Energy efficient Q learning based Kullback sparse encoder for traffic and congestion control data delivery in WSN

Document Type : Research Paper


Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore – 21.Tamil Nadu, India


In dense traffic Wireless Sensor Network (WSN), traffic congestion can increase routing overhead and packet loss, which restricts the entire network performance, therefore a traffic-aware and congestion-control data delivery is required to control the traffic. The proposed Energy-efficient Q-learning and Kullback Sparse Encoder (EQ-KSE) method is used for traffic-aware routing and congestion-control data delivery method for WSN. The method enforces a traffic balancing strategy using the energy-efficient reward function and estimates the wireless link quality by the Energy-efficient and Q-Learning Routing algorithm. On the basis of the estimation of each wireless link, the Energy-efficient Q-Learning based Traffic-aware Routing model makes routing decisions through energy and queue length to reduce routing overhead and time significantly. With the obtained optimal routes data aggregation are performed at the sink node, causing a proportionate amount of congestion. To handle this issue, a Kullback Leibler Sparse Auto Encoder (KL-SAE) Congestion-control Data Delivery method is proposed. This KL-SAE model with the aid of the reconstruction loss function, through divergence reduces the congestion, therefore contributing to packet loss, and packet delivery ratio. Simulation results show that EQ-KSE method performs traffic-aware routing by minimizing both the route selection time and overhead. In high node density scenarios, it also betters the state-of-the-art methods in packet delivery ratio and packet loss rate.


[1] J. Agarkhed, P.Y. Dattatraya and S. Pati, Multi-QoS constraint multipath routing in cluster-based wireless sensor network, Int. J. Inf. Tecnh. 13(3) (2020) 865–876.
[2] A.M. Ahmed and R. Paulus, Congestion detection technique for multipath routing and load balancing in WSN, Wireless Netw. 23(3) (2017) 881–888.
[3] K.M. Awan, N. Ashraf, M.Q. Saleem, O.E. Sheta, K.N. Qureshi, A. Zeb, K. Haseeb and A.S. Sadiq, A priority-based congestion-avoidance routing protocol using IoT-based heterogeneous medical sensors for energy efficiency in healthcare wireless body area networks, Inter. J. Distributed Sensor Netw. 15(6) (2019) 1–16.
[4] A. Ayyasamy, E.G. Julie, Y.H. Robinson, S. Balaji, R. Kumar, L.H. Son, P.H. Thong and I. Priyadarshini, AVRM: adaptive void recovery mechanism to reduce void nodes in wireless sensor networks, Peer-to-Peer Netw. Appl. 13(3) (2020) 987–1001.
[5] I. Bouazzi, M. Zaidi, M. Usman and M.Z.M. Shamim, A new medium access control mechanism for energy optimization in WSN: traffic control and data priority scheme, J. Wireless Com. Network 2021(1) (2021) 42.
[6] W. Ding, L. Tang and S. Ji, Optimizing routing based on congestion control for wireless sensor networks, Wireless Netw. 22(3) (2016) 915–925.
[7] H. Haile, K.-J. Grinnemo, S. Ferlin, P. Hurtig and A. Brunstrom, End-to-end congestion control approaches for high throughput and low delay in 4G/5G cellular networks, Computer Networks 186 (2021) 107692.
[8] M.H. Homaei, F. Soleimani, S. Shamshirband, A. Mosavi, N. Nabipour and A.R. Varkonyi-Koczy, An enhanced distributed congestion control method for classical 6LowPAN protocols using fuzzy decision system, IEEE Access 8 (2020) 20628–20645.
[9] R.N. Jadoon, W.Y. Zhou, I.A. Khan, M.A. Khan and W. Jadoon, EEHRT: Energy efficient technique for handling redundant traffic in zone-based routing for wireless sensor networks, Wireless Commun. Mobile Comput. 2019 (2019) 7502140.
[10] C. Kim, H. Cho, K. Jung, Y. Yim, T. Yang, S.-H. Kim and S. Kim, Agent-based multipath management for supporting sink mobility in wireless sensor networks, Wireless Commun. Mobile Comput. 2020 (2020) 8876928.
[11] J. Luo, J. Wu and Y. Wu, Advanced data delivery strategy based on multiperceived community with IoT in social complex networks, Complexity 2020 (2020) 3576542.
[12] K.-V. Nguyen, C.-H. Nguyen, P.L. Nguyen, T.V. Do and I. Chlamtac, Energy-efficient routing in the proximity of a complicated hole in wireless sensor networks, Wireless Netw. 27(4) (2021) 3073–3089.
[13] V. Pandiyarajud, R. Logambigai, S. Ganapathy and A. Kannan, An energy efficient routing algorithm for WSNs using intelligent fuzzy rules in precision agriculture, Wireless Pers. Commun. 112(1) (2020) 243–259.
[14] S. Qu, L. Zhao and Z. Xiong, Cross-layer congestion control of wireless sensor networks based on fuzzy sliding mode control, Neural Comput. Appl. 32(17) (2020) 13505–13520.
[15] O. Said, Performance evaluation of WSN management system for QoS guarantee, J. Wireless Com. Network 2015(1) (2015) 220.
[16] V. Srivastava, S. Tripathi, K. Singh and L.H. Son, Energy efficient optimized rate based congestion control routing in wireless sensor network, J. Ambient Intell. Human Comput. 11(3) (2020) 1325–1338.
[17] K. Sumathi and P. Pandiaraja, Dynamic alternate buffer switching and congestion control in wireless multimedia sensor networks, Peer-to-Peer Netw. Appl. 13(6) (2020) 2001–2010.
[18] I. Ullah and H.Y. Youn, Efficient data aggregation with node clustering and extreme learning machine for WSN, J. Supercomput. 76(12) (2020) 10009–10035.
[19] J. Zhang, P.-W. Tsai, X. Xue, X. Ye and S. Zhang, A comprehensive data gathering network architecture in large-scale visual sensor networks, PLOS ONE 15(1) (2020) e0226649.
Volume 12, Issue 2
November 2021
Pages 2601-2618
  • Receive Date: 02 May 2021
  • Accept Date: 30 June 2021