Node Selection Algorithm for Underwater Acoustic Sensor Network Based on Particle Swarm Optimization

Cheng, En; Wu, Longhao; Yuan, Fei; Gao, Chunxian; Yi, Jinwang

doi:10.1109/access.2019.2952169

Cited by 23 publications

(15 citation statements)

References 14 publications

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“…To select SNs in UASN, En Cheng et al 17 proposed PSO algorithm. This paper studies the sound velocity correction factor and performs traditional anchor node selection algorithm.…”

Section: Related Workmentioning

confidence: 99%

Sea lion optimization algorithm based node deployment strategy in underwater acoustic sensor network

Gola

Chaurasia²,

Gupta

et al. 2021

Int J Communication

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Summary In the ocean, huge number of sensor nodes (SNs) are located to transfer the information between other nodes using the Underwater Acoustic Sensor Network (UASN) framework. An underwater acoustic communication technique is utilized by this UASN to exchange the information. Because of environmental conditions and adverse channel, the SNs in UASN may have link breakages. Likewise, maximum target coverage rate for SN deployment is considered as another issue. So it is very essential to create a strong communication system in underwater together with the different kind of variations in ocean environment. As a result, the system will perform better data transmission with the severely fluctuating underwater communication conditions. In this paper, a latest optimization algorithm named as Sea Lion Optimization (SLO) procedure is proposed to discover the optimal location for SN in underwater communication. This algorithm optimally places the acoustic SNs based on the maximum connectivity rate by finding the targeted optimal position. The Matlab tool is utilized for implementation purpose, and the different kinds of parameters like connectivity rate, coverage rate, and delay are taken to evaluate the performance of proposed methodology. Moreover, the existing methods like deployment scheme, Connected Dominating set based depth computation Approach (CDA) approach, and distributive approach are taken to contrast the performance of proposed methodology. When compared to the previous algorithms, our proposed methodology achieves 95% connectivity ratio for varying number of acoustic SNs.

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“…To select SNs in UASN, En Cheng et al 17 proposed PSO algorithm. This paper studies the sound velocity correction factor and performs traditional anchor node selection algorithm.…”

Section: Related Workmentioning

confidence: 99%

Sea lion optimization algorithm based node deployment strategy in underwater acoustic sensor network

Gola

Chaurasia²,

Gupta

et al. 2021

Int J Communication

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“…Among these, the AOA-based localization method requires relatively less data exchange (angle-only) and lower hardware support [10], as it does not rely on the time synchronization. In turn, many recent works show that it appears to be a promising technique in real applications [11][12][13][14][15][16]. The main contributions in this direction are mainly focused on the inaccurate sensor location [11], the angle estimation robustness [12,13], and the time-variant acoustic propagation path [14], and the sensor selection [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…In turn, many recent works show that it appears to be a promising technique in real applications [11][12][13][14][15][16]. The main contributions in this direction are mainly focused on the inaccurate sensor location [11], the angle estimation robustness [12,13], and the time-variant acoustic propagation path [14], and the sensor selection [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…In 3-D space, the sensor selection problem becomes more challenging due to the increased size of the Fisher information matrix (two measurements: the azimuth angle and elevation angle), yet the existing AOA-based sensor selection method for 2-D space is not suitable for 3-D space [25]. Furthermore, currently most of the literature on sensor selection normally assumed that the measurement noises of the sensors are ideally uncorrelated [16][17][18][19][20][21][22]. However, in a dynamic marine environment with a similar acoustic background and sound propagation path, this assumption may not be satisfied [26].…”

Section: Introductionmentioning

confidence: 99%

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

Zhou

Chen

Tan

et al. 2022

JMSE

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This paper investigates a sensor selection scheme for optimal target localization with three-dimensional (3-D) Angle of Arrival (AOA) estimation in Underwater Wireless Sensor Networks (UWSN). Specifically, we present a new 3-D AOA-based localization measurement model, which considers correlated noises and Gaussian priors. The trace of Cramer–Rao lower bound (CRLB) for the 3-D AOA measurement model is derived by introducing a special vector to denote the selected sensors with the azimuth and elevation angle measurements. Based on the presenting expressions of the CRLB, we formulate the sensor selection problem as an optimization problem, which has been transformed into the semidefinite problem program by convex relaxation, and a randomization method is adopted to improve the quality of the SDP solution. Simulation results illustrate that the proposed method receives better estimation performance over the reference methods and approaches the exhaustive search method.

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“…After we have a good model, the general computation costs are dictated by the optimization strategies used for search and the numerical solver utilized for simulation. To address the routing issue, Cuckoo Search Optimization Algorithm with Energy Efficient and QOS Aware (CSOA-EQ) based routing methods have been proposed [7]. A wireless sensor network (WSN) is made up of self-contained sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient and Quality-of-Service Aware Routing using Underwater Wireless Sensor Networks

Sathya¹,

Sengottuvelan²

2022

IJACSA

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In current years, there has been an increasing attention in Underwater wireless sensor networks (UWSNs). Underwater sensor networks (USNs) can be applied for many various purposes. To address the routing issue, the Cuckoo Search Optimization Algorithm with Energy Efficient and QOS Aware (CSOA-EQ) based routing methods have been proposed in this chapter. Every application is important in its own right, but some of them can help improve sea investigation to meet a variety of underwater applications, such as a catastrophic event alert system (such as torrent and seismic monitoring), supported navigation, oceanographic data collection, and underwater surveillance, ecological applications (such as the nature of organic water and contamination monitoring), modern applications (such as marine investigation), and so on. For example, sensors can assess specific metrics, such as base intensity and securing pressure, to monitor the auxiliary nature of the securing environment in offshore engineering applications. UASNs have also improved our understanding of underwater environments, such as climate change, underwater creature life, and the number of inhabitants in coral reefs.

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Node Selection Algorithm for Underwater Acoustic Sensor Network Based on Particle Swarm Optimization

Cited by 23 publications

References 14 publications

Sea lion optimization algorithm based node deployment strategy in underwater acoustic sensor network

Sea lion optimization algorithm based node deployment strategy in underwater acoustic sensor network

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

Energy Efficient and Quality-of-Service Aware Routing using Underwater Wireless Sensor Networks

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