Cluster Based Localization Scheme with Backup Node in Underwater Wireless Sensor Network

Sahana, Subrata; Singh, Karan

doi:10.1007/s11277-019-06807-y

Cited by 12 publications

(9 citation statements)

References 26 publications

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“…According to the literature survey, most of the existing works on localization and clustering focus on two-dimensional networks in contrast to the requirement of a three-dimensional network which is not only feasible but also practicable in the underwater environment. [22][23][24][25] As a result, the proposed (EELBL-BR) technique implements the deployment of sensor nodes in 3-dimensional UASN 15 to calculate precise localization, followed by clustering to offer an energy-efficient environment to fulfill the necessities of real-time applications. The comparative study of various existing clustering techniques with the proposed algorithm in UASN by authors with respect to various parameters such as clustering techniques, classification of clustering, mobility of nodes, hop-count classification, nodes localization calculation, homogeneous nodes, and energy efficiency are presented in Table 1.…”

Section: Related Workmentioning

confidence: 99%

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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Section: Related Workmentioning

confidence: 99%

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

Yadav,

Khilar

2023

Trans Emerging Tel Tech

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“…The mathematical notation for recent trust is given in Eq. (10). F 4 t ð Þ represents the trust value based on data bytes which is calculated based on total amount of data bytes transmitted from the source node to the total amount of the data bytes received by the terminal node.…”

Section: Trust Value Calculationmentioning

confidence: 99%

“…The transmission capacity of the acoustic channel is low [9]; subsequently the data rate is lower than the communication rate by transmitting the information to buoyant sensors nodes that hand-off the information to close by coastal observing and control station called remote station [10]. Here we use buoyant systems to place the sensors in the ocean.…”

Section: Introductionmentioning

confidence: 99%

Secured Route Selection Using E-ACO in Underwater Wireless Sensor Networks

Deepak¹,

Mukeshkrishnan²

2022

Intelligent Automation &Amp; Soft Computing

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“…e proposed scheme has also been compared with the recent techniques proposed in [36], which form a clustering head as compared to triangles in a region. e bounded clusters are always equipped with anchor nodes over the deployed region.…”

Section: Comparison With Existingmentioning

confidence: 99%

V2X-Based Mobile Localization in 3D Wireless Sensor Network

Javed

Tang

Shaukat

et al. 2021

Security and Communication Networks

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In a wireless sensor network (WSN), node localization is a key requirement for many applications. The concept of mobile anchor-based localization is not a new concept; however, the localization of mobile anchor nodes gains much attention with the advancement in the Internet of Things (IoT) and electronic industry. In this paper, we present a range-free localization algorithm for sensors in a three-dimensional (3D) wireless sensor networks based on flying anchors. The nature of the algorithm is also suitable for vehicle localization as we are using the setup much similar to vehicle-to-infrastructure- (V2I-) based positioning algorithm. A multilayer C-shaped trajectory is chosen for the random walk of mobile anchor nodes equipped with a Global Positioning System (GPS) and broadcasts its location information over the sensing space. The mobile anchor nodes keep transmitting the beacon along with their position information to unknown nodes and select three further anchor nodes to form a triangle. The distance is then computed by the link quality induction against each anchor node that uses the centroid-based formula to compute the localization error. The simulation shows that the average localization error of our proposed system is 1.4 m with a standard deviation of 1.21 m. The geometrical computation of localization eliminated the use of extra hardware that avoids any direct communication between the sensors and is applicable for all types of network topologies.

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Cluster Based Localization Scheme with Backup Node in Underwater Wireless Sensor Network

Cited by 12 publications

References 26 publications

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

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

Secured Route Selection Using E-ACO in Underwater Wireless Sensor Networks

V2X-Based Mobile Localization in 3D Wireless Sensor Network

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