Notice Board :





Volume XVI Issue VIII

Author Name
Nitesh Kumar, Alka Thakur
Year Of Publication
2024
Volume and Issue
Volume 16 Issue 8
Abstract
Energy is one of the world's most important economic, environmental, and sustainability concerns. To improve living standards and reduce poverty, developing countries, in particular, need reliable, accessible, safe, and effective energy services. In recent years, many solar photovoltaic (PV) based DC microgrids have been developed to provide electric power to rural areas in developing countries. This research paper has proposed an IoT-based smart microgrid system for rural areas with an advanced control system for the optimal microgrid operation using the internet. The solution is provided by thinking a group of people living in a remote area. This prototype would detect the branch's failure and it could be managed from anywhere at any time with the help of internet. The power ratings would be displayed to the authority via a power monitoring system. In case of an emergency, a generator would be used to provide power to the rural region. The simulation of the projects was successfully
PaperID
2024/EUSRM/8/2024/61582

Author Name
Poornima Kaushik, Siddheshvar Mishra
Year Of Publication
2024
Volume and Issue
Volume 16 Issue 8
Abstract
yah adhyayan aaeeteeaee dillee, aaeeteeaee baingalor, aaeeteeaee chennee aur aaeeteeaee pune par dhyaan kendrit karate hue bhaarat mein audyogik prashikshan sansthaanon (aaeeteeaee) dvaara pesh kie gae rojagaar kaaryakramon kee prabhaavasheelata ka moolyaankan karata hai.
PaperID
2024/EUSRM/8/2024/61581

Author Name
Pooja Kumari Singh, Leena Shrivastava
Year Of Publication
2024
Volume and Issue
Volume 16 Issue 8
Abstract
This paper introduces an advanced version of the Imperialist Competitive Algorithm (ICA) tailored for task scheduling in cloud computing environments. Addressing the ICA's common issue of rapid convergence to local optima, the proposed method integrates a uniform mixing process, similar to that used in Genetic Algorithms (GAs), into the absorption policy. This enhancement improves exploration capabilities and reduces the risk of local optima entrapment. The method employs a uniform recombination technique to generate new colony positions and ensure effective mutations. Simulation results, conducted using MATLAB, demonstrate that the proposed algorithm significantly outperforms the Genetic Algorithm (GA) in terms of task completion time and resource productivity. The inclusion of bandwidth considerations in the cost function further enhances scheduling efficiency. Compared to other scheduling methods, including GAs, Particle Swarm Optimization, and traditional ICA, the enhanced ICA appr
PaperID
2024/EUSRM/8/2024/61583