

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Gherbi C, Aliouat Z, Benmohammed M (2015) Distributed energy efficient adaptive clustering protocol with data gathering for large scale wireless sensor networks. In: 2015 12th international symposium on programming and systems (ISPS), IEEE, pp 1–7

  • Gupta GP, Jha S (2018) Integrated clustering and routing protocol for wireless sensor networks using Cuckoo and Harmony search based metaheuristic techniques. Eng Appl Artif Intell 68:101–109

    Article  Google Scholar 

  • Gupta G, Younis M (2003a) Fault- tolerant clustering of wireless sensor networks. In: 2003 IEEE wireless communications and networking, WCNC, 3, IEEE, pp 1579–1584

  • Gupta G, Younis M (2003b) Load-balanced clustering of wireless sensor networks. In: IEEE international conference on communications, ICC'03, 3, IEEE, pp 1848–1852

  • Hashemzehi R, Nourm R, Koroupi F (2013) Congestion in wireless sensor networks and mechanisms for controling congestion

  • Hoang DC, Yadav P, Kumar R, Panda SK (2010) A robust harmony search algorithm based clustering protocol for wireless sensor networks. In: Communications work-shops (ICC), IEEE international conference, IEEE, pp 1–5

  • Hossain A, Biswas PK, Chakrabarti S (2008) Sensing models and its impact on network coverage in wireless sensor network. In: 2008 IEEE region 10 and the third international conference on industrial and information systems, IEEE, pp 1–5

  • Jannu S, Jana PK (2014) Energy efficient grid based clustering and routing algorithms for wireless sensor networks. In: 2014 fourth international conference on communication systems and network technologies, IEEE, pp 63–68

  • Kazmi HSZ, Javaid N, Imran M, Outay F (2019) Congestion control in wireless sensor networks based on support vector machine, Grey Wolf optimization and differential evolution. In: 2019 Wireless Days (WD), IEEE, pp 1–8

  • Kim S, Ko JG, Yoon J, Lee H (2007) Multiple-objective metric for placing multiple base stations in wireless sensor networks. In: 2007 2nd international symposium on wireless pervasive computing, IEEE

  • Kong H, Yu B (2019) An improved method of WSN coverage based on enhanced PSO algorithm. In: 2019 IEEE 8th joint international information technology and artificial intelligence conference (ITAIC), IEEE, pp 1294–1297

  • Kuila P, Gupta SK, Jana PK (2013) A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm Evol Comput 12:48–56

    Article  Google Scholar 

  • Kuila P, Jana PK (2012) Energy efficient load- balanced clustering algorithm for wireless sensor networks. Procedia Technol 6:771–777

    Article  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 

  • Kumar M, Sahu A, Mitra P (2021) A comparison of different metaheuristics for the quadratic assignment problem in accelerated systems. Appl Soft Comput 100:106927

    Article  Google Scholar 

  • Li Z, Lei L (2009) Sensor node deployment in wireless sensor networks based on improved particle swarm optimization. In: 2009 international conference on applied superconductivity and electromagnetic devices, IEEE, pp 215–217

  • Liao Y, Qi H, Li W (2012) Load-balanced clustering algorithm with distributed self-organization for wireless sensor networks. IEEE Sens J 13(5):1498–1506

    Article  Google Scholar 

  • Lipare A, Edla DR, Kuppili V (2019) Energy efficient load balancing approach for avoiding energy hole problem in WSN using grey wolf optimizer with novel fitness function. Appl Soft Comput 84:105706

    Article  Google Scholar 

  • Liu X, He D (2014) Ant colony optimization with greedy migration mechanism for node deployment in wireless sensor networks. J Netw Comput Appl 39:310–318

    Article  Google Scholar 

  • Liu JL, Ravishankar CV (2011) LEACH-GA: genetic algorithm-based energy-efficient adaptive clustering protocol for wireless sensor networks. Int J Machine Learn Comput 1(1):79

    Article  Google Scholar 

  • Low CP, Fang C, Ng JM, Ang YH (2008) Efficient load-balanced clustering algorithms for wireless sensor networks. Comput Commun 31(4):750–759

    Article  Google Scholar 

  • Ma J, Lou W, Wu Y, Li XY, Chen G (2009) Energy efficient TDMA sleep scheduling in wireless sensor networks. In: IEEE INFOCOM, IEEE, pp 630–638

  • Moh’d Alia O (2018) A dynamic harmony search- based fuzzy clustering protocol for energy efficient wireless sensor networks. Annal Telecommun 73(5–6):353–365

    Article  Google Scholar 

  • Nandini SP, Patil PR (2010) Data aggregation in wireless sensor network. In: IEEE international conference on computational intelligence and computing research, pp 1–6

  • Nehra NK, Kumar M, Patel RB (2009) Neural network based energy efficient clustering and routing in wireless sensor networks. In: networks and communications, NETCOM'09, first international conference, IEEE, pp 34–39

  • Nicolaou A, Temene N, Sergiou C, Georgiou C, Vassiliou V (2019) Utilizing mobile nodes for congestion control in wireless sensor networks. Arxiv 1903:08989

    Google Scholar 

  • Olasupo TO, Otero CE (2018) A framework for optimizing the deployment of wireless sensor networks. IEEE Trans Netw Serv Manage 15(3):1105–1118

    Article  Google Scholar 

  • Pavani M, Rao PT (2019) Adaptive PSO with optimised firefly algorithms for secure cluster- based routing in wireless sensor networks. IET Wireless Sensor Syst 9(5):274–283

    Article  Google Scholar 

  • Peng L, Dong GY, Dai FF, Liu GP (2014) A new clustering algorithm based on aco and k-medoids optimization methods. IFAC Proc Vol 47(3):9727–9731

    Article  Google Scholar 

  • Poe WY, Schmitt JB (2009) Node deployment in large wireless sensor networks: coverage, energy consumption, and worst-case delay. In: Asian internet engineering conference, ACM, pp 77–84

  • Potthuri S, Shankar T, Rajesh A (2016) Lifetime improvement in wireless sensor net-works using hybrid differential evolution and simulated annealing (DESA). Ain Shams Eng J 9(4):655–663

    Article  Google Scholar 

  • Randhawa S, Jain S (2017) Data aggregation in wireless sensor networks: previous research, current status and future directions. Wireless Pers Commun 97(3):3355–3425

    Article  Google Scholar 

  • Rhee IK, Lee J, Kim J, Serpedin E, Wu YC (2009) Clock synchronization in wireless sensor networks: an overview. Sensors 9(1):56–85

    Article  Google Scholar 

  • Rhmann W, Pandey B, Ansari GA (2021) Software effort estimation using ensemble of hybrid search-based algorithms based on metaheuristic algorithms. Innov Syst Softw Eng. https://doi.org/10.1007/s11334-020-00377-0

    Article  Google Scholar 

  • Sahoo RR, Singh M, Sahoo BM, Majumder K, Ray S, Sarkar SK (2013a) A light weight trust based secure and energy efficient clustering in wireless sensor network: honey bee mating intelligence approach. Procedia Technol 10:515–523

    Article  Google Scholar 

  • Sahoo RR, Singh M, Sardar AR, Mohapatra S, Sarkar SK (2013b) TREE-CR: Trust based secure and energy efficient clustering in WSN. In emerging trends in computing, communication and nanotechnology (ICE-CCN), In: 2013 international conference, IEEE,pp 532–538

  • 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 

  • Shah SA, Nazir B, Khan IA (2017) Congestion control algorithms in wireless sensor networks: trends and opportunities. J King Saud Univ Comp Inf Sci 29(3):236–245

    Google Scholar 

  • Shanmukhi M, Ramanaiah OBV (2015) Cluster- based comb-needle model for energy-efficient data aggregation in wireless sensor networks. In: 2015 applications and innovations in mobile computing (AIMoC), IEEE, pp 42–47

  • Sharma A, Kansal P (2015) Energy efficient load- balanced clustering algorithm for Wireless Sensor Network. In: 2015 annual IEEE India conference (INDICON), IEEE, pp 1–6

  • Sharma R, Vashisht V, Singh U (2018) Node clustering in wireless sensor networks using fuzzy logic: survey. In: 2018 international conference on system modeling and advancement in research trends (SMART), IEEE, pp 66–72

  • Sharma R, Vashisht V, Singh U (2019a) EEFCM- DE: energy efficient clustering based on fuzzy c means and differential evolution algorithm in wireless sensor networks. IET Commun 13(8):996–1007

    Article  Google Scholar 

  • Sharma R, Vashisht V, Singh U (2019b) eeFFA/DE-a fuzzy based clustering algorithm using hybrid technique for wireless sensor networks. Int J Artif Intell Paradig. https://doi.org/10.1504/IJAIP.2019.10025734

    Article  Google Scholar 

  • Sharma R, Vashisht V, Singh U (2019c) Fuzzy modelling based energy aware clustering in wireless sensor networks using modified invasive weed optimization. J King Saud Univ Comp Inf Sci. https://doi.org/10.1016/j.jksuci.2019.11.014

    Article  Google Scholar 

  • Sharma R, Vashisht V, Singh AV, Kumar S (2019d) Analysis of existing clustering algorithms for wireless sensor networks. System performance and management analytics. Springer, Singapore, pp 259–277

    Chapter  Google Scholar 

  • Sharma R, Vashisht V, Singh U (2019e) Nature inspired algorithms for energy efficient clustering in wireless sensor network. In: 2019 9th international conference on cloud computing, data science and engineering (Confluence), IEEE, pp 365–370

  • Sharma R, Vashisht V, Singh U (2019f) Performance comparison of trust based clustering protocols for wireless sensor networks. In: 2019 6th international conference on computing for sustainable global development (INDIACom), pp 642–647

  • Sharma R, Vashisht V, Singh U (2020a) WOATCA: whale optimization algorithm based trusted scheme for cluster head selection in wireless sensor networks. IET Commun 14(8):1199–1208

    Article  Google Scholar 

  • Sharma R, Vashisht V, Singh U (2020b) Soft computing paradigms based clustering in wireless sensor networks: a survey. Advances in data sciences, security and applications. Springer, Singapore, pp 133–159

    Chapter  Google Scholar 

  • Sharma R, Vashisht V, Singh U (2020c) Metaheuristics-based energy efficient clustering in WSNs: challenges and research contributions. IET Wirel Sens Syst 10(6):253–264. https://doi.org/10.1049/iet-wss.2020.0102

    Article  Google Scholar 

  • Sharma R, Vashisht V, Singh U (2020d) eeTMFO/GA: a secure and energy efficient cluster head selection in wireless sensor networks. Telecommun Syst, http://link.springer.com/article/10.1007/s 11235–020–00654–0.

  • Sichitiu ML, Veerarittiphan C (2003) Simple, accurate time synchronization for wireless sensor networks. In: 2003 IEEE wireless communications networking, WCNC, 2 IEEE, pp 1266–1273

  • Silva R, Zinonos Z, Silva JS, Vassiliou V (2011) Mobility in WSNs for critical applications. In: 2011 IEEE symposium on computers and communications (ISCC), IEEE, pp 451–456

  • Song MAO, Zhao CL, (2011) Unequal clustering algorithm for WSN based on fuzzy logic and improved ACO. J China Univ Posts Telecommun 18(6):89–97

    Article  Google Scholar 

  • Sundararaman B, Buy U, Kshemkalyani AD (2004) Clock synchronization for wireless sensor networks: a survey. Ad Hoc Netw 3(3):281–323

    Article  Google Scholar 

  • Tabatabaei S, Omrani MR (2018) Proposing a method for controlling congestion in wireless sensor networks using comparative fuzzy logic. Wireless Pers Commun 100(4):1459–1476

    Article  Google Scholar 

  • Tolba FD, Ajib W, Obaid, A (2013) Distributed clustering algorithm for mobile wireless sensors networks. In: SENSORS, IEEE, pp 1–4

  • Veena KN, Kumar BV (2010) Dynamic clustering for Wireless Sensor Networks: a neuro- fuzzy technique approach. In: IEEE international conference on computational intelligence and computing re-search (ICCIC), IEEE, pp 1–6

  • Wu X, Chen G, Das SK (2008) Avoiding energy holes in wireless sensor networks with nonuniform node distribution. IEEE Trans Parallel Distrib Syst 19(5):710–720

    Article  Google Scholar 

  • Xu Y, Ji Y (2011) A clustering algorithm of wireless sensor networks based on PSO. In: International conference on artificial intelligence and computational intelli-gence. Springer, pp 187–194

  • Yuste-Delgado AJ, Cuevas-Martine JC, Triviño-Cabrera A (2012) EUDFC-enhanced unequal distributed type-2 fuzzy clustering algorithm. IEEE Sens J 19(12):4705–4716

    Article  Google Scholar 

  • Zadeh PH, Schlegel C, MacGregor MH (2012) Distributed optimal dynamic base station positioning in wireless sensor networks. Comput Netw 56(1):34–49

    Article  Google Scholar 

  • Zafar S, Bashir A, Chaudhry SA (2019) Mobility-aware hierarchical clustering in mobile wireless sensor networks. IEEE Access 7:20394–20403

    Article  Google Scholar 

  • Zhang H, Liu C (2012) A review on node deployment of wireless sensor network. Int J Comp Sci Issues (IJCSI) 9(6):378

    Google Scholar 

  • Zhang J, Lin Y, Zhou C, Ouyang J (2008) Optimal model for energy-efficient clustering in wireless sensor networks using global simulated annealing ge-netic algorithm. In: intelligent information technology application workshops, IITAW'08. international symposium, IEEE, pp 656–660

  • Zhang Y, Wang J, Han D, Wu H, Zhou R (2017) Fuzzy-logic based distributed energy-efficient clustering algorithm for wireless sensor networks. Sensors 17(7):1554

    Article  Google Scholar 

  • Zhang X, Chen H, Lin K, Wang Z, Yu J, Shi L (2019) RMTS: a robust clock synchronization scheme for wireless sensor networks. J Netw Comput Appl 135:1–10

    Article  Google Scholar 

Download references

Funding

No funding is provided.

Author information

Authors and Affiliations

  1. Amity Institute of Information Technology, AmityUniversity, Noida, India

    Richa Sharma

Authors
  1. Richa Sharma
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Richa Sharma.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharma, R. Optimized clustering using soft computing approaches in wireless sensor networks: research dimensions and contributions. Int J Syst Assur Eng Manag 13, 557–570 (2022). https://doi.org/10.1007/s13198-021-01346-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-021-01346-x

Keywords