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J. Sens. Actuator Netw. 2018, 7(2), 20; https://doi.org/10.3390/jsan7020020

Optimization of Wireless Sensor Networks Deployment Based on Probabilistic Sensing Models in a Complex Environment

1
Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 1417853933, Iran
2
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1439957131, Iran
3
Department of Geomatics Engineering, Kerman Graduate University of Advanced Technology, Kerman 7631818356, Iran
4
Center for Research in Geomatics, Department of Geomatics, Université Laval, Quebec, QC G1V 0A6, Canada
*
Author to whom correspondence should be addressed.
Received: 4 April 2018 / Revised: 4 May 2018 / Accepted: 7 May 2018 / Published: 9 May 2018
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Abstract

In recent years, wireless sensor networks have been studied in numerous cases. One of the important problems studied in these networks is the optimal deployment of sensors to obtain the maximum of coverage. Hence, in most studies, optimization algorithms have been used to achieve the maximum coverage. Optimization algorithms are divided into two groups of local and global optimization algorithms. Global algorithms generally use a random method based on an evolutionary process. In most of the conducted research, the environment model and, sometimes, the layout of sensors in the network have been considered in a very simplified form. In this research, by raster and vector modeling of the environment in two- and three-dimensional spaces, the function of global optimization algorithms was compared and assessed for optimal deployment of sensors and a vector environment model was used as a more accurate model. Since the purpose of this paper is to compare the performance and results of global algorithms, the studied region and the implementation conditions considered are the same for all applied algorithms. In this article, some optimization methods are considered for sensor deployment including genetic algorithms, L-BFGS, VFCPSO and CMA-ES, and the implementation and assessment criteria of algorithms for deployment of wireless sensor network are considered some factors such as the optimal coverage amount, their coverage accuracy towards the environment model and convergence speed of the algorithms. On the other hand, in this paper, the probability coverage model is implemented for each of the global optimization algorithms. The results of these implementations show that the presence of more complex parameters in environment model and coverage produce accurate results that are more consistent with reality. Nonetheless, it may reduce the time efficiency of algorithms. View Full-Text
Keywords: wireless sensor networks; sensor deployment; network coverage; global optimization algorithms; probability coverage model; raster model; vector model wireless sensor networks; sensor deployment; network coverage; global optimization algorithms; probability coverage model; raster model; vector model
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Argany, M.; Karimipour, F.; Mafi, F.; Afghantoloee, A. Optimization of Wireless Sensor Networks Deployment Based on Probabilistic Sensing Models in a Complex Environment. J. Sens. Actuator Netw. 2018, 7, 20.

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