Nodes deployment optimization algorithm based on improved evidence theory of underwater wireless sensor networks

Song, Xiaoli; Gong, Yi; Jin, Dahai; Li, Qiangyi

doi:10.1007/s11107-018-0807-3

Cited by 29 publications

(15 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As mentioned above, in recent years, research on UWSNs mainly involves topics like node deployment [ 7 , 8 , 9 , 10 ], node localization [ 11 ], network protocol design [ 3 , 12 ], simulation [ 13 , 14 , 15 ], etc. Specially, the issue of node deployment has attracted attention from lots of scholars and engineers.…”

Section: Related Researchmentioning

confidence: 99%

“…In recent years, acoustic-based UWSNs have been applied in lots of fields such as environment monitoring, military surveillance, data collection, etc [4][5][6]. Research on UWSNs mainly involves node deployment [7][8][9][10], node localization [11], network protocol design [3,12], simulation [13][14][15], etc. Deployment of sensor nodes in 3D UWSNs is a crucial issue, which directly affects the performance of networks.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Yan

Huangfu

2021

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Related Researchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Yan

Huangfu

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…In these works, they mainly study sensor deployment under 2D space. In [ 18 ], Song X. and Gong Y. et al proposed a 3D node deployment algorithm for underwater sensor networks. The proposed algorithm can achieve a large coverage area with the minimal number of nodes.…”

Section: Related Workmentioning

confidence: 99%

Node Deployment of Marine Monitoring Networks: A Multiobjective Optimization Scheme

Duan

Lin

Cai

et al. 2020

Sensors

View full text Add to dashboard Cite

The increasing demands for real-time marine monitoring call for the wide deployment of Marine Monitoring Networks (MMNs). The low-rate underwater communications over a long distance, long propagation delay of underwater acoustic channel, and high deployment costs of marine sensors in a large-scale three-dimensional space bring great challenges in the network deployment and management of MMN. In this paper, we first propose a multitier, hierarchical network architecture of MMN with the support of edge computing (HMMN-EC) to enable efficient monitoring services in a harsh marine environment, taking into consideration the salient features of marine communications. Specifically, HMMN-EC is composed of three subnetworks, i.e., underwater acoustic subnetwork, the sea-surface wireless subnetwork, and the air wireless subnetwork, with a diversity of network nodes with different capabilities. We then jointly investigate the deployment diverse network nodes with various constraints in different subnetworks of HMMN-EC. To this end, we formulate a Multiobjective Optimization (MO) problem to minimize the network deployment cost while achieving the maximal network lifetime, subject to the limited energy of different marine nodes and the complex deployment environment. To solve the formulated problem, we present an Ant-Colony-based Efficient Topology Optimization (AC-ETO) algorithm to find the optimal locations of nodes in different subnetworks of MMN in a large-scale deployment. The time complexity of the proposed algorithm is also analyzed. Finally, extensive simulations are carried out to validate the superior performance of the proposed algorithm compared with some existing solutions.

show abstract

“…As a consequence, various types of new researches for UWSN have been performed, such as the development of autonomous underwater vehicles (AUV), upgradation of the deployment strategy of sensors, and localization techniques for the underwater sensor. With the aim of covering blind areas, a data fusion model based on organic small molecules is proposed in Song et al, 9 and an algorithm for the deployment of 3D underwater sensor network using passive sonar probability perception model and enhanced data fusion model has been presented in Song et al 10 An approach has been proposed in Senel et al 11 where after the random deployment of the sensors over the sea surface as 2D network, it is stretched to form 3D network while maintaining connectivity with maximum coverage, and different deployment strategies have been presented in previous studies. [12][13][14] The connectivity and coverage of a network have been focused in the literature, 12,15 a number of the activate nodes have been estimated in Chowdhury et al, 16 and the main concern of Anower et al 4 is to mitigate the multipath effect of underwater communication.…”

Section: Introductionmentioning

confidence: 99%

Dimensionality determination of unknown deployed underwater sensor network (UWSN) using cost function

Afrin

Anower

Islam

2020

Int J Communication

View full text Add to dashboard Cite

Summary This paper proposes a novel estimation technique for dimensionality of deployed underwater sensor network (UWSN). In order to obtain the dimension, probing requests are sent from two anchor nodes (i.e., receivers), which are placed inside the network through buoy, to sensor nodes. Then, the sensor nodes transmit Gaussian signal as feedback to the anchor nodes. The cross‐correlation function (CCF) is determined of these Gaussian signals, which varies according to network dimension. As reference, analytical CCF of five reference networks of different dimensions is determined. Thereafter, by comparing the CCFs of unknown and reference UWSN, cost function (CF) is obtained. The lowest value of CF attained by the specific combination of the one reference and the unknown network indicates that the dimension of these networks is identical. The dimension of two unknown deployed networks is investigated to demonstrate the feasibility of the proposed technique. From the illustrated results, it is justified that the new strategy can provide correct information about the dimension of the deployed network.

show abstract

Nodes deployment optimization algorithm based on improved evidence theory of underwater wireless sensor networks

Cited by 29 publications

References 20 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

Node Deployment of Marine Monitoring Networks: A Multiobjective Optimization Scheme

Dimensionality determination of unknown deployed underwater sensor network (UWSN) using cost function

Contact Info

Product

Resources

About