An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network

Wang, Hui; Li, Youming; Chang, Ting-Cheng; Chang, Shengming

doi:10.3390/s18082512

Cited by 24 publications

(15 citation statements)

References 29 publications

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“…We later introduce a Memetic Algorithm (MA) for the node location problem in the localization field for the first time in the authors’ best knowledge with the characterization of time and noise uncertainties of LOS and NLOS conditions. There exist some previous studies of the application of the MA to the optimization of Wireless Sensor Networks (WSN) [ 43 , 44 , 45 , 46 ], but these studies are focused on the coverage among the system nodes.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Memetic Algorithm for the Node Location Problem in Local Positioning Systems

Díez-González

Verde

Ferrero-Guillén

et al. 2020

Sensors

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Local Positioning Systems (LPS) have shown excellent performance for applications that demand high accuracy. They rely on ad-hoc node deployments which fit the environment characteristics in order to reduce the system uncertainties. The obtainment of competitive results through these systems requires the solution of the Node Location Problem (finding the optimal cartesian coordinates of the architecture sensors). This problem has been assigned as NP-Hard, therefore a heuristic solution is recommended for addressing this complex problem. Genetic Algorithms (GA) have shown an excellent trade-off between diversification and intensification in the literature. However, in Non-Line-of-Sight (NLOS) environments in which there is not continuity in the fitness function evaluation of a particular node distribution among contiguous solutions, challenges arise for the GA during the exploration of new potential regions of the space of solutions. Consequently, in this paper, we first propose a Hybrid GA with a combination of the GA operators in the evolutionary process for the Node Location Problem. Later, we introduce a Memetic Algorithm (MA) with a Local Search (LS) strategy for exploring the most different individuals of the population in search of improving the previous results. Finally, we combine the Hybrid Genetic Algorithm (HGA) and Memetic Algorithm (MA), designing an enhanced novel methodology for solving the Node Location Problem, a Hybrid Memetic Algorithm (HMA). Results show that the HMA proposed in this article outperforms all of the individual configurations presented and attains an improvement of 14.2% in accuracy for the Node Location Problem solution in the scenario of simulations with regards to the previous GA optimizations of the literature.

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Section: Introductionmentioning

confidence: 99%

Hybrid Memetic Algorithm for the Node Location Problem in Local Positioning Systems

Díez-González

Verde

Ferrero-Guillén

et al. 2020

Sensors

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“…After data integration and other forms of information processing, underwater acoustic communication is used to transmit real-time monitoring information to a sink node on the water surface before a sink on the water surface transmits the real-time information to observers. Because underwater nodes are often deployed in large areas of water that are sparsely populated by humans, the expensive batteries are not easily changed [6][7][8], and environmental damage causes frequent accidental node failures. Therefore, practical deployment algorithms must simultaneously consider coverage and connectivity [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Self-Deployment Algorithm for Maintaining Maximum Coverage and Connectivity in Underwater Acoustic Sensor Networks Based on an Ant Colony Optimization

Wang

Li-liang

et al. 2019

Applied Sciences

Self Cite

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The self-deployment of nodes with non-uniform coverage in underwater acoustic sensor networks (UASNs) is challenging because it is difficult to access the three-dimensional underwater environment. The problem is further complicated if network connectivity needs to be considered. In order to solve the optimization problem of sensor network node deployment, we propose a maximum coverage and connectivity self-deployment algorithm that is based on ant colony optimization (MCC-ACO). We carry out the greedy strategy, improve the path selection probability and pheromone update system, and propose a self-deployment algorithm based on the foundation of standard ant colony optimization algorithms, so as to achieve energy-saving optimization coverage of target events. The main characteristic of the MCC-ACO algorithm is that it fully considers the effects of the changes in the event quantities and the random distribution of the nodes on the deployment effect of the nodes, ensures that every deployed node can be connected to the sink, and achieves the matching of node distribution density and event distribution. Therefore, the MCC-ACO algorithm has great practical value. A large number of comparative simulation experiments show that the algorithm can effectively realize the self-deployment problem of underwater sensor nodes. In addition, the paper also gives the impact of changes in the number of events in the network on the deployment effect.

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“…However, this method does not guarantee sufficient coverage of the ROI, especially when sensors nodes adopt a configuration with small concentrations in specific locations of the service area. Since the coverage performance of a WSN depends on the spatial arrangement of the sensor nodes, techniques to properly distribute sensor nodes had been extensively studied and developed in the last few years; as a result, there are plenty of works on the literature devoted to this subject [8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A Real-Coded Optimal Sensor Deployment Scheme for Wireless Sensor Networks Based on the Social Spider Optimization Algorithm

Fausto¹,

Cuevas²,

Maciel-Castillo³

et al. 2019

IJCIS

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A B S T R A C TWireless sensor networks (WSNs) involves a set of wireless sensor nodes located within a region of interest (ROI) to acquire and/or transmit specific information from their surroundings. A common problem in the operation of WSNs is sensor coverage, which is related to the distribution of sensor nodes within their ROI. Several approaches have been proposed to solve this problem; however, most of these methods consider a simplified arrangement scheme based on sensor placement over a set of fixed discrete locations defined by a grid. This fact severally limits the ability of these methods to find potentially better solutions as they are conditioned to select a limited number of candidate solutions. In this paper, a real-coded sensor deployment approach based on the Social Spider Optimization (SSO) algorithm is proposed to solve the problem of optimal sensor deployment (OSD) in WSNs. The performance of our proposed approach (referred in this paper as real-coded SSO [R-SSO]) was also compared against other metaheuristics-based methods used in the literature. Experimental results demonstrate its ability to solve the problem of OSD in terms of accuracy and robustness.

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An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network

Cited by 24 publications

References 29 publications

Hybrid Memetic Algorithm for the Node Location Problem in Local Positioning Systems

Hybrid Memetic Algorithm for the Node Location Problem in Local Positioning Systems

A Self-Deployment Algorithm for Maintaining Maximum Coverage and Connectivity in Underwater Acoustic Sensor Networks Based on an Ant Colony Optimization

A Real-Coded Optimal Sensor Deployment Scheme for Wireless Sensor Networks Based on the Social Spider Optimization Algorithm

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