scholarly journals An energy efficient optimal path selection technique for IoT using genetic algorithm

2021 ◽  
Vol 1 (3) ◽  
pp. 230
Author(s):  
J. Shreyas ◽  
Dharamendra Chouhan ◽  
Sowmya T. Rao ◽  
P.K. Udayaprasad ◽  
N.N. Srinidhi ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Efficient ◽  
Optimal Path ◽  
Path Selection ◽  
Selection Technique

scholarly journals An Energy Efficient Optimal Path Selection Technique for IoT using Genetic Algorithm

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Dilip Kumar S. M ◽  
Srinidhi N. N ◽  
Udayaprasad P. K ◽  
Sowmya T. Rao ◽  
Shreyas J ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Efficient ◽  
Optimal Path ◽  
Path Selection ◽  
Selection Technique

An Energy Efficient Routing Approach to Enhance Coverage for Application Specific Wireless Sensor Networks using Genetic Algorithm

2019 ◽  
Vol 13 ◽  
Author(s):  
Amandeep Kaur Sohal ◽  
Ajay Kumar Sharma ◽  
Neetu Sood
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Transmission ◽  
Energy Efficient ◽  
Search Algorithm ◽  
Cluster Formation ◽  
Green Computing ◽  
Wireless Sensor ◽  
Energy Efficient Routing

Background: An information gathering is a typical and important task in agriculture monitoring and military surveillance. In these applications, minimization of energy consumption and maximization of network lifetime have prime importance for green computing. As wireless sensor networks comprise of a large number of sensors with limited battery power and deployed at remote geographical locations for monitoring physical events, therefore it is imperative to have minimum consumption of energy during network coverage. The WSNs help in accurate monitoring of remote environment by collecting data intelligently from the individual sensors. Objective: The paper is motivated from green computing aspect of wireless sensor network and an Energy-efficient Weight-based Coverage Enhancing protocol using Genetic Algorithm (WCEGA) is presented. The WCEGA is designed to achieve continuously monitoring of remote areas for a longer time with least power consumption. Method: The cluster-based algorithm consists two phases: cluster formation and data transmission. In cluster formation, selection of cluster heads and cluster members areas based on energy and coverage efficient parameters. The governing parameters are residual energy, overlapping degree, node density and neighbor’s degree. The data transmission between CHs and sink is based on well-known evolution search algorithm i.e. Genetic Algorithm. Conclusion: The results of WCEGA are compared with other established protocols and shows significant improvement of full coverage and lifetime approximately 40% and 45% respectively.

scholarly journals Unified Multi-Objective Genetic Algorithm for Energy Efficient Job Shop Scheduling

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hongjing Wei ◽  
Shaobo Li ◽  
Huafeng Quan ◽  
Dacheng Liu ◽  
Shu Rao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Efficient ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

An intelligent approach towards very short-term load forecasting

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Uttamarani Pati ◽  
Papia Ray ◽  
Arvind R. Singh
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Load Forecasting ◽  
Dew Point ◽  
Short Term ◽  
Spinning Reserve ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Load Demand ◽  
Short Term Load Forecasting

Abstract Very short term load forecasting (VSTLF) plays a pivotal role in helping the utility workers make proper decisions regarding generation scheduling, size of spinning reserve, and maintaining equilibrium between the power generated by the utility to fulfil the load demand. However, the development of an effective VSTLF model is challenging in gathering noisy real-time data and complicates features found in load demand variations from time to time. A hybrid approach for VSTLF using an incomplete fuzzy decision system (IFDS) combined with a genetic algorithm (GA) based feature selection technique for load forecasting in an hour ahead format is proposed in this research work. This proposed work aims to determine the load features and eliminate redundant features to form a less complex forecasting model. The proposed method considers the time of the day, temperature, humidity, and dew point as inputs and generates output as forecasted load. The input data and historical load data are collected from the Northern Regional Load Dispatch Centre (NRLDC) New Delhi for December 2009, January 2010 and February 2010. For validation of proposed method efficacy, it’s performance is further compared with other conventional AI techniques like ANN and ANFIS, which are integrated with genetic algorithm-based feature selection technique to boost their performance. These techniques’ accuracy is tested through their mean absolute percentage error (MAPE) and normalized root mean square error (nRMSE) value. Compared to other conventional AI techniques and other methods provided through previous studies, the proposed method is found to have acceptable accuracy for 1 h ahead of electrical load forecasting.

Sign in / Sign up

Export Citation Format

Share Document