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

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.

A Survey of Machine Learning for Information Processing and Networking

“…[20] provide a fuzzy deployment strategy that is only suitable for field observation. Near-optimal sensor deployment models that optimize information acquisition while minimizing communication costs are introduced by the authors of [21] irrespective of the application. This concept may be used for a variety of WSN applications, not only monitoring.…”

A Local 3D Voronoi-Based Optimization Method for Sensor Network Deployment in Complex Indoor Environments

“…To show that our proposed algorithm reached an optimal coverage, we compared our algorithm with the Genetic Algorithm (GA) and with the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) as two examples of global methods [34]. To implement According to Table 1, it can be concluded that the computational time of the 3D Voronoi approach was six times less than the computational time of the GA and CMA-ES algorithms.…”

“…To show that our proposed algorithm reached an optimal coverage, we compared our algorithm with the Genetic Algorithm (GA) and with the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) as two examples of global methods [ 34 ]. To implement the GA and CMA-ES for the deployment of the same set of cameras on the ceilings or walls, it was necessary to prepare the algorithms for the optimization process.…”

Optimal Placement of Roadside Infrastructure Sensors towards Safer Autonomous Vehicle Deployments

“…Research efforts in wireless sensor networks (WSNs) have also studied how to model, simulate and maximize the coverage of a WSN. In [10], Argany et al proposes an environment framework using a geographic CityGML model of a region which is then converted to either a raster or vector format for estimating sensor coverage. They estimate sensor coverage by quantifying the number of tiles on the raster map which can be 'seen' by each sensor.…”