Adaptive Node Clustering for Underwater Sensor Networks

Khan, Muhammad Fahad; Bibi, Muqaddas; Aadil, Farhan; Lee, Jong Weon

doi:10.3390/s21134514

Cited by 20 publications

(13 citation statements)

References 50 publications

(52 reference statements)

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“…They assumed this network scenario was connected to the network. Due to the fact that the underwater sensors are movable due to the water current's velocity of around 1-3 m/s, the topology changes rapidly [25]. The network's assumption might be characterized as follows:…”

Section: System Modelmentioning

confidence: 99%

An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks

Neelakandan¹,

Mohan

Alotaibi

et al. 2022

Sensors

126

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In recent years, the underwater wireless sensor network (UWSN) has received a significant interest among research communities for several applications, such as disaster management, water quality prediction, environmental observance, underwater navigation, etc. The UWSN comprises a massive number of sensors placed in rivers and oceans for observing the underwater environment. However, the underwater sensors are restricted to energy and it is tedious to recharge/replace batteries, resulting in energy efficiency being a major challenge. Clustering and multi-hop routing protocols are considered energy-efficient solutions for UWSN. However, the cluster-based routing protocols for traditional wireless networks could not be feasible for UWSN owing to the underwater current, low bandwidth, high water pressure, propagation delay, and error probability. To resolve these issues and achieve energy efficiency in UWSN, this study focuses on designing the metaheuristics-based clustering with a routing protocol for UWSN, named MCR-UWSN. The goal of the MCR-UWSN technique is to elect an efficient set of cluster heads (CHs) and route to destination. The MCR-UWSN technique involves the designing of cultural emperor penguin optimizer-based clustering (CEPOC) techniques to construct clusters. Besides, the multi-hop routing technique, alongside the grasshopper optimization (MHR-GOA) technique, is derived using multiple input parameters. The performance of the MCR-UWSN technique was validated, and the results are inspected in terms of different measures. The experimental results highlighted an enhanced performance of the MCR-UWSN technique over the recent state-of-art techniques.

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Section: System Modelmentioning

confidence: 99%

An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks

Neelakandan¹,

Mohan

Alotaibi

et al. 2022

Sensors

126

View full text Add to dashboard Cite

show abstract

“…Since the turn of the twentieth century, WSNs have become more prevalent in this research field (Bharany et al, 2021;Khan et al, 2021). Initially, wireless sensor networks (WSNs) were exclusively employed in terrestrial situations.…”

Section: Introductionmentioning

confidence: 99%

“…1. 1 Background et al, 2021;Khan et al, 2021). Sensor nodes are small devices that track and demonstrate physical characteristics and transmit the information to a base station using wireless communication protocols.…”

Section: Introductionmentioning

confidence: 99%

“…Sensor nodes are small devices that track and demonstrate physical characteristics and transmit the information to a base station using wireless communication protocols. Radiofrequency waves are not suggested in underwater sensor networks because their signal attenuation is excessive and limited in distance range (Awan et al, 2019;Khan et al, 2021;Nguyen et al, 2021). Due to the limited capacity and frequency of the UW channel, its bandwidth is significantly smaller than that of a conventional radio channel.…”

Section: Introductionmentioning

confidence: 99%

“…Differentiating from the terrestrial environment, the marine environment is far more complicated and variable, making ocean monitoring more challenging. A UWSN is an ad-hoc network designed for underwater communication and data transmission in which several surface stations are required for better communication (Awan et al, 2019;Bharany et al, 2021;Khan et al, 2021;Nguyen et al, 2021) so that Sensor nodes can easily transmit and receive data between a source and a destination.…”

Section: Introductionmentioning

confidence: 99%

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Energy-efficient clustering protocol for underwater wireless sensor networks using optimized glowworm swarm optimization

et al. 2023

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In the past few decades, cutting-edge information and communication technology has been used in several ways to keep an eye on the marine environment. Underwater wireless sensor networks (UWSNs) can measure the amount of water and soil conditions, such as soil salinity, moisture, and movements, to predict landslides. UWSNs are made up of many wireless underwater sensor nodes (WSNs) that are spread out across the thalassic environment. These networks have several uses, including data collection, navigation, resource analysis, surveillance, disaster prediction, etc. Nowadays, energy efficiency becomes a complex issue to handle in the design of the UWSN due to the limited battery capacity and the challenges associated with changing or charging the integrated batteries. According to previous research, clustering and routing have already been effective methods of improving energy efficiency in the UWSN, as unreplaceable batteries and long-distance communication delays are particularly vulnerable. As a result, one of the UWSN’s critical issues is determining how to extend the network’s lifespan while balancing its energy consumption and shortening transmission distances. In UWSN clustering, the most important considerations are acquiring a suitable count of clusters, constituting the clusters, and picking the most satisfactory cluster head (CH) for each cluster. Based on several factors, such as residuary energy, total energy consumption, and other considerations, our proposed approach picks CHs and arranges them into clusters. Also, the proposed SS-GSO method constructs a fitness function by including various sources of information, like total energy, residual energy, and luciferin value. Several simulation runs were executed to test how much better the SS-GSO approach worked. The comparison results showed that while evaluating clustering time, our proposed SS-GSO technique performs 22.91%, 50.03%, 42.42%, 58.06% better, in case of Total energy consumption 27.02%,14%,33.76%,41.97% more energy efficient, in Cluster lifetime 9.2%,19.88%,35.91%,40.54% less and in Packet delivery rate 8.29%,14.05%,17.67%,23.97% better as compared with other heuristic techniques, such as ACO, GWO, MFO and LEACH.

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UASN‐3D: An energy efficient localization based on LEACH‐BR algorithm (EELBL‐BR)

Yadav,

Khilar

2023

Trans Emerging Tel Tech

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Underwater acoustic sensor networks (UASN) research is gaining popularity as it has various applications such as military communication, oil rig maintenance, animal survival, linking submarines to land, gathering data for water monitoring, and other commercial fields. The two most critical requirements for the application's proper operation are the accurate knowledge of sensor node locations and the efficient transmission of accurate underwater sensor node information to the base station with efficient energy consumption. The proposed energy efficient localization based on the LEACH‐beacon and reinforced node (EELBL‐BR) algorithm satisfies both the requirements in 3D‐UASN. The proposed algorithm considers the deployment and computation of accurate location of sensor nodes in the underwater environment by applying I‐LASP (improvement of localization algorithm for compensating stratification effect based on extended improved particle swarm optimization technique) (Yadav N, Khilar PM. Trans Emerg Telecommun Technol. 2023;34:e4772.) for 3D environment. It performs clustering of sensor nodes for enhancing network lifetime using three different types of nodes such as beacon, reinforced, and member nodes. The proposed clustering LEACH‐BR (low‐energy adaptive clustering hierarchy‐beacon and reinforced nodes) algorithm is based upon the LEACH algorithm which provides accurate location of all the sensor nodes, improves energy consumption and reliability in the underwater environment. The result shows that the proposed algorithm EELBL‐BR, considering both beacon and reinforced nodes, provides the improvement in the number of alive nodes, reduction in the number of dead nodes, reduction in energy consumption and enhances residual energy in the UASN by 68.90%, 51.91%, 51.47%, and 68.12% respectively with respect to the number of rounds as compared to that of the existing algorithm by Rizvi et al. (Wirel Pers Commun. 2022;124(4):3725–3741.) and thus outperforms the existing algorithm (Rizvi HH, Khan SA, Enam RN. Wirel Pers Commun. 2022;124(4):3725–3741.).

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Adaptive Node Clustering for Underwater Sensor Networks

Cited by 20 publications

References 50 publications

An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks

An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks

Energy-efficient clustering protocol for underwater wireless sensor networks using optimized glowworm swarm optimization

UASN‐3D: An energy efficient localization based on LEACH‐BR algorithm (EELBL‐BR)

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