Octoroute: Revolutionizing Buoyant Sensor Mobility in Underwater Communication Networks

Octoroute: Revolutionizing Buoyant Sensor Mobility in Underwater Communication Networks

  IJETT-book-cover           
  
© 2024 by IJETT Journal
Volume-72 Issue-12
Year of Publication : 2024
Author : J. Divya Jose, D. Vimal Kumar
DOI : 10.14445/22315381/IJETT-V72I12P123

How to Cite?
J. Divya Jose, D. Vimal Kumar, "Octoroute: Revolutionizing Buoyant Sensor Mobility in Underwater Communication Networks," International Journal of Engineering Trends and Technology, vol. 72, no. 12, pp. 269-285, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT--V72I12P123

Abstract
In the environment of underwater communication networks, buoyant sensor mobility poses unique challenges that hinder efficient data transmission and network reliability. Traditional routing protocols struggle to navigate the dynamic underwater environment, leading to packet loss, high latency, and inefficient energy consumption. In response to these challenges, this paper introduces OctoRoute, a novel routing protocol designed to revolutionize buoyant sensor mobility in underwater communication networks. OctoRoute leverages Octopus Optimization (O2) and Enhanced Greedy Perimeter Stateless Routing (EGPSR) techniques to dynamically adapt to changing environmental conditions, optimize routing paths, and maximize data transmission efficiency. Through comprehensive performance evaluations, OctoRoute consistently outperforms traditional protocols by achieving higher packet delivery ratios, lower packet drop ratios, increased throughput, reduced delay, and improved energy efficiency.

Keywords
OctoRoute, Buoyant sensor mobility, Underwater communication networks, Routing, Octopus optimization, Enhanced GPSR, Dynamic mobility.

References
Monika Chaudhary et al., “Underwater Wireless Sensor Networks: Enabling Technologies for Node Deployment and Data Collection Challenges,” IEEE Internet of Things Journal, vol. 10, no. 4, pp. 3500-3524, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Rucun Jia, “Design of A Simulation Platform for Intelligent Marine Search and Rescue Based on Wireless Sensors,” Microprocessors and Microsystems, vol. 80, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Shuxian Su et al., “Application of Ship Electric Propulsion Based on Internet of Things System and Electronic System,” Microprocessors and Microsystems, vol. 81, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Anwar Elhadad, and Seokheun Choi, “Powering the Internet of Things in Aquatic Environments: Solar Energy Harvesting Through a Buoyant Biosolar Cell Array,” Journal of Power Sources, vol. 581, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Ganggang Liang et al., “Performance Enhancement of Hydrodynamic Piezoelectric Energy Harvester by Array Disturbance,” Energy Conversion and Management, vol. 298, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Amrita Datta, and Mou Dasgupta, “Energy Efficient Topology Control in Underwater Wireless Sensor Networks,” Computers and Electrical Engineering, vol. 105, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Aya Ayadi et al., “A Framework of Monitoring Water Pipeline Techniques Based on Sensors Technologies,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 2, pp. 47-57, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[8] K. Sumathi, and D. Vimal Kumar, “Ambient Intelligence-Based Fish Swarm Optimization Routing Protocol for Congestion Avoidance in Mobile Ad-Hoc Network,” International Journal of Computer Networks and Applications (IJCNA), vol. 9, no. 3, pp. 340-349, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] S. Kawsalya, and D. Vimal Kumar, “Invigorated Chameleon Swarm Optimization-Based Ad-Hoc On-Demand Distance Vector (ICSO-AODV) for Minimizing Energy Consumption in Healthcare Mobile Wireless Sensor Networks,” International Journal of Computer Networks and Applications (IJCNA), vol. 11, no. 2, pp. 191-212, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Saziya Tabbassum, and Rajesh Kumar Pathak, “Effective Data Transmission Through Energy-Efficient Clustering and Fuzzy-Based IDS Routing Approach in Wsns,” Virtual Reality and Intelligent Hardware, vol. 6, no. 1, pp. 1-16, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Vishal B. Patil, and Surekha Kohle, “A High-Scalability and Low-Latency Cluster-Based Routing Protocol in Time-Sensitive Wsns Using Genetic Algorithm,” Measurement: Sensors, vol. 31, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Ines Lahmar et al., “Optimal Data Transmission for Decentralized IoT and WSN based on Type-2 Fuzzy Harris Hawks Optimization,” Internet of Things, vol. 25, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Malathi Murugesan et al., “Enhancing Network Lifetime of WSNs through the Implementation of a Modified Ant Colony Optimization Algorithm,” Procedia Computer Science, vol. 230, pp. 368-376, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Vasim Babu M. et al., “AE-LEACH: An Incremental Clustering Approach for Reducing the Energy Consumption in WSN,” Microprocessors and Microsystems, vol. 93, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Xiaojun Mei et al., “Target Localization Using Information Fusion in Wsns-Based Marine Search and Rescue,” Alexandria Engineering Journal, vol. 68, pp. 227-238, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Zhongwei Shen et al., “A Power Control-Aided Q-Learning-Based Routing Protocol for Optical-Acoustic Hybrid Underwater Sensor Networks,” IEEE Transactions on Green Communications and Networking, vol. 7, no. 4, pp. 2117-2129, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Debing Wei et al., “Power-Efficient Data Collection Scheme for AUV-Assisted Magnetic Induction and Acoustic Hybrid Internet of Underwater Things,” IEEE Internet of Things Journal, vol. 9, no. 14, pp. 11675-11684, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Shahrokh Vahabi et al., “CBDS2R: A Cluster-Based Depth Source Selection Routing for Underwater Wireless Sensor Network,” IEEE Transactions on Signal and Information Processing over Networks, vol. 9, pp. 468-476, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Cangzhu Xu et al., “An Efficient Deployment Scheme with Network Performance Modeling for Underwater Wireless Sensor Networks,” IEEE Internet of Things Journal, vol. 11, no. 5, pp. 8345-8359, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Chao Wang et al., “Multi-Agent Reinforcement Learning-Based Routing Protocol for Underwater Wireless Sensor Networks with Value of Information,” IEEE Sensors Journal, vol. 24, no. 5, pp. 7042-7054, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Yanan Li et al., “An AUV-Assisted Data Collection Scheme for UWSNs Based on Reinforcement Learning Under the Influence of Ocean Current,” IEEE Sensors Journal, vol. 24, no. 3, pp. 3960-3972, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Swati Gupta, and Niraj Pratap Singh, “Underwater Wireless Sensor Networks: A Review of Routing Protocols, Taxonomy, And Future Directions,” The Journal of Supercomputing, vol. 80, pp. 5163-5196, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Sangeeta Kumari, Pavan Kumar Mishra, and Veena Anand, “Coverage and Connectivity Aware Deployment Scheme for Autonomous Underwater Vehicles in Underwater Wireless Sensor Networks,” Wireless Personal Communications, vol. 132, pp. 909-931, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Aifen Yao, and Jianmei Chen, “A Novel Method for Routing in Underwater Wireless Sensor Networks to Increase Efficiency Based on IoT,” Multiscale and Multidisciplinary Modeling, Experiments and Design, vol. 7, pp. 411-424, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Yangmei Zhang, Zhouzhou Liu, and Yang Bi, “Node Deployment Optimization of Underwater Wireless Sensor Networks Using Intelligent Optimization Algorithm and Robot Collaboration,” Scientific Reports, vol. 13, no. 1, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Ashwini B. Gavali, Vinod M. Vaze, and Swapnaja A. Ubale, “HOCOR: Hybrid Optimization-Based Cooperative Opportunistic Routing for Underwater Wireless Sensor Networks,” Wireless Personal Communications, vol. 135, pp. 1449-1472, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Mingyue Zhang et al., “MO-CBACORP: A New Energy-Efficient Secure Routing Protocol for Underwater Monitoring Wireless Sensor Network,” Journal of King Saud University-Computer and Information Sciences, vol. 35, no. 9, 2023.
[CrossRef] [Google Scholar] [Publisher Link]