A Voronoi-Based Optimized Depth Adjustment Deployment Scheme for Underwater Acoustic Sensor Networks

Su, Yishan; Guo, Lei; Jin, Zhigang; Fu, Xiaomei

doi:10.1109/jsen.2020.3005685

Cited by 39 publications

(14 citation statements)

References 21 publications

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“…Su et al [ 29 ] proposed a Voronoi-based Optimized Depth Adjustment (VODA) deployment scheme to deploy sensor nodes in a target water space. This algorithm has the goal of coverage and connectivity joint optimization.…”

Section: Related Researchmentioning

confidence: 99%

“…These factors can decrease the network performance. This problem is addressed with the method described in [ 29 ] in this paper.…”

Section: Description Of the Proposed Algorithmmentioning

confidence: 99%

“…The simulation experiments are performed on a computer equipped with an Intel Core i7-8550U 1.8 GHz CPU and 8.0 G RAM. GRSUNDSOA is compared with four approaches— Cacc [ 28 ], CDA [ 26 , 27 ], VODA [ 29 ]—and the random based deployment approach in this simulation. To fully verify the algorithms performance, each simulation is conducted 20 times.…”

Section: Algorithm Simulation and Analysismentioning

confidence: 99%

“…This algorithm strives to obtain the optimal network coverage rate on the basis of ensuring energy consumption balance and connectivity. Different from the recent approaches in the literature such as CDA [ 26 , 27 ], Cacc [ 28 ], VODA [ 29 ], and DODA [ 30 ], the proposed algorithm searches the global optimal dive position based on the basic nodes for depth calculation rather than the local optimal dive position. Furthermore, the proposed depth calculation method is with the goal of comprehensive optimization of both maximizing coverage utilization and energy balance, rather than just coverage only.…”

Section: Introductionmentioning

confidence: 99%

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An Uneven Node Self-Deployment Optimization Algorithm for Maximized Coverage and Energy Balance in Underwater Wireless Sensor Networks

Yan

Huangfu

2021

Sensors

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The underwater wireless sensor networks (UWSNs) have been applied in lots of fields such as environment monitoring, military surveillance, data collection, etc. Deployment of sensor nodes in 3D UWSNs is a crucial issue, however, it is a challenging problem due to the complex underwater environment. This paper proposes a growth ring style uneven node depth-adjustment self-deployment optimization algorithm (GRSUNDSOA) to improve the coverage and reliability of UWSNs, meanwhile, and to solve the problem of energy holes. In detail, a growth ring style-based scheme is proposed for constructing the connective tree structure of sensor nodes and a global optimal depth-adjustment algorithm with the goal of comprehensive optimization of both maximizing coverage utilization and energy balance is proposed. Initially, the nodes are scattered to the water surface to form a connected network on this 2D plane. Then, starting from sink node, a growth ring style increment strategy is presented to organize the common nodes as tree structures and each root of subtree is determined. Meanwhile, with the goal of global maximizing coverage utilization and energy balance, all nodes depths are computed iteratively. Finally, all the nodes dive to the computed position once and a 3D underwater connected network with non-uniform distribution and balanced energy is constructed. A series of simulation experiments are performed. The simulation results show that the coverage and reliability of UWSN are improved greatly under the condition of full connectivity and energy balance, and the issue of energy hole can be avoided effectively. Therefore, GRSUNDSOA can prolong the lifetime of UWSN significantly.

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Section: Related Researchmentioning

confidence: 99%

“…These factors can decrease the network performance. This problem is addressed with the method described in [ 29 ] in this paper.…”

Section: Description Of the Proposed Algorithmmentioning

confidence: 99%

Section: Algorithm Simulation and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Uneven Node Self-Deployment Optimization Algorithm for Maximized Coverage and Energy Balance in Underwater Wireless Sensor Networks

Yan

Huangfu

2021

Sensors

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“…Prediction or measurement of the ocean environment and water column parameters need efficient methods. However, algorithms such as VODA [1], RVPR [2], glider-assist routing [3], DVOR [4], DQELR [5], RCAR [6], DSR-SDN [7], EBOR [8] developed for efficient routing in different ocean environmental conditions, whereas for water column variations such as geometric spread, sedimentation drift, Doppler effect few UASN algorithms proposed such as COCAN, LOCAN [9]. In UASN, acoustic signal performs better than extreme Low frequencies (ELF) due to less attenuation in underwater.…”

Section: Introductionmentioning

confidence: 99%

Modified Mackenzie Sound Equation and CVOA Algorithm Based UASN Routing in Dissolved Gases with Divergent Wind Environment to Reduce Propagation Delay

Amirthavalli¹,

Shanker²

2021

Preprint

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In Underwater Acoustic Sensor Network (UASN), routing and propagation delay is affected through various water column environment effects in each node such as temperature, salinity, depth, gases, divergent and rotational wind. High sound velocity increases the transmission rate of packets and high dissolved gases in the water environment increase sound velocity. High dissolved gases and sound velocity environment in water column provide high transmission rates among UASN nodes. In this paper, the Modified Mackenzie Sound equation calculates sound velocity in each node for energy-efficient routing. Golden Ratio Optimization Method (GROM) and Gaussian Process Regression (GPR) predict propagation delay of each node in UASN from dataset which consists of Mackenzie Sound equation calculated sound velocity based on temperature, salinity, depth, dissolved gases. Dissolved gases, rotational and divergent winds, and stress play major problems in UASN, increases propagation delay and energy consumption. Predicted values from GPR and GROM lead to node selection and among selected nodes Corona Virus Optimization Algorithm (CVOA) routing is performed. The proposed GPR-CVOA and GROM-CVOA algorithm solves the problem of propagation delay and consumes less energy in nodes based on appropriate tolerant delays in transmitting packets among nodes during high rotational and divergent winds. From simulation results, CVOA Algorithm performs better than traditional DF and LION algorithms.

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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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A Voronoi-Based Optimized Depth Adjustment Deployment Scheme for Underwater Acoustic Sensor Networks

Cited by 39 publications

References 21 publications

An Uneven Node Self-Deployment Optimization Algorithm for Maximized Coverage and Energy Balance in Underwater Wireless Sensor Networks

An Uneven Node Self-Deployment Optimization Algorithm for Maximized Coverage and Energy Balance in Underwater Wireless Sensor Networks

Modified Mackenzie Sound Equation and CVOA Algorithm Based UASN Routing in Dissolved Gases with Divergent Wind Environment to Reduce Propagation Delay

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

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