Abstract
For efficient monitoring of marine environments using underwater wireless sensor networks (UWSNs), a fundamental issue is to maximize network coverage as well as connectivity by appropriate deployment of the nodes within a given sensing area. Most of the existing node deployment schemes proposed for UWSNs, assume that sensor nodes are static and their location after deployment are fixed. However, due to ocean current and the contact of the underwater creatures, these deployed static sensor node moves from its original locations to other locations, which disrupts the network connectivity with the base stations as well as most of the targets remain uncovered. To find a solution to this issue, authors propose a coverage and connectivity aware deployment scheme for optimal placement of a set of autonomous underwater vehicles (AUVs) within UWSNs. The proposed scheme uses an improved Non-dominated Sorting Genetic Algorithm-II based metaheuristic technique with a novel fitness function which contains three parameters namely coverage quality, connected cost and network lifetime. Further, proposed scheme applies an effective encoding scheme for the population representation and devises a novel fitness function for upgrading the quantity of the AUVs as well as their location. Performance estimation of the proposed scheme and its comparison with the existing schemes with respect to convergence rate, coverage quality, connected cost, average energy consumption and network lifetime are discussed in detail. The simulation outcome confirm that the proposed approach upgrades the coverage of the network.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11277-023-10642-7/MediaObjects/11277_2023_10642_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11277-023-10642-7/MediaObjects/11277_2023_10642_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11277-023-10642-7/MediaObjects/11277_2023_10642_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11277-023-10642-7/MediaObjects/11277_2023_10642_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11277-023-10642-7/MediaObjects/11277_2023_10642_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11277-023-10642-7/MediaObjects/11277_2023_10642_Fig6_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11277-023-10642-7/MediaObjects/11277_2023_10642_Fig7_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11277-023-10642-7/MediaObjects/11277_2023_10642_Fig8_HTML.png)
Similar content being viewed by others
Data Availability
This is a random simulation model. So, we have not used any data for this proposed scheme.
References
Zhu, C., et al. (2012). A survey on coverage and connectivity issues in wireless sensor networks. Journal of Network and Computer Applications, 35, 619–632.
Wang, B. (2011). Coverage problems in sensor networks: A survey. ACM Computing Surveys (CSUR), 43, 32.
Yu, J., et al. (2016). On connected target k-coverage in heterogeneous wireless sensor networks. Sensors, 16, 104.
Wang, B., et al. (2009). A survey of movement strategies for improving network coverage in wireless sensor networks. Computer Communications, 32, 1427–1436.
Han, G., et al. (2015). Impacts of deployment strategies on localization performance in underwater acoustic sensor networks. IEEE Transactions on Industrial Electronics, 62, 1725–1733.
Xiaoyu, D., et al. (2013). Coverage optimization algorithm based on sampling for 3D underwater sensor networks. International Journal of Distributed Sensor Networks, 9, 478470.
Yang, Q., et al. (2015). Energy-efficient probabilistic area coverage in wireless sensor networks. IEEE Transactions on Vehicular Technology, 64, 367–377.
Yoon, Y., et al. (2013). An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks. IEEE Transactions on Cybernetics, 43, 1473–1483.
Gupta, S. K., et al. (2016). Genetic algorithm approach for k-coverage and m-connected node placement in target based wireless sensor networks. Computers & Electrical Engineering, 56, 544–556.
Xiang, Y., et al. (2016). 3D space detection and coverage of wireless sensor network based on spatial correlation. Journal of Network and Computer Applications, 61, 93–101.
Binh, H. T. T., et al. (2018). Improved cuckoo search and chaotic flower pollination optimization algorithm for maximizing area coverage in wireless sensor networks. Neural Computing and Applications, 30, 2305–2317.
Zhang, Y., et al. (2017). Coverage enhancing of 3D underwater sensor networks based on improved fruit fly optimization algorithm. Soft Computing, 21, 6019–6029.
Wang, Z., et al. (2017). A novel node sinking algorithm for 3D coverage and connectivity in underwater sensor networks. Ad hoc networks, 56, 43–55.
Khoufi, I., et al. (2017). Survey of deployment algorithms in wireless sensor networks: Coverage and connectivity issues and challenges. International Journal of Autonomous and Adaptive Communications Systems, 10, 341–390.
Sandeep, D., et al. (2017). Review on clustering, coverage and connectivity in underwater wireless sensor networks: A communication techniques perspective. IEEE Access, 5, 11176–11199.
Fang, W., et al. (2018). Novel efficient deployment schemes for sensor coverage in mobile wireless sensor networks. Information Fusion, 41, 25–36.
Panag, T. S., et al. (2018). A novel random transition based PSO algorithm to maximize the lifetime of wireless sensor networks. Wireless Personal Communications, 98, 2261–2290.
Panag, T. S., et al. (2019). Maximal coverage hybrid search algorithm for deployment in wireless sensor networks. Wireless Networks, 25, 637–652.
Elhoseny, M., et al. (2018). Optimizing K-coverage of mobile WSNs. Expert Systems with Applications, 92, 142–153.
Gupta, G. P., et al. (2019). Biogeography-based optimization scheme for solving the coverage and connected node placement problem for wireless sensor networks. Wireless Networks, 25, 3167–3177.
Sun, Z., et al. (2017). CASMOC: A novel complex alliance strategy with multi-objective optimization of coverage in wireless sensor networks. Wireless Networks, 23, 1201–1222.
Priyadarshini, R., et al. (2020). Enhancing coverage and connectivity using energy prediction method in underwater acoustic WSN. Journal of Ambient Intelligence and Humanized Computing, 11, 2751–2760.
Yi, J., et al. (2023). Sensor deployment strategies for target coverage problems in underwater acoustic sensor networks. IEEE Communications Letters, 27(3), 836–840.
Chenthil, T. R., et al. (2022). An energy-efficient distributed node clustering routing protocol with mobility pattern support for underwater wireless sensor networks. Wireless Networks, 28(8), 3367–3390.
Chaudhary, M., et al. (2023). Underwater wireless sensor networks: Enabling technologies for node deployment and data collection challenges. IEEE Internet of Things Journal, 10(4), 3500–3524.
Zhang, W., et al. (2021). A coverage vulnerability repair algorithm based on clustering in underwater wireless sensor networks. Mobile Networks and Applications, 26, 1107–1121.
Lv, X., et al. (2017). A node coverage algorithm for a wireless-sensor-network-based water resources monitoring system. Cluster Computing, 20(4), 3061–3070.
Kaya, M. (2011). The effects of two new crossover operators on genetic algorithm performance. Applied Soft Computing, 11(1), 881–890.
Wang, L., et al. (2012). Optimal node placement of industrial wireless sensor networks based on adaptive mutation probability binary particle swarm optimization algorithm. Computer Science and Information Systems, 9(4), 1553–1576.
Deb, K., et al. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6, 182–197.
Li, H., & Zhang, Q. (2009). Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Computation, 13, 284–302.
Bharamagoudra, M. R., et al. (2016). Deployment scheme for enhancing coverage and connectivity in underwater acoustic sensor networks. Wireless Personal Communications, 89, 1265–1293.
Funding
The authors have not disclosed any funding.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no known conflict of interest or personal relationships that could have appeared to influence the work reported in this paper.
Informed Consent
No.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Kumari, S., Mishra, P.K. & Anand, V. Coverage and Connectivity Aware Deployment Scheme for Autonomous Underwater Vehicles in Underwater Wireless Sensor Networks. Wireless Pers Commun 132, 909–931 (2023). https://doi.org/10.1007/s11277-023-10642-7
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-023-10642-7