3 research outputs found
Multiple solutions based particle swarm optimization for cluster-head-selection in wireless-sensor-network
Wireless sensor network (WSN) has a significant role in wide range of scientific and industrial applications. In WSN, within the operation area of sensor nodes the nodes are randomly deployed. The constraint related to energy is considered as one of the major challenges for WSN, which may not only affect the sensor nodes efficiency but also influences the operational capabilities of the network. Therefore, numerous attempts of researches have been proposed to counter this energy problem in WSN. Hierarchical clustering approaches are popular techniques that offered the efficient consumption of the energy in WSN. In addition to this, it is understood that the optimum choice of sensor as cluster head can critically help to reduce the energy consumption of the sensor node. In recent years, metaheuristic optimization is used as a proposed technique for the optimal selection of cluster heads. Furthermore, it is noteworthy here that proposed techniques should be efficient enough to provide the optimal solution for the given problem. Therefore, in this regard, various attempts are made in the form of modified versions or new metaheuristic algorithms for optimization problems. The research in the paper offered a modified version of particle-swarm-optimization (PSO) for the optimal selection of sensor nodes as cluster heads. The performance of the suggested algorithm is experimented and compared with the renowned optimization techniques. The proposed approach produced better results in the form of residual energy, number of live nodes, sum of dead nodes, and convergence rate
A metaheuristic optimization approach for energy efficiency in the IoT networks
© 2020 John Wiley & Sons, Ltd. Recently Internet of Things (IoT) is being used in several fields like smart city, agriculture, weather forecasting, smart grids, waste management, etc. Even though IoT has huge potential in several applications, there are some areas for improvement. In the current work, we have concentrated on minimizing the energy consumption of sensors in the IoT network that will lead to an increase in the network lifetime. In this work, to optimize the energy consumption, most appropriate Cluster Head (CH) is chosen in the IoT network. The proposed work makes use of a hybrid metaheuristic algorithm, namely, Whale Optimization Algorithm (WOA) with Simulated Annealing (SA). To select the optimal CH in the clusters of IoT network, several performance metrics such as the number of alive nodes, load, temperature, residual energy, cost function have been used. The proposed approach is then compared with several state-of-the-art optimization algorithms like Artificial Bee Colony algorithm, Genetic Algorithm, Adaptive Gravitational Search algorithm, WOA. The results prove the superiority of the proposed hybrid approach over existing approaches
Assessment of Renewable Energy Resources with Remote Sensing
The development of renewable energy sources plays a fundamental role in the transition towards a low carbon economy. Considering that renewable energy resources have an intrinsic relationship with meteorological conditions and climate patterns, methodologies based on the remote sensing of the atmosphere are fundamental sources of information to support the energy sector in planning and operation procedures. This Special Issue is intended to provide a highly recognized international forum to present recent advances in remote sensing to data acquisition required by the energy sector. After a review, a total of eleven papers were accepted for publication. The contributions focus on solar, wind, and geothermal energy resource. This editorial presents a brief overview of each contribution.About the Editor .............................................. vii
Fernando Ramos Martins
Editorial for the Special Issue: Assessment of Renewable Energy Resources with
Remote Sensing
Reprinted from: Remote Sens. 2020, 12, 3748, doi:10.3390/rs12223748 ................. 1
André R. Gonçalves, Arcilan T. Assireu, Fernando R. Martins, Madeleine S. G. Casagrande, Enrique V. Mattos, Rodrigo S. Costa, Robson B. Passos, Silvia V. Pereira, Marcelo P. Pes, Francisco J. L. Lima and Enio B. Pereira
Enhancement of Cloudless Skies Frequency over a Large Tropical Reservoir in Brazil
Reprinted from: Remote Sens. 2020, 12, 2793, doi:10.3390/rs12172793 ................. 7
Anders V. Lindfors, Axel Hertsberg, Aku Riihelä, Thomas Carlund, Jörg Trentmann and Richard Müller
On the Land-Sea Contrast in the Surface Solar Radiation (SSR) in the Baltic Region
Reprinted from: Remote Sens. 2020, 12, 3509, doi:10.3390/rs12213509 ................. 33
JoaquĂn Alonso-Montesinos
Real-Time Automatic Cloud Detection Using a Low-Cost Sky Camera
Reprinted from: Remote Sens. 2020, 12, 1382, doi:10.3390/rs12091382 ................. 43
Román MondragĂłn, JoaquĂn Alonso-Montesinos, David Riveros-Rosas, Mauro ValdĂ©s, HĂ©ctor EstĂ©vez, Adriana E. González-Cabrera and Wolfgang Stremme
Attenuation Factor Estimation of Direct Normal Irradiance Combining Sky Camera Images and Mathematical Models in an Inter-Tropical Area
Reprinted from: Remote Sens. 2020, 12, 1212, doi:10.3390/rs12071212 ................. 61
Jinwoong Park, Jihoon Moon, Seungmin Jung and Eenjun Hwang
Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island
Reprinted from: Remote Sens. 2020, 12, 2271, doi:10.3390/rs12142271 ................. 79
Guojiang Xiong, Jing Zhang, Dongyuan Shi, Lin Zhu, Xufeng Yuan and Gang Yao
Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models
Reprinted from: Remote Sens. 2019, 11, 2795, doi:10.3390/rs11232795 ................. 101
Alexandra I. Khalyasmaa, Stanislav A. Eroshenko, Valeriy A. Tashchilin, Hariprakash Ramachandran, Teja Piepur Chakravarthi and Denis N. Butusov
Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning
Reprinted from: Remote Sens. 2020, 12, 3420, doi:10.3390/rs12203420 ................. 125
Ian R. Young, Ebru Kirezci and Agustinus Ribal
The Global Wind Resource Observed by Scatterometer
Reprinted from: Remote Sens. 2020, 12, 2920, doi:10.3390/rs12182920 ................. 147
Susumu Shimada, Jay Prakash Goit, Teruo Ohsawa, Tetsuya Kogaki and Satoshi Nakamura
Coastal Wind Measurements Using a Single Scanning LiDAR
Reprinted from: Remote Sens. 2020, 12, 1347, doi:10.3390/rs12081347 ................. 165
Cristina Sáez Blázquez, Pedro Carrasco GarcĂa, Ignacio MartĂn Nieto, MiguelAngel ´ MatĂ©-González, Arturo Farfán MartĂn and Diego González-Aguilera
Characterizing Geological Heterogeneities for Geothermal Purposes through Combined Geophysical Prospecting Methods
Reprinted from: Remote Sens. 2020, 12, 1948, doi:10.3390/rs12121948 ................. 189
Miktha Farid Alkadri, Francesco De Luca, Michela Turrin and Sevil Sariyildiz
A Computational Workflow for Generating A Voxel-Based Design Approach Based on Subtractive Shading Envelopes and Attribute Information of Point Cloud Data
Reprinted from: Remote Sens. 2020, 12, 2561, doi:10.3390/rs12162561 ................. 207Instituto do Ma