Congestion Control Techniques in Wireless Sensor Networks to Achieve QoS Parameters: a survey

  • Unique Paper ID: 183387
  • PageNo: 1119-1123
  • Abstract:
  • Wireless Sensor Networks (WSNs) play a crucial role in various applications, but their performance is often hindered by congestion, which adversely affects Quality of Service (QoS) parameters. This paper presents a comprehensive review and analysis of congestion control techniques in WSNs aimed at achieving optimal QoS. We examine various approaches, including rate-based, buffer-based, priority-based, cluster-based, and cross-layer techniques, as well as emerging methods utilizing machine learning, fuzzy logic, and bio-inspired algorithms. The study evaluates these techniques based on their ability to address key challenges in WSNs, such as resource constraints, network dynamics, and scalability issues. A comparative analysis is conducted using simulation scenarios to assess the performance of different congestion control methods across various metrics, including throughput, packet delivery ratio, end-to-end delay, and energy efficiency. The results demonstrate that hybrid approaches combining multiple techniques often yield superior performance in maintaining QoS under diverse network conditions. Furthermore, the paper identifies research gaps and proposes future directions for developing more efficient and adaptive congestion control mechanisms in WSNs, emphasizing the need for energy-aware and QoS-driven solutions.

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{183387,
        author = {Priti W. Pawade and Dr.Rahul Kumar Budania and Dr.Dattatray Waghole},
        title = {Congestion Control Techniques in Wireless Sensor Networks to Achieve QoS Parameters: a survey},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {1119-1123},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183387},
        abstract = {Wireless Sensor Networks (WSNs) play a crucial role in various applications, but their performance is often hindered by congestion, which adversely affects Quality of Service (QoS) parameters. This paper presents a comprehensive review and analysis of congestion control techniques in WSNs aimed at achieving optimal QoS. We examine various approaches, including rate-based, buffer-based, priority-based, cluster-based, and cross-layer techniques, as well as emerging methods utilizing machine learning, fuzzy logic, and bio-inspired algorithms. The study evaluates these techniques based on their ability to address key challenges in WSNs, such as resource constraints, network dynamics, and scalability issues. A comparative analysis is conducted using simulation scenarios to assess the performance of different congestion control methods across various metrics, including throughput, packet delivery ratio, end-to-end delay, and energy efficiency. The results demonstrate that hybrid approaches combining multiple techniques often yield superior performance in maintaining QoS under diverse network conditions. Furthermore, the paper identifies research gaps and proposes future directions for developing more efficient and adaptive congestion control mechanisms in WSNs, emphasizing the need for energy-aware and QoS-driven solutions.},
        keywords = {Wireless Sensor Networks, Congestion Control, Quality of Service, Network Performance, Resource Constraints, Hybrid Approaches, Energy Efficiency, Adaptive Mechanisms.},
        month = {August},
        }

Cite This Article

Pawade, P. W., & Budania, D. K., & Waghole, D. (2025). Congestion Control Techniques in Wireless Sensor Networks to Achieve QoS Parameters: a survey. International Journal of Innovative Research in Technology (IJIRT), 12(3), 1119–1123.

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