Accurate prediction of longitudinal dispersion coefficient (K) is a key element in studying of pollutant transport in rivers when the full cross sectional mixing has occurred. In this regard, several research studies have been carried out and different equations have been proposed. The predicted values of K obtained by different equations showed a great amount of uncertainty due to the complexity of the phenomenon. Therefore, there is still a need to make an improvement on the existing predictive models. In this study, a multi-objective particle swarm optimization (PSO) technique was used to derive new equations for predicting longitudinal dispersion coefficient in natural rivers. To do this, extensive field data, including hydraulic and geometrical characteristics of different rivers were applied. The results of this study were compared with those of the previous studies using the statistical error measures. The comparison revealed that the proposed model is superior to the previous ones. According to this study, PSO algorithm can be applied to improve the performance of the predictive equations by finding optimum values of the coefficients. The proposed model can be successfully applied to estimate the longitudinal dispersion coefficient for a wide range of rivers' characteristics.
ABSTRACT:Sensor deployment optimization to achieve the maximum spatial coverage is one of the main issues in Wireless geoSensor Networks (WSN). The model of the environment is an imperative parameter that influences the accuracy of geosensor coverage. In most of recent studies, the environment has been modeled by Digital Surface Model (DSM). However, the advances in technology to collect 3D vector data at different levels, especially in urban models can enhance the quality of geosensor deployment in order to achieve more accurate coverage estimations. This paper proposes an approach to calculate the geosensor coverage in 3D vector environments. The approach is applied on some case studies and compared with DSM based methods.
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.
KEY WORDS: Geosensor networks deployment, Network coverage problem, Voronoi based optimization algorithm ABSTRACT:Recent advances in electrical, mechanical and communication systems have led to development of efficient low-cost and multifunction geosensor networks. The efficiency of a geosensor network is significantly based on network coverage, which is the result of network deployment. Several optimization methods have been proposed to enhance the deployment efficiency and hence increase the coverage, but most of them considered the problem in the 2D environment models, which is usually far from the real situation. This paper extends a Voronoi-based deployment algorithm to 3D environment, which takes the 3D features into account. The proposed approach is applied on two case studies whose results are evaluated and discussed.
ABSTRACT:This paper addresses an innovative evolutionary computation approach to 3D path planning of autonomous UAVs in real environment. To solve this Np-hard problem, Newtonian imperialist competitive algorithm (NICA) was developed and extended for path planning problem. This paper is related to optimal trajectory-designing before UAV missions. NICA planner provides 3D optimal paths for UAV planning in real topography of north Tehran environment. To simulate UAV path planning, a real DTM is used to algorithm. For real-world applications, final generated paths should be smooth and also physical flyable that made the path planning problems complex and more constrained. The planner progressively presents a smooth 3D path from first position to mission target location. The objective function contains distinctive measures of the problem. Our main goal is minimization of the total mission time. For evaluating of NICA efficiency, it is compared with other three well-known methods, i.e. ICA, GA, and PSO. Then path planning of UAV will done. Finally simulations proved the high capabilities of proposed methodology.
Optimal sensor network deployment in built environments for tracking, surveillance, and monitoring of dynamic phenomena is one of the most challenging issues in sensor network design and applications (e.g., people movement). Most of the current methods for sensor network deployment and optimization are empirical and they often result in important coverage gaps in the monitored areas. To overcome these limitations, several optimization methods have been proposed in the recent years. However, most of these methods oversimplify the environment and do not consider the complexity of 3D architectural nature of the built environments specially for indoor applications (e.g., indoor navigation, evacuation, etc.). In this paper, we propose a novel local optimization algorithm based on a 3D Voronoi diagram, which allows a clear definition of the proximity relations between sensors in 3D indoor environments. This proposed structure is integrated with an IndoorGML model to efficiently manage indoor environment components and their relations as well as the sensors in the network. To evaluate the proposed method, we compared our results with the Genetic Algorithm (GA) and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithms. The results show that the proposed method achieved 98.86% coverage which is comparable to GA and CMA-ES algorithms, while also being about six times more efficient.
A mental map refers to the personalized representation of spatial knowledge in the human brain and is based on the perceptions, experiences, and interactions of people with their environment. For people with motor disabilities (PWMD) some perceptions and interactions with the environment during their mobility occur in different ways and consequently lead to different mental maps. For example, these people perceive and interact differently with elevators, escalators, and steps during their mobility. Hence, their perceptions of the level of complexity and the legibility of an environment may be different. Legibility of an environment is an indicator that measures the level of complexity and the ease of understanding of that environment by a person. In the literature, legibility is mostly estimated based on the environmental factors such as visibility, connectivity, and layout complexity for a given space. However, the role of personal factors (e.g., capacities) is rarely considered in the legibility assessment, which complicates its personalization. This paper aims at studying the influence of personal factors on the evaluation of the legibility of indoor environments for PWMD. In addition to the visibility, the connectivity, and the complexity of indoor environments, we also integrate the influence of the level of accessibility (i.e., presence of facilitators and obstacles) in the legibility assessment process. The Quebec City Convention Centre is selected as our study area and the legibility of this building is quantified. We show how the integration of the above-mentioned factors can influence the legibility for PWMD and hence their mobility performance in those environments.
KEY WORDS: Newtonian imperialist competitive algorithm, multi-sensor, multiple target, collective intelligent, deployment ABSTRACT:The problem of specifying the minimum number of sensors to deploy in a certain area to face multiple targets has been generally studied in the literatures. In this paper, we are arguing the multi-sensors deployment problem (MDP). The Multi-sensor placement problem can be clarified as minimizing the cost required to cover the multi target points in the area. We propose a more feasible method for the multi-sensor placement problem. Our method makes provision the high coverage of grid based placements while minimizing the cost as discovered in perimeter placement techniques. The NICA algorithm as improved ICA (Imperialist Competitive Algorithm) is used to decrease the performance time to explore an enough solution compared to other meta-heuristic schemes such as GA, PSO and ICA. A three dimensional area is used for clarify the multiple target and placement points, making provision x, y, and z computations in the observation algorithm. A structure of model for the multi-sensor placement problem is proposed: The problem is constructed as an optimization problem with the objective to minimize the cost while covering all multiple target points upon a given probability of observation tolerance.
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