Research Article |
Congestion-Free Cluster Formation and Energy Efficient Path Selection in Wireless Sensor Networks using ButPCNN
Author(s): S. Panimalar1* and Dr. T. Prem Jacob2
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 11, Issue 2
Publisher : FOREX Publication
Published : 30 May 2023
e-ISSN : 2347-470X
Page(s) : 315-322
Abstract
Today, network congestion is a common occurrence that needs to be focused on and effectively addressed, particularly in Wireless Sensor Networks (WSN) for packed type networks. The main causes of congestion in WSN are a lack of channel capacity and energy waste. This study's major goal is to develop Energy Efficient Congestion Free Path Selection Protocol (ECFPSP) protocol, which aims to reduce network congestion. By selecting the most appropriate main cluster head (PCH) and secondary cluster head (SCH), the ECFPSP protocol is proposed to decrease end-to-end delay time and extend the network lifetime. The suggested protocol implements a routing protocol that provides security by avoiding hostile nodes and reducing data loss. It also routes the nodes. Hence, a Congestion-Free Cluster Formation is provided to increase the lifetime of the network by proposed ButPCNN approach. To decrease packet loss and conserve energy, this research also uses brand-new cluster-based WSNs. In comparison to other standard protocols, the simulation results reveal that ButPCNN has a reduced packet drop rate, which increases the ratio of packet distribution, network life, and residual energy. As a result, the suggested method enhances congestion control performance while using less energy and a recently developed strategy is suggested to successfully enhance network performance. The proposed ButPCNN gives 25 percent improvement to optimize traffic on overloaded node than the other traditional approaches.
Keywords: Wireless Sensor Networks (WSNs)
, Congestion Control
, Bee Optimization
, Butterfly Optimization
,BatFuzzyBee
.
S. Panimalar*, Research Scholar, School of Computing, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India; Email: panimalarjerome@gmail.com
Dr. T. Prem Jacob, Professor, School of Computing, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India; Email: premjac@gmail.com
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[1] Almalkawi I T, Guerrero Z M, Al-Karaki J N and Morillo-Pozo J 2010 Wireless multimedia sensor networks: current trends and future directions Sensors. 10(7) 6662-717. [Cross Ref]
-
[2] Osuo-Genseleke, M., Kabari, L. and Nathaniel, O., 2018. Performance measures for congestion control techniques in a wireless sensor network. International Journal of Scientific and Research Publications, 7, pp.1-5. [Cross Ref]
-
[3] Trappe W, Ning P and Perrig A 2010 Security and privacy for sensor networks Handbook on Array Processing and Sensor Networks. 855-87. [Cross Ref]
-
[4] Farahani, S.S.S. and Fakhimi Derakhshan, S., 2019. LMI-based congestion control algorithms for a delayed network. International Journal of Industrial Electronics Control and Optimization, 2(2), pp.91-98.
-
[5] Singh, K., Singh, K. and Aziz, A., 2018. Congestion control in wireless sensor networks by hybrid multi-objective optimization algorithm. Computer Networks, 138, pp.90-107. [Cross Ref]
-
[6] K. Singh, K. Singh, L. H. Son, and A. Aziz, ‘‘Congestion control in wireless sensor networks by hybrid multi-objective optimization algorithm,’’ Comput. Netw., vol. 138, pp. 90–107, Jun. 2018. [Cross Ref]
-
[7] V. Bibin Christopher and J. Jasper, “Jellyfish dynamic routing protocol with mobile sink for location privacy and congestion avoidance in wireless sensor networks”, Journal of Systems Architecture, Elsevier, Volume 112, January 2021, Pages 1-14. [Cross Ref]
-
[8] Amit Grover, R. Mohan Kumar, Mohit Angurala, Mehtab Singh, Anu Sheetal and R. Maheswar, “Rate aware congestion control mechanism for wireless sensor networks”, Alexandria Engineering Journal, Elsevier, Volume 61, Issue 6, June 2022, Pages 4765-4777. [Cross Ref]
-
[9] Vaibhav Narawade and Uttam D. Kolekar, “ACSRO: Adaptive cuckoo search based rate adjustment for optimized congestion avoidance and control in wireless sensor networks”, Alexandria Engineering Journal, Elsevier, Volume 57, 2018, Pages 131–145. [Cross Ref]
-
[10] Karishma Singh, Karan Singh, Le Hoang Son and Ahmed Aziz, “Congestion Control in Wireless Sensor Networks by Hybrid Multi-Objective Optimization Algorithm”, Computer Networks, Elsevier, Volume 138, 19 June 2018, Pages 90-107. [Cross Ref]
-
[11] Yawar Abbas Bangash, Ling-Fang Zeng and Dan Feng, “MimiBS: Mimicking Base-Station to Provide Location Privacy Protection in Wireless Sensor Networks”, Journal of Computer Science and Technology, Springer, Volume 32, Issue 5, September 2017, Pages 991–1007. [Cross Ref]
-
[12] Prasenjit Chanak and Indrajit Banerjee, “Congestion Free Routing Mechanism for IoT- Enabled Wireless Sensor Networks for Smart Healthcare Applications”, IEEE Transactions on Consumer Electronics, Volume 66, Issue 3, August 2020, Pages 223 – 232. [Cross Ref]
-
[13] Daniyal Alghazzawi, Omaima Bamasaq, Surbhi Bhatia, Ankit Kumar, Pankaj Dadheech and Aiiad Albeshri, “Congestion Control in Cognitive IoT-Based WSN Network for Smart Agriculture”, IEEE Access, Volume 9, November 2021, Pages 151401 - 151420. [Cross Ref]
-
[14] Srijit Chowdhury, Abderrahim Benslimane and Chandan Giri, “Noncooperative Gaming for EnergyEfficient Congestion Control in 6LoWPAN”, IEEE Internet of Things Journal, Volume 7, Issue 6, June 2020, Pages 4777 – 4788. [Cross Ref]
-
[15] P. Kuppusamy, R. Kalpana and P. Venkateswara Rao, "Optimized traffic control and data processing using IoT", Cluster Computing, vol. 22, no. 1, pp. 2169-2178, 2018. [Cross Ref]
-
[16] S. Qu, L. Zhao and Z. Xiong, "Cross-layer congestion control of wireless sensor networks based on fuzzy sliding mode control", Neural Computing and Applications, vol. 32, no. 17, pp. 13505-13520, 2020. [Cross Ref]
-
[17] A. Hussain, S. Manikanthan, T. Padmapriya and M. Nagalingam, "Genetic algorithm based adaptive offloading for improving IoT device communication efficiency", Wireless Networks, vol. 26, no. 4, pp. 2329-2338, 2019. [Cross Ref]
-
[18] M. Swarna and T. Godhavari, "Enhancement of CoAP based congestion control in IoT network - a novel approach", Materials Today: Proceedings, vol. 37, pp. 775-784, 2021. [Cross Ref]
-
[19] S. Gheisari and E. Tahavori, "CCCLA: A cognitive approach for congestion control in Internet of Things using a game of learning automata", Computer Communications, vol. 147, pp. 40-49, 2019. [Cross Ref]
-
[20] F. Naeem, G. Srivastava and M. Tariq, "A Software Defined Network Based Fuzzy Normalized Neural Adaptive Multipath Congestion Control for the Internet of Things", IEEE Transactions on Network Science and Engineering, vol. 7, no. 4, pp. 2155-2164, 2020. [Cross Ref]
-
[21] T. Akhtar, N. G. Haider, and S. M. Khan, “A Comparative Study of the Application of Glowworm Swarm Optimization Algorithm with other Nature-Inspired Algorithms in the Network Load Balancing Problem”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 4, pp. 8777–8784, Aug. 2022. [Cross Ref]
-
[22] M. A. Mahdi, T. C. Wan, A. Mahdi, M. A. G. Hazber, and B. A. Mohammed, “A Multipath Cluster-Based Routing Protocol For Mobile Ad Hoc Networks”, Eng. Technol. Appl. Sci. Res., vol. 11, no. 5, pp. 7635–7640, Oct. 2021. [Cross Ref]