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Progressive Fuzzy PSO-PID Congestion Control Algorithm for WSNs

  • Research Article-Computer Engineering and Computer Science
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Abstract

The wireless sensor network is a collection of diverse sensor nodes in which data is communicated via physical sensors and relayed using self-configured protocols. The network becomes congested because myriad nodes are involved in the sensing and data transmission. Congestion decreases the packet delivery ratio (PDR) virtue of significant packet drop and increases transmission delay. The lost packets are retransmitted at the cost of additional energy and time, which provokes the reduction in network performance and the extravagance of energy resources. Traditional techniques for congestion control are inflexible and lack adaptive capability. To overcome these constraints, this paper proposes a PFP-PID controller (Progressive Fuzzy Particle Swarm Optimization (PSO) Proportional Integral Derivative (PID)). The initial position of the reference particle is determined using fuzzy logic, which accelerates PSO convergence by escaping numerous initial iterations. Additionally, this hybrid mechanism based on PSO and fuzzy logic controller is employed to generate the ideal PID controller design with rapid convergence, resulting in an optimized transmission rate for sensor nodes. The PFP-PID is implemented to alleviate congestion by simulating it in network simulator (NS3) and comparing it to cuckoo fuzzy PID (CFPID), PSO-neural PID, fuzzy-PID, and traditional PID. The simulation results demonstrate that the PFP-PID is scalable and has outclassed existing mechanisms and significantly improved performance, increasing PDR by 4.89%, minimizing packet drop, delay, and active queue length deviation by 36.83%, 25.32%, and 10.84%, respectively, against CFPID.

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References

  1. Fei, Z.; Li, B.; Yang, S.; Xing, C.; Chen, H.; Hanzo, L.: A survey of multi-objective optimization in wireless sensor networks: metrics, algorithms, and open problems. IEEE Commun. Surv. Tutor. 19(1), 550–586 (2016)

    Article  Google Scholar 

  2. Bohloulzadeh, A.; Rajaei, M.: A survey on congestion control protocols in wireless sensor networks. Int. J. Wireless Inf. Netw. pp 1–20, (2020)

  3. Nikokheslat, H.D.; Ghaffari, A.: Protocol for controlling congestion in wireless sensor networks. Wirel. Pers. Commun. 95(3), 3233–3251 (2017)

    Article  Google Scholar 

  4. Ghaffari, A.: Congestion control mechanisms in wireless sensor networks: a survey. J. Netw. Comput. Appl. 52, 101–115 (2015). https://doi.org/10.1016/j.jnca.2015.03.002

    Article  Google Scholar 

  5. Arora, V.K.; Sharma, V.; Sachdeva, M.: On qos evaluation for zigbee incorporated wireless sensor network (IEEE 802.15. 4) using mobile sensor nodes. J. King Saud Univ.-Comput. Inf. Sci.(2018)

  6. Yang, X.; Chen, X.; Xia, R.; Qian, Z.: Wireless sensor network congestion control based on standard particle swarm optimization and single neuron pid. Sensors 18(4), 1265 (2018)

    Article  Google Scholar 

  7. Alaei, M.; Sabbagh, P.; Yazdanpanah, F.: A qos-aware congestion control mechanism for wireless multimedia sensor networks. Wirel. Netw. 25(7), 4173–4192 (2019)

    Article  Google Scholar 

  8. Mosavvar, I.; Ghaffari, A.: Data aggregation in wireless sensor networks using firefly algorithm. Wirel. Pers. Commun. 104(1), 307–324 (2019). https://doi.org/10.1007/s11277-018-6021-x

    Article  Google Scholar 

  9. Narawade, V.; Kolekar, U.D.: Acsro: adaptive cuckoo search based rate adjustment for optimized congestion avoidance and control in wireless sensor networks. Alex. Eng. J. 57(1), 131–145 (2018)

    Article  Google Scholar 

  10. Rezaee, A.A.; Pasandideh, F.: A fuzzy congestion control protocol based on active queue management in wireless sensor networks with medical applications. Wirel. Pers. Commun. 98(1), 815–842 (2018)

    Article  Google Scholar 

  11. Aimtongkham, P.; Heng, S.; Horkaew, P.; Nguyen, T.G.; So-In, C.: Fuzzy logic rate adjustment controls using a circuit breaker for persistent congestion in wireless sensor networks. Wirel. Netw. pp 1–25 (2020)

  12. Lin, L.; Shi, Y.; Chen, J.; Ali, S.: A novel fuzzy pid congestion control model based on cuckoo search in wsns. Sensors 20(7) (2020)

  13. Qu, S.; Zhao, L.; Chen, Y.; Mao, W.: A discrete-time sliding mode congestion controller for wireless sensor networks. Optik 225,(2021). https://doi.org/10.1016/j.ijleo.2020.165727

  14. Chiou, J.S.; Tsai, S.H.; Liu, M.T.: A pso-based adaptive fuzzy pid-controllers. Simul. Model. Pract. Theory 26, 49–59 (2012)

    Article  Google Scholar 

  15. Cheng, Y.S.; Liu, YH.; Hesse, HC.; Naumann, M.; Truong, CN.; Jossen, A.: A pso-optimized fuzzy logic control-based charging method for individual household battery storage systems within a community. Energies 11(2), (2018). https://doi.org/10.3390/en11020469

  16. Ahmadi, S.; Abdi, S.; Kakavand, M.: Maximum power point tracking of a proton exchange membrane fuel cell system using pso-pid controller. Int. J. Hydrog. Energy 42(32), 20430–20443 (2017). https://doi.org/10.1016/j.ijhydene.2017.06.208

    Article  Google Scholar 

  17. Kazmi, H.S.Z.; Javaid, N.; Awais, M.; Tahir, M.; Shim, S.; Zikria, Y.B.: Congestion avoidance and fault detection in wsns using data science techniques. Trans. Emerg. Telecommun. Technol. (2019). https://doi.org/10.1002/ett.3756

  18. Swain, S.K.; Nanda, P.K.: Priority based adaptive rate control in wireless sensor networks: a difference of differential approach. IEEE Access 7, 112435–112447 (2019)

    Article  Google Scholar 

  19. Qu, S.; Zhao, L.; Xiong, Z.: Cross-layer congestion control of wireless sensor networks based on fuzzy sliding mode control. Neural Comput. Appl. 32, 13505–13520 (2020). https://doi.org/10.1007/s00521-020-04758-1

    Article  Google Scholar 

  20. Gholipour, M.; Haghighat, A.T.; Meybodi, M.R.: Hop-by-hop congestion avoidance in wireless sensor networks based on genetic support vector machine. Neurocomputing 223, 63–76 (2017)

    Article  Google Scholar 

  21. Donta, P.K., Amgoth, T., Annavarapu, C.S.R., Congestion-aware data acquisition with q-learning for wireless sensor networks. In: 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), IEEE, pp 1–6 (2020)

  22. Danladi, S.B.; Ambursa, F.U.: Dyred: an enhanced random early detection based on a new adaptive congestion control. In: The 15th International Conference on Electronics, Computer and Computation (ICECCO), (2019).https://doi.org/10.1109/ICECCO48375.2019.9043276

  23. Parsavand, H.; Ghaffari, A.: Controlling congestion in wireless sensor networks through imperialist competitive algorithm. Wirel. Pers. Commun. 101, 1123–1142 (2018). https://doi.org/10.1007/s11277-018-5752-z

    Article  Google Scholar 

  24. Chen, T.S.; Kuo, C.H.; Wu, Z.X.: Adaptive load-aware congestion control protocol for wireless sensor networks. Wirel. Pers. Commun. 97(3), 3483–3502 (2017). https://doi.org/10.1007/s11277-017-4680-7

    Article  Google Scholar 

  25. Sun, Z.; Wang, P.; Vuran, M.C.; Al-Rodhaan, M.A.; Al-Dhelaan, A.M.; Akyildiz, I.F.: Bordersense: Border patrol through advanced wireless sensor networks. Ad Hoc Netw. 9(3), 468–477 (2011). https://doi.org/10.1016/j.adhoc.2010.09.008

    Article  Google Scholar 

  26. Berrahal, S.; Kim, J.H.; Rekhis, S.; Boudriga, N.; Wilkins, D.; Acevedo, J.: Border surveillance monitoring using quadcopter uav-aided wireless sensor networks. J. Commun. Softw. Syst. 12(1), 67–82 (2016)

    Article  Google Scholar 

  27. Rezaee, A.A.; Yaghmaee, M.H.; Rahmani, A.M.: Optimized congestion management protocol for healthcare wireless sensor networks. Wirel. Pers. Commun. 75(1), 11–34 (2014)

    Article  Google Scholar 

  28. Wei, Z.; Feng, L.; Han, J.; Xu, X.; Peng, H.: A reliable transport protocol with prediction mechanism for urgent information in wireless sensor networks. Int. J. Distrib. Sens. Netw. 9(12), 221–235 (2013)

    Article  Google Scholar 

  29. Daanoune, I.; Baghdad, A.; Balllouk, A.: A comparative study between aco-based protocols and pso-based protocols in wsn. In: The 7th Mediterranean Congress of Telecommunications (CMT), pp 1–4 (2019)

  30. Mirjalili, S.; Hashim, SZM.: A new hybrid psogsa algorithm for function optimization. In: 2010 International Conference on Computer and Information Application, pp 374–377 (2010)

  31. Weise, T.; Zapf, M.; Chiong, R.; Nebro, A.J.: Why is optimization difficult?, Springer, pp 1–50 (2009)

  32. Aimtongkham, P.; Nguyen, T.G.; So-In, C.: Congestion control and prediction schemes using fuzzy logic system with adaptive membership function in wireless sensor networks. Wirel. Commun. Mobile Comput. 2018,(2018). https://doi.org/10.1155/2018/6421717

  33. Jain, H.; Deb, K.: An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part ii: handling constraints and extending to an adaptive approach. IEEE Trans. Evolut. Comput. 18(4), 602–622 (2013)

    Article  Google Scholar 

  34. Jan, S.R.U.; Jan, M.A.; Khan, R.; Ullah, H.; Alam, M.; Usman, M.: An energy-efficient and congestion control data-driven approach for cluster-based sensor network. Mobile Netw. Appl. 24, 1295–1305 (2019). https://doi.org/10.1007/s11036-018-1169-x

    Article  Google Scholar 

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Bhatti, K.A., Asghar, S. Progressive Fuzzy PSO-PID Congestion Control Algorithm for WSNs. Arab J Sci Eng 48, 1157–1172 (2023). https://doi.org/10.1007/s13369-022-06701-z

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  • DOI: https://doi.org/10.1007/s13369-022-06701-z

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