Recent advances in sensors technology have led to design and development of variety of geosensor networks and their applications in many disciplines. Such networks consist of a set of sensors laying on different locations and sensing various real world phenomena for environmental monitoring, object surveillance, tracking and controlling applications. A fundamental issue in a geosensor network optimization is estimation of its spatial coverage. The existence of various obstacles in the sensing environment and its complexity result in several holes in the environment. These holes should be detected and minimized in the optimization process. Different approaches have been proposed in the literatures to resolve these problems. A considerable number of current approaches use Voronoi diagram and Delaunay triangulation to identify the holes in the network and deploy an optimal arrangement for the sensors. These structures are more compatible with the spatial distribution of sensors in the environment. This paper presents a survey of the existing solutions for geosensor network optimization that use Voronoi diagram and Delaunay triangulation in their approach.

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.