Channel allocation based on genetic algorithm for multiple IEEE 802.15.4-compliant wireless sensor networks

2017 ◽  
Author(s):  
Xiaoya Hu ◽  
Sanyou Ge ◽  
Jiangwen Xiao
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Ieee 802.15.4 ◽  
Channel Allocation ◽  
Wireless Sensor

A Channel Allocation Scheme to Mitigate Wifi Interference for Wireless Sensor Networks

2012 ◽  
Vol 35 (3) ◽  
pp. 504-517 ◽  
Author(s):  
Zhao-Liang ZHANG ◽  
Hai-Ming CHEN ◽  
Ting-Pei HUANG ◽  
Li CUI ◽  
Ze ZHAO
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Channel Allocation ◽  
Wireless Sensor ◽  
Allocation Scheme

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.

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