Hybrid Optimization Algorithm for Reliable Routing in Wireless Sensor Network

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 12

Year of Publication : 2024

Authors : Shankar Madkar, Aparna Patil, Mangal Patil, Anuradha Nigade, Sonali Pawar, Sanjay Pardeshi

10.14445/23488379/IJEEE-V11I12P123

How to Cite?

Shankar Madkar, Aparna Patil, Mangal Patil, Anuradha Nigade, Sonali Pawar, Sanjay Pardeshi, "Hybrid Optimization Algorithm for Reliable Routing in Wireless Sensor Network," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 12, pp. 255-262, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I12P123

Abstract:

In Wireless Sensor Networks (WSN), congestion is a major problem as data traffic increases to the channel’s aggregate capacity. Routing is one of the best methods for reducing node energy usage and increasing throughput in WSNs. This research suggests an effective congestion avoidance strategy based on the Huffman coding technique and Forward Error Code (FEC) integrated with a metaheuristic optimization algorithm to enhance network performance. Using a hybridization of the traditional Zebra Optimization Algorithm (ZOA) and Harris Hawk Optimization (HHO) techniques, the proposed Zebra Hunt Optimization (ZHO) algorithm determines the optimal best paths for sharing the data packet. This process determines the best path for routing the data packet. Here, selection is based on fitness parameters such as flow-based features, buffer occupancy, bandwidth, and throughput. Then, the link quality estimation is evaluated for the identified optimal best path. Also, including a coding scheme in the data packet corrects the errors in transmitted data without retransmission and reduces redundancy.

Keywords:

Wireless Sensor Networks, Reliability, Zebra Hunt Optimization.

References:

[1] Mohammed Zaid Ghawy et al., “An Effective Wireless Sensor Network Routing Protocol Based on Particle Swarm Optimization Algorithm,” Wireless Communications and Mobile Computing, vol. 2022, pp. 1-13, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[2] K. SureshKumar, and P. Vimala, “Energy Efficient Routing Protocol Using Exponentially-Ant Lion Whale Optimization Algorithm in Wireless Sensor Networks,” Computer Networks, 197, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Jyoti Bhola, Surender Soni, and Gagandeep Kaur Cheema, “Genetic Algorithm Based Optimized Leach Protocol for Energy Efficient Wireless Sensor Networks,” Journal of Ambient Intelligence and Humanized Computing, vol. 11, pp. 1281-1288, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Shiva Gorgich, and Shayesteh Tabatabaei, “Proposing an Energy-Aware Routing Protocol by Using Fish Swarm Optimization Algorithm in WSN (Wireless Sensor Networks),” Wireless Personal Communications, vol. 119, no. 3, pp. 1935-1955, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Zongshan Wang et al., “An Energy Efficient Routing Protocol Based on Improved Artificial Bee Colony Algorithm for Wireless Sensor Networks” IEEE Access, vol. 8, pp. 133577-133596, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Piyush Rawat, and Siddhartha Chauhan, “Particle Swarm Optimization-Based Energy Efficient Clustering Protocol in Wireless Sensor Network,” Neural Computing and applications, vol. 33, pp. 14147-14165, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Wenmin He et al., “Improve Zebra Optimization Algorithm with Adaptive Oscillation Weight and Golden Sine Operator,” Research Square, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Hamzeh Mohammad Alabool et al., “Harris Hawks Optimization: A Comprehensive Review of Recent Variants and Applications,” Neural Computing and Applications, vol. 33, pp. 8939-8980, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[9] M. Baskar et al., “Energy Efficient Congestion Free And Adaptive Mechanism For Data Delivery in Underwater Wireless Sensor Networks Using 2H-ACK,” Research Square, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Le Yang et al., “Energy Efficient Cluster-Based Routing Protocol for WSN Using Multi-Strategy Fusion Snake Optimizer and Minimum Spanning Tree,” Scientific Reports, vol. 14, no. 1, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Boniface Kayode Alese, Bamidele Moses Kuboye, and Omolara Iyabode Alabede, “Development of Recovery and Redundancy Model for Real Time Wireless Networks,” Journal of Computer Science Research, vol. 4, no. 3, pp. 12-19, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Kamanashis Biswas, “A Multipath Routing Protocol for Secure Energy Efficient Communication in Wireless Sensor Networks,” Computer Networks, vol. 232, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Addisalem Genta, D.K. Lobiyal, Jemal H. Abawajy, “Energy Efficient Multipath Routing Algorithm for Wireless Multimedia Sensor Network,” Sensors, vol. 19, no. 17, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Zahid Ullah Khan et al., “A Comprehensive Survey of Energy-Efficient MAC and Routing Protocols for Underwater Wireless Sensor Networks,” Electronics, vol. 11, no. 19, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Linlan Liu et al., “A Link Quality Estimation Method Based on Improved Weighted Extreme Learning Machine,” IEEE Access, vol. 9, pp. 11378-11392, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Xinlu Li, Brian Keegan, and Fredrick Mtenzi, “Energy Efficient Hybrid Routing Protocol Based on the Artificial Fish Swarm Algorithm and Ant Colony Optimisation for WSNs,” Sensors, vol. 18, no. 10, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Neelakandan Subramani et al., “Controlling Energy Aware Clustering and Multihop Routing Protocol for IoT Assisted Wireless Sensor Networks,” Concurrency and Computation: Practice and Experience, vol. 34, no. 21, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Marcin Lewandowski, and Bartłomiej Płaczek, “Data Transmission Reduction in Wireless Sensor Network for Spatial Event Detection,” Sensors, vol. 21, no. 21, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Latha R et al., “Routing Protocol Using Ant Colony Optimization- Traveling Salesman Problem,” Procedia Computer Science, vol. 230, pp. 515-521, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Rehmat Ullah et al., “Improving Resource-Constrained IoT Device Lifetimes by Mitigating Redundant Transmissions Across Heterogeneous Wireless Multimedia of Things,” Digital Communications and Networks, vol. 8, no. 5, pp. 778-790, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Ali Seyfollahi, Meysam Moodi, and Ali Ghaffari, “MFO-RPL: A Secure RPL-Based Routing Protocol Utilizing Moth-Flame Optimizer for the IoT Applications,” Computer Standards & Interfaces, vol. 82, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Jyoti Bhola et al., “Performance Evaluation of Multilayer Clustering Network Using Distributed Energy Efficient Clustering with Enhanced Threshold Protocol,” Wireless Personal Communications, vol. 126, pp. 2175-2189.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Gulista Khan et al., “An Energy-Efficient Event Reliability Protocol for Wireless Communication Networks,” Mobile Information Systems, vol. 2022, pp. 1-10. 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Mostafa Ali Babaei Shahraki et al., “RQAR: Location‐Free Reliable and QoS‐Aware Routing Protocol for Mobile Sink Underwater Wireless Sensor Networks,” International Journal of Communication Systems, vol. 36, no. 6, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Prakash K Sonwalkar, and Vijay Kalmani, “Energy Efficient Hop-by-Hop Retransmission and Congestion Mitigation of an Optimum Routing and Clustering Protocol for WSNs,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 3, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Gudla Sateesh, & Kuda Rao Nageswara, “A Reliable Routing Mechanism with Energy-Efficient Node Selection for Data Transmission Using a Genetic Algorithm in Wireless Sensor Network,” Facta Universitatis, Series: Electronics and Energetics, vol. 36, no. 2, pp. 209-226, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Eva Trojovská, Mohammad Dehghani, and Pavel Trojovský, “Zebra Optimization Algorithm: A New Bio-Inspired Optimization Algorithm for Solving Optimization Algorithm,” IEEE Access, vol. 10, pp. 49445-49473, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Fulin Tian, Jiayang Wang, and Fei Chu, “Improved Multi-Strategy Harris Hawks Optimization and Its Application in Engineering Problems,” Mathematics, vol. 11, no. 6, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[29] De-gan Zhang et al., “New Approach of Multi-Path Reliable Transmission for Marginal Wireless Sensor Network,” Wireless Networks, vol. 26, no. 2, pp. 1503-1517, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Sai Krishna Mothku, and Rashmi Ranjan Rout, “Markov Decision Process and Network Coding for Reliable Data Transmission in Wireless Sensor and Actor Networks,” Pervasive and Mobile Computing, vol. 56, pp. 29-44, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[31] M. A. Matheen and S. Sundar, “A Novel Technique to Mitigate the Data Redundancy and to Improvise Network Lifetime Using Fuzzy Criminal Search Ebola Optimization for WMSN,” Sensors, vol. 23, no. 4, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Computational Intelligence and Neuroscience, “Retracted: A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things,” Computational Intelligence and Neuroscience, vol. 2023, no. 1, 2023.
[CrossRef] [Google Scholar] [Publisher Link]