A survey on QoS mechanisms in WSN for computational intelligence based routing protocols

  • Tarunpreet Kaur
  • Dilip KumarEmail author


With the rapid development in ubiquitous smart sensors, wireless sensor networks have started to evolve into numerous applications including healthcare, medical, agriculture, transportation, industry, internet of things, and smart cities. However, satisfying Quality of Service (QoS) requirements of the diverse application domains remains a challenging issue due to heterogeneous traffic flows, dynamic network conditions, and resource-constrained nature of sensor nodes. In this regard, application-specific QoS provisioning techniques have received considerable research attention at the network layer. This paper presents a systematic review on the QoS mechanisms that have been employed by routing protocols and also highlights the performance issues of each mechanism. Afterwards, the survey presents a comparative analysis of computational intelligence based QoS-aware routing protocols with their strengths and limitations. Finally, this survey discusses various potential directions for future research in the field of QoS provisioning at network layer.


Quality of Service (QoS) Computational intelligence (CI) Routing protocol Wireless sensor network (WSN) 



  1. 1.
    Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.CrossRefGoogle Scholar
  2. 2.
    Bhandary, V., Malik, A., & Kumar, S. (2016). Routing in wireless multimedia sensor networks: A survey of existing protocols and open research issues. Journal of Engineering, 2016, 1–27.CrossRefGoogle Scholar
  3. 3.
    Mendes, L. D., & Rodrigues, J. J. (2011). A survey on cross-layer solutions for wireless sensor networks. Journal of Network and Computer Applications, 34(2), 523–534.CrossRefGoogle Scholar
  4. 4.
    Aswale, S., & Ghorpade, V. R. (2015). Survey of QoS routing protocols in wireless multimedia sensor networks. Journal of Computer Networks and Communications, 2015, 1–29.CrossRefGoogle Scholar
  5. 5.
    Hamid, Z., & Hussain, F. B. (2014). QoS in wireless multimedia sensor networks: A layered and cross-layered approach. Wireless Personal Communications, 75(1), 729–757.CrossRefGoogle Scholar
  6. 6.
    Gungor, V. C., & Hancke, G. P. (2009). Industrial wireless sensor networks: Challenges, design principles, and technical approaches. IEEE Transactions on Industrial Electronics, 56(10), 4258–4265.CrossRefGoogle Scholar
  7. 7.
    Liao, Y., Leeson, M. S., & Higgins, M. D. (2016). Flexible quality of service model for wireless body area sensor networks. Healthcare Technology Letters, 3(1), 12–15.CrossRefGoogle Scholar
  8. 8.
    Khalid, M., Ullah, Z., Ahmad, N., Arshad, M., Jan, B., Cao, Y., & Adnan, A. (2017). A survey of routing issues and associated protocols in underwater wireless sensor networks. Journal of Sensors, 2017, 1–17.CrossRefGoogle Scholar
  9. 9.
    Munir, S. A., Ren, B., Jiao, W., Wang, B., Xie, D., & Ma, J. (2007). Mobile wireless sensor network: Architecture and enabling technologies for ubiquitous computing. In 21st IEEE International conference on advanced information networking and applications workshops, AINAW’07 (Vol. 2, pp. 113–120).Google Scholar
  10. 10.
    Saleem, M., Di Caro, G. A., & Farooq, M. (2011). Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions. Information Sciences, 181(20), 4597–4624.CrossRefGoogle Scholar
  11. 11.
    Guo, W., & Zhang, W. (2014). A survey on intelligent routing protocols in wireless sensor networks. Journal of Network and Computer Applications, 38, 185–201.CrossRefGoogle Scholar
  12. 12.
    Kumar, J., Tripathi, S., & Tiwari, R. K. (2016). A survey on routing protocols for wireless sensor networks using swarm intelligence. International Journal of Internet Technology and Secured Transactions, 6(2), 79–102.CrossRefGoogle Scholar
  13. 13.
    Ehsan, S., & Hamdaoui, B. (2012). A survey on energy-efficient routing techniques with QoS assurances for wireless multimedia sensor networks. IEEE Communications Surveys & Tutorials, 14(2), 265–278.CrossRefGoogle Scholar
  14. 14.
    Hasan, M. Z., Al-Rizzo, H., & Al-Turjman, F. (2017). A survey on multipath routing protocols for QoS assurances in real-time wireless multimedia sensor networks. IEEE Communications Surveys & Tutorials, 19(3), 1424–1456.CrossRefGoogle Scholar
  15. 15.
    Yessad, N., Omar, M., Tari, A., & Bouabdallah, A. (2018). QoS-based routing in wireless body area networks: A survey and taxonomy. Computing, 100(3), 245–275.MathSciNetCrossRefGoogle Scholar
  16. 16.
    Alanazi, A., & Elleithy, K. (2015). Real-time QoS routing protocols in wireless multimedia sensor networks: Study and analysis. Sensors, 15(9), 22209–22233.CrossRefGoogle Scholar
  17. 17.
    Asif, M., Khan, S., Ahmad, R., Sohail, M., & Singh, D. (2017). Quality of service of routing protocols in wireless sensor networks: A review. IEEE Access, 5, 1846–1871.CrossRefGoogle Scholar
  18. 18.
    Bhatnagar, S., Deb, B., & Nath, B. (2001). Service differentiation in sensor networks. In Proceedings of wireless personal multimedia communications.Google Scholar
  19. 19.
    Afsar, M. M., & Tayarani-N, M. H. (2014). Clustering in sensor networks: A literature survey. Journal of Network and Computer Applications, 46, 198–226.CrossRefGoogle Scholar
  20. 20.
    Yetgin, H., Cheung, K. T. K., El-Hajjar, M., & Hanzo, L. H. (2017). A survey of network lifetime maximization techniques in wireless sensor networks. IEEE Communications Surveys & Tutorials, 19(2), 828–854.CrossRefGoogle Scholar
  21. 21.
    Xu, L., Collier, R., & O’Hare, G. M. (2017). A survey of clustering techniques in WSNs and consideration of the challenges of applying such to 5G IoT scenarios. IEEE Internet of Things Journal, 4(5), 1229–1249.CrossRefGoogle Scholar
  22. 22.
    Korkmaz, T., & Krunz, M. (2001). Multi-constrained optimal path selection. In IEEE INFOCOM Institute of Electrical Engineers Inc (Vol. 2, pp. 834–843).Google Scholar
  23. 23.
    Oyman, E. I., & Ersoy, C. (2004). Multiple sink network design problem in large scale wireless sensor networks. IEEE International Conference on Communications, 6, 3663–3667.Google Scholar
  24. 24.
    Nazir, B., & Hasbullah, H. (2010). Mobile sink based routing protocol (MSRP) for prolonging network lifetime in clustered wireless sensor network. In International conference on computer applications and industrial electronics (ICCAIE) (pp. 624–629).Google Scholar
  25. 25.
    Wang, Z. M., Basagni, S., Melachrinoudis, E., & Petrioli, C. (2005). Exploiting sink mobility for maximizing sensor networks lifetime. In Proceedings of the 38th Annual Hawaii international conference on system sciences, HICSS’05 (pp. 287a–287a).Google Scholar
  26. 26.
    Radi, M., Dezfouli, B., Bakar, K. A., & Lee, M. (2012). Multipath routing in wireless sensor networks: Survey and research challenges. Sensors, 12(1), 650–685.CrossRefGoogle Scholar
  27. 27.
    Kulkarni, R. V., Forster, A., & Venayagamoorthy, G. K. (2011). Computational intelligence in wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 13(1), 68–96.CrossRefGoogle Scholar
  28. 28.
    Dorigo, M., & Di Caro, G. (1999). Ant colony optimization: A new meta-heuristic. In Proceedings of the IEEE congress on evolutionary computation-CEC99 (Vol. 2, pp. 1470–1477).Google Scholar
  29. 29.
    Dorigo, M., & Stützle, T. (2004). Ant colony optimization. Cambridge, MA: MIT Press.CrossRefzbMATHGoogle Scholar
  30. 30.
    Cai, W., Jin, X., Zhang, Y., Chen, K., & Wang, R. (2006). ACO based QoS routing algorithm for wireless sensor networks. In International conference on ubiquitous intelligence and computing (pp. 419–428). Berlin: Springer.Google Scholar
  31. 31.
    Cobo, L., Quintero, A., & Pierre, S. (2010). Ant-based routing for wireless multimedia sensor networks using multiple QoS metrics. Computer Networks, 54(17), 2991–3010.CrossRefGoogle Scholar
  32. 32.
    Zuo, Y., Ling, Z., & Yuan, Y. (2013). A hybrid multi-path routing algorithm for industrial wireless mesh networks. EURASIP Journal on Wireless Communications and Networking, 2013(1), 82.CrossRefGoogle Scholar
  33. 33.
    Tong, M., Chen, Y., Chen, F., Wu, X., & Shou, G. (2015). An energy-efficient multipath routing algorithm based on ant colony optimization for wireless sensor networks. International Journal of Distributed Sensor Networks, 11(6), 642189.CrossRefGoogle Scholar
  34. 34.
    Malik, S. K., Dave, M., Dhurandher, S. K., Woungang, I., & Barolli, L. (2017). An ant-based QoS-aware routing protocol for heterogeneous wireless sensor networks. Soft Computing, 21(21), 6225–6236.CrossRefGoogle Scholar
  35. 35.
    Wang, J., Cao, J., Sherratt, R. S., & Park, J. H. (2017). An improved ant colony optimization-based approach with mobile sink for wireless sensor networks. The Journal of Supercomputing, 74(12), 6633–6645.CrossRefGoogle Scholar
  36. 36.
    Kennedy, J. (2011). Particle swarm optimization. In: C. Sammut & G. I. Webb (Eds.), Encyclopedia of machine learning (pp. 760–766). Boston, MA: SpringerGoogle Scholar
  37. 37.
    Parsopoulos, K. E., & Vrahatis, M. N. (2002). Particle swarm optimization method in multiobjective problems. In: Proceedings of the 2002 ACM symposium on applied computing (pp. 603–607). ACM.Google Scholar
  38. 38.
    Liu, M., Xu, S., & Sun, S. (2012). An agent-assisted QoS-based routing algorithm for wireless sensor networks. Journal of Network and Computer Applications, 35(1), 29–36.CrossRefGoogle Scholar
  39. 39.
    Hu, Y. F., Ding, Y. S., Ren, L. H., Hao, K. R., & Han, H. (2015). An endocrine cooperative particle swarm optimization algorithm for routing recovery problem of wireless sensor networks with multiple mobile sinks. Information Sciences, 300, 100–113.CrossRefGoogle Scholar
  40. 40.
    Yang, J., Liu, F., Cao, J., & Wang, L. (2016). Discrete particle swarm optimization routing protocol for wireless sensor networks with multiple mobile sinks. Sensors, 16(7), 1081.CrossRefGoogle Scholar
  41. 41.
    Wang, J., Cao, Y., Li, B., Kim, H. J., & Lee, S. (2017). Particle swarm optimization based clustering algorithm with mobile sink for WSNs. Future Generation Computer Systems, 76, 452–457.CrossRefGoogle Scholar
  42. 42.
    Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471.MathSciNetCrossRefzbMATHGoogle Scholar
  43. 43.
    Karaboga, D., & Akay, B. (2009). A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation, 214(1), 108–132.MathSciNetCrossRefzbMATHGoogle Scholar
  44. 44.
    Karaboga, D., Okdem, S., & Ozturk, C. (2012). Cluster based wireless sensor network routing using artificial bee colony algorithm. Wireless Networks, 18(7), 847–860.CrossRefGoogle Scholar
  45. 45.
    Ari, A. A. A., Yenke, B. O., Labraoui, N., Damakoa, I., & Gueroui, A. (2016). A power efficient cluster-based routing algorithm for wireless sensor networks: Honeybees swarm intelligence based approach. Journal of Network and Computer Applications, 69, 77–97.CrossRefGoogle Scholar
  46. 46.
    Kalyanmoy, D. (2001). Multi objective optimization using evolutionary algorithms (p. 124). New York: Wiley.zbMATHGoogle Scholar
  47. 47.
    Norouzi, A., & Zaim, A. H. (2014). Genetic algorithm application in optimization of wireless sensor networks. The Scientific World Journal.
  48. 48.
    Coello, C. C. (2006). Evolutionary multi-objective optimization: A historical view of the field. IEEE Computational Intelligence Magazine, 1(1), 28–36.MathSciNetCrossRefGoogle Scholar
  49. 49.
    EkbataniFard, G. H., Monsefi, R., Akbarzadeh-T, M. R., & Yaghmaee, M. H. (2010). A multi-objective genetic algorithm based approach for energy efficient QoS-routing in two-tiered wireless sensor networks. In 5th IEEE International symposium on wireless pervasive computing (ISWPC) (pp. 80–85).Google Scholar
  50. 50.
    Murugeswari, R., Radhakrishnan, S., & Devaraj, D. (2016). A multi-objective evolutionary algorithm based QoS routing in wireless mesh networks. Applied Soft Computing, 40, 517–525.CrossRefGoogle Scholar
  51. 51.
    Magaia, N., Horta, N., Neves, R., Pereira, P. R., & Correia, M. (2015). A multi-objective routing algorithm for wireless multimedia sensor networks. Applied Soft Computing, 30, 104–112.CrossRefGoogle Scholar
  52. 52.
    Faheem, M., Tuna, G., & Gungor, V. C. (2018). QERP: quality-of-service (QoS) aware evolutionary routing protocol for underwater wireless sensor networks. IEEE Systems Journal, 12(3), 2066–2073.CrossRefGoogle Scholar
  53. 53.
    Minhas, M. R., Gopalakrishnan, S., & Leung, V. C. (2009). Multiobjective routing for simultaneously optimizing system lifetime and source-to-sink delay in wireless sensor networks. In 29th IEEE international conference on distributed computing systems workshops (pp. 123–129).Google Scholar
  54. 54.
    Gaddour, O., Koubâa, A., Baccour, N., & Abid, M. (2014). OF-FL: QoS-aware fuzzy logic objective function for the RPL routing protocol. In 12th International symposium on modeling and optimization in mobile, ad hoc, and wireless networks (WiOpt) (pp. 365–372).Google Scholar
  55. 55.
    Revathi, T., & Muneeswaran, K. (2017). Multi-constraint multi-objective QoS aware routing heuristics for query driven sensor networks using fuzzy soft sets. Applied Soft Computing, 52, 532–548.CrossRefGoogle Scholar
  56. 56.
    Thrun, S., & Littman, M. L. (2000). Reinforcement learning: An introduction. AI Magazine, 21(1), 103.Google Scholar
  57. 57.
    Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 237–285.CrossRefGoogle Scholar
  58. 58.
    Liang, X., Balasingham, I., & Byun, S. S. (2008). A reinforcement learning based routing protocol with QoS support for biomedical sensor networks. In First international symposium on applied sciences on biomedical and communication technologies, ISABEL’08 (pp. 1–5).Google Scholar
  59. 59.
    Jin, Z., Ma, Y., Su, Y., Li, S., & Fu, X. (2017). A Q-learning-based delay-aware routing algorithm to extend the lifetime of underwater sensor networks. Sensors, 17(7), 1660.CrossRefGoogle Scholar
  60. 60.
    Askarzadeh, A. (2014). Bird mating optimizer: An optimization algorithm inspired by bird mating strategies. Communications in Nonlinear Science and Numerical Simulation, 19(4), 1213–1228.MathSciNetCrossRefGoogle Scholar
  61. 61.
    Corde, S., Chifu, V. R., Salomie, I., Chifu, E. S., & Iepure, A. (2016). Bird mating optimization method for one-to-n skill matching. In IEEE 12th International conference on intelligent computer communication and processing (ICCP) (pp. 155–162).Google Scholar
  62. 62.
    Faheem, M., & Gungor, V. C. (2018). Energy efficient and QoS-aware routing protocol for wireless sensor network-based smart grid applications in the context of industry 4.0. Applied Soft Computing, 68, 910–922.CrossRefGoogle Scholar
  63. 63.
    Shokouhifar, M., & Jalali, A. (2017). Optimized sugeno fuzzy clustering algorithm for wireless sensor networks. Engineering Applications of Artificial Intelligence, 60, 16–25.CrossRefGoogle Scholar
  64. 64.
    Amiri, E., Keshavarz, H., Alizadeh, M., Zamani, M., & Khodadadi, T. (2014). Energy efficient routing in wireless sensor networks based on fuzzy ant colony optimization. International Journal of Distributed Sensor Networks, 10(7), 768936.CrossRefGoogle Scholar
  65. 65.
    Tian, J., Gao, M., & Ge, G. (2016). Wireless sensor network node optimal coverage based on improved genetic algorithm and binary ant colony algorithm. Eurasip Journal on Wireless Communications and Networking, 2016(1), 104.CrossRefGoogle Scholar
  66. 66.
    Lu, J., Wang, X., Zhang, L., & Zhao, X. (2014). Fuzzy random multi-objective optimization based routing for wireless sensor networks. Soft Computing, 18(5), 981–994.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Electronics and Communication Engineering DepartmentSLIETLongowalIndia

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