Sensor Selection for Optimal Target localization with 3-D Angle of Arrival Estimation in Underwater Wireless Sensor Networks

Zhou, Rongyan; Chen, Jianfeng; Tan, Weijie; Cai, Chang

doi:10.3390/jmse10020245

Cited by 18 publications

(18 citation statements)

References 30 publications

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“…The CPU or control collects the sensor's data, stores them in memory, analyzes them, and then sends them via an acoustic modem to other sensors is shown in the Figure 3. Occasionally, bottom-mounted instrument frames are designed to authorize unidirectional messages to protect all sensor components from potential trawling gear damage [12].…”

Section: Underwater Sensor's Internal Structurementioning

confidence: 99%

“…Telecom 2023, 4, FOR PEER REVIEW bottom-mounted instrument frames are designed to authorize unidirectional message protect all sensor components from potential trawling gear damage [12]. Submerged acoustic message channels are centrally influenced by factors such as ter heat, commotion, multi-path, Doppler spread, and sign lessening.…”

Section: Underwater Sensor's Internal Structurementioning

confidence: 99%

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Review of Localization and Clustering in USV and AUV for Underwater Wireless Sensor Networks

Sathish

Venkata

Anbazhagan

et al. 2023

Telecom

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Oceanographic data collection, disaster prevention, aided navigation, critical observation sub-missions, contaminant screening, and seaward scanning are just a few of the submissions that use underwater sensor hubs. Unmanned submerged vehicles (USVs) or autonomous acoustic underwater vehicles (AUVs) through sensors would similarly be able to explore unique underwater resources and gather data when utilized in conjunction with integrated screen operations. The most advanced technological method of oceanic observation is wireless information routing beneath the ocean or generally underwater. Water bottoms are typically observed using oceanographic sensors that collect data at certain ocean zones. Most research on UWSNs focuses on physical levels, even though the localization level, such as guiding processes, is a more recent zone. Analyzing the presenting metrics of the current direction conventions for UWSNs is crucial for considering additional enhancements in a procedure employing underwater wireless sensor networks for locating sensors (UWSNs). Due to their severely constrained propagation, radio frequency (RF) transmissions are inappropriate for underwater environments. This makes it difficult to maintain network connectivity and localization. This provided a plan for employing adequate reliability and improved communication and is used to locate the node exactly using a variety of methods. In order to minimize inaccuracies, specific techniques are utilized to calculate the distance to the destination. It has a variety of qualities, such as limited bandwidth, high latency, low energy, and a high error probability. Both nodes enable technical professionals stationed on land to communicate data from the chosen oceanic zones rapidly. This study investigates the significance, uses, network architecture, requirements, and difficulties of undersea sensors.

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Section: Underwater Sensor's Internal Structurementioning

confidence: 99%

Section: Underwater Sensor's Internal Structurementioning

confidence: 99%

Review of Localization and Clustering in USV and AUV for Underwater Wireless Sensor Networks

Sathish

Venkata

Anbazhagan

et al. 2023

Telecom

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“…In Table 2, we have compared the existing underwater localization with respect to various parameters, and Table 3 presents comparisons of the existing underwater localization algorithms with respect to the proposed algorithm [I-LASP]. algorithms 15,25,[27][28][29][30] and then mentioned the limitations:…”

Section: Related Workmentioning

confidence: 99%

An efficient 3D localization algorithm for compensating stratification effect in underwater acoustic sensor network

Yadav

Khilar

2023

Trans Emerging Tel Tech

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Communication and networking plays a crucial role in data transmission in UASN. The sensor nodes in UASN are intermittently deployed randomly in the three‐dimension scenario. As a consequence, the three‐dimension based localization algorithm is the need for an hour. To fulfill the objective, we have proposed a localization algorithm I‐LASP (Improvement of localization algorithm for compensating stratification effect based on extended improved particle swarm optimization technique) in three‐dimensional UASN, based on compensation of stratification effect for the improvement of the performance parameters such as localization accuracy, ranging accuracy, convergence rate and execution time. To compute the accurate position of target nodes, EIPSO (Extended improved PSO) technique is applied, and the degree of coplanarity is checked before the calculation of distance among nodes in order to get the accurate location of target nodes. The Centroid method is used to initialize the position of sensor nodes, and the ray theory method is used to compensate the stratification effect on the layered ocean water. The proposed algorithm is compared to the existing LASP, Std PSO, and GNA‐ESSP (Gauss‐newton algorithm‐extended sound speed profile) localization algorithm. The proposed algorithm provides 34.50%, 38.87%, and 42.66% of high accuracy in terms of localization with low density of target sensor nodes and 37.96%, 29.58%, and 50.77% high accuracy in terms of localization with a high density of target sensor nodes respectively. The proposed algorithm is compared with LASP, GNA‐ESSP, and TDOA to obtain 66.84%, 71.14%, and 86.13% of high accuracy in terms of ranging with low density of target sensor nodes and 42.34%, 89.00%, and 95.08% high accuracy in terms of ranging with a high density of target sensor nodes respectively. Experimental results represents that the proposed algorithm obtains better performance in terms of localization accuracy, ranging accuracy, root mean square error, normalized localization error, execution time and convergence rate.

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“…Currently, there are several approaches and algorithms employed for WSN localization. These include angle-of-arrival (AOA) estimation, received signal strength indicator (RSSI) methods, time difference of arrival (TDOA) algorithms, and the Channel State Information (CSI) approach [2,3,4,5]. AOA methods estimate the location of a node by measuring the angle at which a radio signal is received.…”

Section: Introductionmentioning

confidence: 99%

Improved Tree Dimensional DV-Hop Protocol for Large Scale Range-Free Wireless Sensors Network

Liouane

Amdouni

Hajjaji

et al. 2023

Preprint

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Location plays a crucial role in many applications of Wireless Sensor Networks (WSNs), and accurate sensor localization is an important aspect of the acquired data. While connectivity algorithms are commonly used for localizing multi-hop WSNs because their simplicity and acceptable accuracy, their effectiveness can be limited in two-dimensional (2D) or three-dimensional (3D) environments. An analytic model that incorporates hop size quantization and the Recursive Least Squares (RLS) method can be advantageous for Range-Free 3D wireless sensor networks (WSNs) in localization. This approach reduces computational complexity, memory requirements, and localization errors. The third dimension significantly impacts localization accuracy, necessitating the development of effective self-localization algorithms for 3D WSNs. This article introduces a novel probabilistic quantization technique for hop sizes in 3D-WSNs, specifically designed to address the uniform distribution of sensor nodes. The RLS method is employed as an adaptive filtering algorithm to recursively estimate the positions of sensor nodes in the system by minimizing the sum of squared errors between actual measured values and predicted values. Through extensive simulations conducted in isotropic settings under various conditions, the proposed algorithms are evaluated based on their average localization error performance. The simulation data clearly demonstrate that the suggested localization algorithm outperforms previous 3D-DV-Hop heuristics in terms of accuracy. The proposed localization method for 3D-WSNs successfully decreases the average localization error of nodes and achieves superior location accuracy when utilizing predicted hop quantization for hop-size estimation and the RLS algorithm for position estimation compared to competing approaches.

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Sensor Selection for Optimal Target localization with 3-D Angle of Arrival Estimation in Underwater Wireless Sensor Networks

Cited by 18 publications

References 30 publications

Review of Localization and Clustering in USV and AUV for Underwater Wireless Sensor Networks

Review of Localization and Clustering in USV and AUV for Underwater Wireless Sensor Networks

An efficient 3D localization algorithm for compensating stratification effect in underwater acoustic sensor network

Improved Tree Dimensional DV-Hop Protocol for Large Scale Range-Free Wireless Sensors Network

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