An oceanographic data collection scheme using hybrid optimization for leakage detection during oil mining in mobility assisted UWSN

• Published in: Multimedia tools and applications Vol. 83; no. 42; pp. 89723 - 89741
• Main Authors: Choudhary, Monika, Goyal, Nitin, Gupta, Deepali, Sharma, Bhanu, Sharma, Nonita
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2024; Springer Nature B.V
• Subjects: Algorithms; Buffers; Computer Communication Networks; Computer Science; Data acquisition; Data collection; Data Structures and Information Theory; Data transmission; Energy levels; Leak detection; Multimedia Information Systems; Network latency; Optimization; Optimization algorithms; Optimization techniques; Path planning; Real time; Residual energy; Special Purpose and Application-Based Systems; Task scheduling; Water currents; Wireless sensor networks; Wolves; Leakage detection; Whale optimization algorithm; And oil mining; Grey wolf optimizer; Bubble net hunting; Autonomous underwater vehicles (AUVs)
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-024-19023-z

Data acquisition is the process of collecting, measuring and analysing information using standardised, validated techniques for application-specific tasks. In mobility-assisted underwater wireless sensor networks (UWSNs), where nodes are not fixed due to water current of 3 m/sec, data collection becomes an arduous task. There are few works that provide a mobile sink with optimised data transmission path planning and scheduling. These systems do not transmit the data fast enough to provide real-time data transmission as these methods do not consider the buffer occupancy rate and latency in data acquisition. In this paper, a stimulating transmission path planning technique for mobile sinks using the hybrid Grey Wolf Optimizer Whale Optimization Algorithm (GWOWOA) is proposed. In contrast to other optimization techniques, this hybrid technique includes a number of update processes such as random position update, prey search by the Grey Wolf Optimizer (GWO) and prey search by the Whale Optimization Algorithm (WOA). In this paper, the fitness function is calculated in terms of distance to the mobile sink, buffer occupancy rate, energy level and data acquisition latency. The use of these variables makes the proposed technique innovative. To prove the efficiency of the proposed system, GWOWOA is compared with existing systems. The simulation results show that the proposed system increases the residual energy and accuracy of the collected data and minimises the delay.