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MOTCO: Multi-objective Taylor Crow Optimization Algorithm for Cluster Head Selection in Energy Aware Wireless Sensor Network

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Abstract

Wireless Sensor Networks (WSNs) are widely employed for sensing and collecting the data of an environment from a remote area and are used for a variety of engineering applications. The data transfer between the nodes is enabled using the routing protocols that face huge challenge in terms of energy. With energy as an effective constraint, the energy-aware routing is proposed using the optimal cluster head selection procedure. The proposed method of selecting the optimal cluster head is performed using Multi-Objective Taylor Crow Optimization (MOTCO) algorithm that is the combination of the Taylor series and the Crow Search Algorithm (CSA). The proposed objective function is based on the distance between the nodes in the cluster, energy of the nodes, traffic density of the cluster, and the delay in transmitting the data packets. The designed objective function is tuned for a minimum value and the cluster head corresponding to the minimum value of the objective function becomes the optimal cluster head. The simulation is carried out by considering 50 nodes and 100 nodes in the WSN environment for analysis. The analysis proves that the proposed MOTCO outperforms the existing methods by attaining the network energy and throughput at a maximum value of 10% and 65% at the 2000th round.

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References

  1. Mahajana S, Malhotrab J, Sharmac S (2014) An energy balanced QoS based cluster head selection strategy for WSN. Egyptian Informatics Journal 15(3):189–199

    Article  Google Scholar 

  2. Kumar R, Kumar D (2016) Multi-objective fractional artificial bee colony algorithm to energy aware routing protocol in wireless sensor network. Wirel Netw 22(5):1461–1474

    Article  Google Scholar 

  3. shende D (2018) Automation of dry-wet waste collection to support Swachh Bharat Abhiyan and its monitoring over IOT enabled WSN. Int J ComputSci Eng 6(6):477–479

    Google Scholar 

  4. Omar M, Yahiaoui S, Bouabdallah A (2016) Reliable and energy aware query-driven routing protocol for wireless sensor networks. Ann Telecommun 71(1–2):73–85

    Article  Google Scholar 

  5. Chavan A, Khiani S (2016) Securely energy aware routing in WSN with efficient clustering. In: Proceedings of the 2016 international conference on advanced communication control and computing technologies (ICACCCT). IEEE, Ramanathapuram, pp 624–628

  6. pande NS, Udupi V (2017) Fractional lion optimization for cluster head-based routing protocol in wireless sensor network. Journal of the Franklin Institute 354(11):4457–4480

    Article  MathSciNet  Google Scholar 

  7. Purkait R, Tripathi S (2017) Energy aware fuzzy based multi-hop routing protocol using unequal clustering. Wirel Pers Commun 94(3):809–833

    Article  Google Scholar 

  8. Wei G, Ling Y, Guo B, Xiao B, Vasilakos AV (2011) Prediction-based data aggregation in wireless sensor networks: combining grey model and Kalman filter. Comput Commun 34(6):793–802

    Article  Google Scholar 

  9. Haseeb K, Bakar KA, Abdullah AH, Darwish T (2017) Adaptive energy aware cluster-based routing protocol for wireless sensor networks. Wirel Netw 23(6):1953–1966

    Article  Google Scholar 

  10. Chi Y, Chang H (2013) An energy-aware grid-based routing scheme for wireless sensor networks. Telecommun Syst 54(4):405–415

    Article  Google Scholar 

  11. Kang SH, Nguyen T (2012) Distance based thresholds for cluster head selection in wireless sensor networks. IEEE Commun Lett 16(9):1396–1399

    Article  Google Scholar 

  12. Gautam N, Pyun JY (2010) Distance aware intelligent clustering protocol for wireless sensor networks. J Commun Netw 12(2):122–129

    Article  Google Scholar 

  13. Hammoudeh M, Newman R (2015) Adaptive routing in wireless sensor networks: QoS optimisation for enhanced application performance. Information Fusion 22:3–15

    Article  Google Scholar 

  14. Kong L, Pan JS, Snášel V, Tsai PW, Sung T (2018) An energy-aware routing protocol for wireless sensor network based on genetic algorithm. Telecommun Syst 67(3):451–463

    Article  Google Scholar 

  15. Tao M, Yang DLJ (2012) An adaptive energy-aware multi-path routing protocol with load balance for wireless sensor networks. Wirel Pers Commun 63(4):823–846

    Article  Google Scholar 

  16. Arya R, Sharma SC (2018) Energy optimization of energy aware routing protocol and bandwidth assessment for wireless sensor network. Int J Syst Assur Eng Manag 9(3):612–619

    Article  Google Scholar 

  17. Ya L, Pengjun W, Rong L, Huazhong Y, Wei L (2014) Reliable energy-aware routing protocol for heterogeneous WSN based on beaconing. In: Proceedings of the 16th international conference on advanced communication technology. IEEE, Pyeongchang, pp 109–112

  18. Khan NM, Khalid Z, Ahmed G (2009) GRAdient cost establishment (GRACE) for an energy-aware routing in wireless sensor networks. EURASIP J Wirel Commun Netw

  19. 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 

  20. Fersi G, Jemaa WLMB (2016) CLEVER: cluster-based energy-aware virtual ring routing in randomly deployed wireless sensor networks. Peer-to-Peer Networking and Applications 9(4):640–655

    Article  Google Scholar 

  21. Karlekar NP, Gomathi N (2017) Kronecker product and bat algorithm-based coefficient generation for privacy protection on cloud. Int J Model Simul Sci Comput 8(3):94–107

  22. Ranjan NM, Prasad RS (2018) LFNN: lion fuzzy neural network-based evolutionary model for text classification using context and sense based features. Appl Soft Comput 71:994–1008

    Article  Google Scholar 

  23. Thomas R, Rangachar MJS (2016) Integrating GWTM and BAT algorithm for face recognition in low-resolution images. The Imaging Science Journal 64(8):441–452

    Article  Google Scholar 

  24. Menaga D, Revathi S (2018) Least lion optimisation algorithm (LLOA) based secret key generation for privacy preserving association rule hiding. IET Inf Secur 12(4):332–340

    Article  Google Scholar 

  25. Shankar T, Shanmugavel S, Rajesh A (2016) Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks. Swarm Evol Comput 30:1–10

    Article  Google Scholar 

  26. Potthuri S, Shankar T, Rajesh A (2018) Lifetime improvement in wireless sensor networks using hybrid differential evolution and simulated annealing (DESA). Ain Shams Eng J 9(4):655–663

    Article  Google Scholar 

  27. Elshrkawey M, Elsherif SM, Wahed ME (2018) An enhancement approach for reducing the energy consumption in wireless sensor networks. Journal of King Saud University – Computer and Information Sciences 30(2):259–267

    Article  Google Scholar 

  28. Kang J, Zhang Y, Nath B (2005) Accurate and energy-efficient congestion level measurement in ad hoc networks. In: IEEE international conference on wireless communications and networking conference. IEEE, New Orleans

  29. Mangai SA, Sankar BR, Alagarsamy K (2014) Taylor series prediction of time series data with error propagated by artificial neural network. Int J Comput Appl 89(1):41–47

    Google Scholar 

  30. Askarzadeh A (2016) A novel meta heuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12

    Article  Google Scholar 

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Authors and Affiliations

  1. Indira Gandhi College of Engineering & Technology for Women, Chengalpattu, Kancheepuram, Tamil Nadu, India

    Jacob John

  2. Indira Gandhi College of Engineering & Technology for Women, Chengalpattu, Kancheepuram, Tamil Nadu, India

    Paul Rodrigues

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  1. Jacob John
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  2. Paul Rodrigues
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Correspondence to Jacob John.

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John, J., Rodrigues, P. MOTCO: Multi-objective Taylor Crow Optimization Algorithm for Cluster Head Selection in Energy Aware Wireless Sensor Network. Mobile Netw Appl 24, 1509–1525 (2019). https://doi.org/10.1007/s11036-019-01271-1

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  • DOI: https://doi.org/10.1007/s11036-019-01271-1

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