Congestion aware clustered WSN based on an improved ant colony algorithm

被引：0
作者：
Anto Pravin, R. [1 ]
Asha Shiny, X.S. [2 ]
Baby Vennila, V. [3 ]
Selvaraju, P. [4 ]
Uma Mageswari, R. [5 ]
Satish kumar, S. [6 ]
机构：
[1] Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu
[2] Department of Information Technology, CMR Engineering College, Hyderabad
[3] Department of Information Technology, SSM College of Engineering, Tamil Nadu
[4] Department of Artificial Intelligence and Data Science, Excel Engineering College, Tamil Nadu
[5] Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu
[6] Department of Electrical and Electronics Engineering, AMET University, Tamil Nadu
来源：
Measurement: Sensors | 2024年 / 34卷
关键词：
Ant colony optimization; Clustering; Congestion rate; Coverage cost; Environmental monitoring; Rendezvous node;
D O I：
10.1016/j.measen.2024.101280
中图分类号：
学科分类号：
摘要：
Conventional works carried out in Wireless Sensor Networks (WSN) mostly focussed on energy oriented services and very less significant measures given to delay oriented and congestion aware services. Hence the proposed mechanism specially focuses on network structural design by placing rendezvous location for each cluster as well as route segmentation for controlling the congestion occurrence and unwanted delay. Here Congestion Aware Clustering with Improved Ant Colony Algorithm (CAC_IACA) is proposed. This mechanism involves two steps (i) identifying the best route by following the Ant Colony Optimization (ACO) algorithm and (ii) data segmentation using rendezvous mobile nodes. The Rendezvous nodes are present in each cluster to reduce the congestion rate on receiver side during data transmission. This proposed methodology mainly concentrates on reducing coverage cost for 3D environmental monitoring. Simulation results are analysed and the efficiency of the proposed scheme proves 26.54 % better than the conventional method. © 2024 The Authors
引用
收藏
相关论文
共 24 条
  • [1] Deif D.S., Gadallah Y., An ant colony optimization approach for the deployment of reliable wireless sensor networks, IEEE Access, 5, pp. 10744-10756, (2017)
  • [2] Sun B., Gui C., Song Y., Chen H., A novel network coding and multi-path routing approach for wireless sensor network, Wireless Pers. Commun., 77, 1, pp. 87-99, (2014)
  • [3] Kiruba D.G., Benitha J., Fuzzy based energy proficient secure clustered routing (FEPSRC) for IOT-MWSN, J. Intell. Fuzzy Syst., 43, 6, pp. 7633-7645, (2022)
  • [4] Rajesh D., Rajanna G.S., CSCRT protocol with energy efficient secured CH clustering for smart dust network using quantum key distribution, International Journal of Safety and Security Engineering, 12, 4, pp. 441-448, (2022)
  • [5] Kar A.K., Bio inspired computing–a review of algorithms and scope of applications, Expert Syst. Appl., 59, pp. 20-32, (2016)
  • [6] Liu X., Routing protocols based on ant colony optimization in wireless sensor networks: a survey, IEEE Access, 5, pp. 26303-26317, (2017)
  • [7] Ammad Uddin M., Mansour A., Le Jeune D., Ayaz M., Aggoune E.H.M., UAV-assisted dynamic clustering of wireless sensor networks for crop health monitoring, Sensors, 18, 2, (2018)
  • [8] Rajesh D., Rajanna G.S., CSCRT protocol with energy efficient secured CH clustering for smart dust network using quantum key distribution, International Journal of Safety and Security Engineering, 12, 4, pp. 441-448, (2022)
  • [9] Gui T., Ma C., Wang F., Wilkins D.E., Survey on swarm intelligence based routing protocols for wireless sensor networks: an extensive study, 2016 IEEE International Conference on Industrial Technology (ICIT), pp. 1944-1949, (2016)
  • [10] Sun Y., Dong W., Chen Y., An improved routing algorithm based on ant colony optimization in wireless sensor networks, IEEE Commun. Lett., 21, 6, pp. 1317-1320, (2017)
123