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Multi-objective fuzzy krill herd congestion control algorithm for WSN

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Abstract

Wireless Sensor Network (WSN) consists of hundreds of devices with limited resources that collect, analyze, and transmit data to a base station. The carry-send nature and inconsistent transmission rate caused network congestion. Congestion incites decreased throughput, increased packet loss, and energy depletion. The existing congestion control strategies address congestion problems but still lack performance and quality of service issues. The optimal source transmission rate helps to alleviate congestion. The article proposes a Multi-objective Fuzzy Krill Herd Algorithm (MFKHA) control network congestion by optimizing the source sending rate. This innovative multi-objective outflow rate optimization mechanism improves network performance by designing a unique probability-based data differentiation mechanism coupled with an optimal source outflow rate optimization. To minimize network congestion by achieving fast convergence, this optimization algorithm incorporates the five objectives (congestion level, inflow rate, outflow rate, bandwidth, and queue length). To validate the performance of the proposed MFKHA algorithm, extensive simulations are carried out using MATLAB. Moreover, the proposed MFKHA algorithm is compared to those of cutting-edge meta-heuristic algorithms such as ECA-HA, ACSRO, and PSOGSA. The simulation result shows that the proposed MFKHA outperformed all counterparts and specifically improved the sending rate, throughput, and fairness and friendliness index. Furthermore, it has also reduced packet loss, delay, queue size, energy usage, and congestion against ECA-HA.

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References

  1. Ahmed MM, Houssein EH, Hassanien AE, Taha A, Hassanien E (2019) Maximizing lifetime of large-scale wireless sensor networks using multi-objective whale optimization algorithm. Telecommun Syst 72(2):243–259. https://doi.org/10.1007/s11235-019-00559-7

    Article  Google Scholar 

  2. Aimtongkham P, Nguyen TG, So-In C (2018) Congestion control and prediction schemes using fuzzy logic system with adaptive membership function in wireless sensor networks. Wirel Commun Mob Comput 2018:1–19. https://doi.org/10.1155/2018/6421717

    Article  Google Scholar 

  3. Aimtongkham P, Heng S, Horkaew P, Nguyen TG, So-In C (2020) Fuzzy logic rate adjustment controls using a circuit breaker for persistent congestion in wireless sensor networks. Wireless Netw 26: 3603–3627. https://doi.org/10.1007/s11276-020-02289-0

    Article  Google Scholar 

  4. Alaei M, Sabbagh P, Yazdanpanah F (2019) A qos-aware congestion control mechanism for wireless multimedia sensor networks. Wireless Netw 25 (7):4173–4192. https://doi.org/10.1007/s11276-018-1738-8

    Article  Google Scholar 

  5. Alipio MI, Tiglao NMC (2018) Rt-cacc: A reliable transport with cache-aware congestion control protocol in wireless sensor networks. IEEE Trans Wireless Commun 17(7):4607–4619. https://doi.org/10.1109/TWC.2018.2827986

    Article  Google Scholar 

  6. Almalawi A, Khan AI, Alsolami F, Abushark YB, Alfakeeh AS, Mekuriyaw WD (2022) Analysis of the exploration of security and privacy for healthcare management using artificial intelligence: Saudi hospitals. Comput Intell Neurosci 2022:1–10. https://doi.org/10.1155/2022/4048197

    Article  Google Scholar 

  7. Amer H, Al-Kashoash H, Khami MJ, Mayfield M, Mihaylova L (2020) Non-cooperative game based congestion control for data rate optimization in vehicular ad hoc networks. Ad Hoc Netw 107:1–13. https://doi.org/10.1016/j.adhoc.2020.102181

    Article  Google Scholar 

  8. Beitelspacher S, Mubashir M, Besher K M, Ali M Z (2020) Prioritizing health care data traffic in a congested iot cloud network. In: IEEE Wireless Communications and Networking Conference Workshops, WCNCW, IEEE. https://doi.org/10.1109/WCNCW48565.2020.9124867

  9. Bhatti KA, Asghar S (2022) Progressive fuzzy pso-pid congestion control algorithm for wsns. Arabian Journal for Science and Engineering, https://doi.org/10.1007/s13369-022-06701-z

  10. Bohloulzadeh A, Rajaei M (2020) A survey on congestion control protocols in wireless sensor networks. Int J Wireless Inf Networks 27:365–384. https://doi.org/10.1007/s10776-020-00479-3

    Article  Google Scholar 

  11. Cameron J, Kaye ME, Scheme E (2020) Dynamic prioritization of surveillance video data in real-time automated detection systems. Expert Syst Appl 161:1–21. https://doi.org/10.1016/j.eswa.2020.113672

    Article  Google Scholar 

  12. Chaturvedi S, Pragya P, Verma HK (2015) Comparative analysis of particle swarm optimization, genetic algorithm and krill herd algorithm. In: 2015 International Conference on Computer, Communication and Control (IC4), pp 1–7, https://doi.org/10.1109/IC4.2015.7375552

  13. Daanoune I, Baghdad A, Balllouk A (2019) A comparative study between aco-based protocols and pso-based protocols in wsn. In: The 7th Mediterranean Congress of Telecommunications (CMT), pp 1–4, https://doi.org/10.1109/CMT.2019.8931320

  14. Dev K, Maddikunta PKR, Gadekallu TR, Bhattacharya S, Hegde P, Singh S (2022) Energy optimization for green communication in iot using harris hawks optimization. IEEE Transactions on Green Communications and Networking 6(2):685–694. https://doi.org/10.1109/TGCN.2022.3143991

    Article  Google Scholar 

  15. Fei Z, Li B, Yang S, **ng C, Chen H, Hanzo L (2016) A survey of multi-objective optimization in wireless sensor networks: Metrics, algorithms, and open problems. IEEE Communications Surveys and Tutorials 19(1):550–586. https://doi.org/10.1109/COMST.2016.2610578

    Article  Google Scholar 

  16. Grover A, Kumar RM, Angurala M, Singh M, Sheetal A, Maheswar R (2022) Rate aware congestion control mechanism for wireless sensor networks. Alex Eng J 61(6):4765–4777. https://doi.org/10.1016/j.aej.2021.10.032

    Article  Google Scholar 

  17. Hamidouche R, Aliouat Z, Ari AAA, Gueroui M (2019) An efficient clustering strategy avoiding buffer overflow in iot sensors: a bio-inspired based approach. IEEE Access 7:156733–156751. https://doi.org/10.1109/ACCESS.2019.2943546

    Article  Google Scholar 

  18. Khan AI, Alghamdi ASAM, Alsolami FJ, Abushark YB, Almalawi A, Ali AM, Agrawal A, Kumar R, Khan RA (2022a) Integrating blockchain technology into healthcare through an intelligent computing technique. Computers, Materials and Continua 70(2):2835–2860. https://doi.org/10.32604/cmc.2022.020342

  19. Khan AI, Alsolami F, Alqurashi F, Abushark YB, Sarker IH (2022b) Novel energy management scheme in iot enabled smart irrigation system using optimized intelligence methods. Eng Appl Artif Intell 114:104996

  20. Lin L, Shi Y, Chen J, Ali S (2020) A novel fuzzy pid congestion control model based on cuckoo search in wsns. Sensors 20(7):1–16. https://doi.org/10.3390/s20071862

    Article  Google Scholar 

  21. Ma C (2018) A congestion control protocol for wireless sensor networks. In: International Conference on Computational Social Networks, Springer, pp 356–367, https://doi.org/10.1007/978-3-030-04648-4_30

  22. Maddikunta PKR, Gadekallu TR, Kaluri R, Srivastava G, Parizi RM, Khan MS (2020) Green communication in iot networks using a hybrid optimization algorithm. Comput Commun 159:97–107 . https://doi.org/10.1016/j.comcom.2020.05.020. https://www.sciencedirect.com/science/article/pii/S0140366420301286

    Article  Google Scholar 

  23. Mansouri N, Zade BMH, Javidi MM (2020) A multi-objective optimized replication using fuzzy based self-defense algorithm for cloud computing. J Netw Comput Appl 171:1–33. https://doi.org/10.1016/j.jnca.2020.102811

    Article  Google Scholar 

  24. Narawade V, Kolekar UD (2018) Acsro: Adaptive cuckoo search based rate adjustment for optimized congestion avoidance and control in wireless sensor networks. Alex Eng J 57(1):131–145. https://doi.org/10.1016/j.aej.2016.10.005

    Article  Google Scholar 

  25. Nikokheslat HD, Ghaffari A (2017) Protocol for controlling congestion in wireless sensor networks. Wireless Pers Commun 95(3):3233–3251. https://doi.org/DOI10.1007/s11277-017-3992-y

  26. Parsa A, Moghim N (2021) Qos-aware routing and traffic management in multi-flow opportunistic routing. Computers & Electrical Engineering 94:1–14. https://doi.org/10.1016/j.compeleceng.2021.107330

    Article  Google Scholar 

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

    Article  Google Scholar 

  28. Qureshi IA, Asghar S (2021) A genetic fuzzy contention window optimization approach for IEEE 802.11 WLANS. Wireless Netw 27(4):2323–2336. https://doi.org/10.1007/s11276-021-02572-8

    Article  Google Scholar 

  29. 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(1):815–842. https://doi.org/10.1007/s11277-017-4896-6

    Article  Google Scholar 

  30. Sarker IH, Khan AI, Abushark YB, Alsolami F (2022) Internet of things (iot) security intelligence: A comprehensive overview, machine learning solutions and research directions. Mobile Networks and Applications, https://doi.org/10.1007/s11036-022-01937-3

  31. Shah SA, Nazir B, Khan IA (2017) Congestion control algorithms in wireless sensor networks: Trends and opportunities. J King Saud Univ Comput Inform Sci 29(3):236–245. https://doi.org/10.1016/j.jksuci.2015.12.005

    Article  Google Scholar 

  32. Shelke MP, Malhotra A, Mahalle P (2017) A packet priority intimation-based data transmission for congestion free traffic management in wireless sensor networks. Comput Electr Eng 64:248–261. https://doi.org/10.1016/j.compeleceng.2017.03.007

    Article  Google Scholar 

  33. Singh K, Singh K, Son LH, Aziz A (2018) Congestion control in wireless sensor networks by hybrid multi-objective optimization algorithm. Comput Netw 138:90–107. https://doi.org/10.1016/j.comnet.2018.03.023

    Article  Google Scholar 

  34. Srivastava V, Tripathi S, Singh K, Son LH (2020) Energy efficient optimized rate based congestion control routing in wireless sensor network. J Ambient Intell Humaniz Comput 11:1325–1338. https://doi.org/10.1007/s12652-019-01449-1

    Article  Google Scholar 

  35. Vijayalakshmi K, Anandan P (2019) A multi objective tabu particle swarm optimization for effective cluster head selection in wsn. Cluster Computing 22(5):12275–12282. https://doi.org/10.1007/s10586-017-1608-7

    Article  Google Scholar 

  36. Wang GG, Gandomi AH, Alavi AH, Gong D (2017) A comprehensive review of krill herd algorithm: variants, hybrids and applications. Artif Intell Rev 51(1):119–148. https://doi.org/10.1007/s10462-017-9559-1

    Article  Google Scholar 

  37. Yadav SL, Ujjwal RL, Kumar S, Kaiwartya O, Kumar M, Kashyap PK (2021) Traffic and energy aware optimization for congestion control in next generation wireless sensor networks. J Sensors 2021:5575802. https://doi.org/10.1155/2021/5575802

    Article  Google Scholar 

  38. Yang X, Chen X, **a R, Qian Z (2018) Wireless sensor network congestion control based on standard particle swarm optimization and single neuron pid. Sensors 18(4):1265. https://doi.org/doi:10.3390/s18041265

  39. Yannibelli V, Pacini E, Monge D, Mateos C, Rodriguez G (2020) A comparative analysis of nsga-ii and nsga-iii for autoscaling parameter sweep experiments in the cloud. Scientific Programming 2020, https://doi.org/10.1155/2020/4653204

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Correspondence to Kabeer Ahmed Bhatti.

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Bhatti, K.A., Asghar, S. & Naz, S. Multi-objective fuzzy krill herd congestion control algorithm for WSN. Multimed Tools Appl 83, 2093–2121 (2024). https://doi.org/10.1007/s11042-023-15200-8

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