An Enhanced Genetic Algorithm (EGA)-based Multi-Hop Path for Energy Efficient in Wireless Sensor Network (WSN)

Kumar, Battina Srinuvasu; Santhi, S. G.; Narayana, Sujay

doi:10.14569/ijacsa.2022.01304114

Cited by 2 publications

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Research on WSN Intelligent Routing Algorithm based on Bayesian Learning and Particle Swarm Optimization

Cai

Jia

Yang

et al. 2024

EEENG

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Background: Wireless sensor networks have the characteristics of strong scalability, easy maintenance, and self-organization, but the energy of nodes is limited and it is difficult to replace the energy supply module. The survival time of the network has always been the key to restricting the development of wireless sensor networks. Objective: Aiming at the problems of short network lifetime and low coverage, a multi-objective optimization routing algorithm has been proposed, focusing on how to balance the communication energy consumption of each node in the network and improve the coverage area of the remaining nodes. Method: Firstly, the node region was divided into several fan ring subregions. Then, the particle swarm optimization algorithm was used to find the fan angles and radii of each fan ring subregion. Next, Bayesian learning was used to select the appropriate cluster head. Results: The simulation results showed the convergence speed of the proposed algorithm to be improved, solving the problems of cluster head election and node routing planning, improving the utilization of node energy, and verifying the effectiveness. Conclusion: The particle swarm optimization algorithm and Bayesian learning have been introduced to cluster network nodes, and a multi-objective fitness function compatible with the energy consumption and coverage of network nodes has been designed. By optimizing the selection method of convergence nodes, the network communication cost of each node can be effectively balanced, and the speed of network coverage area reduction can be effectively reduced in the later period of node communication.

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Research on WSN Intelligent Routing Algorithm based on Bayesian Learning and Particle Swarm Optimization

Cai

Jia

Yang

et al. 2024

EEENG

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Fundamentals of Wireless Sensor Networks

Satheesh Kumar,

Shaikh,

Kathirisetty

et al. 2024

Advances in Computer and Electrical Engineering

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As the current computing era is working behind digital data, it is indeed important to concentrate on climatic change, which has become dynamic. Even a few non-automation processes have been converted into automation by using IIoT and industry 4.0 revolution technologies such as data analytics, the Internet of Things, cybersecurity, and machine learning. Sensor networks (SN) play a pivotal role in the collection and transformation of data through electronic devices as sensors. The sensors that work in this stream are ultrasonic sensors that help measure the distance between two vehicles. This proposal concentrates on different sensors used in environmental monitoring, data collection, and data transformation from devices to clouds and cyberattacks that poison wireless sensor networks: tampering attacks, replication attacks, blackhole attacks, wormhole attacks, Sybil attacks, and link layer attacks. Sensors which retrograde in environmental monitoring are listed as Temperature sensor, humidity sensors, airflow sensors, pressure sensors, vibration sensors, and water measuring sensor

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An Enhanced Genetic Algorithm (EGA)-based Multi-Hop Path for Energy Efficient in Wireless Sensor Network (WSN)

Cited by 2 publications

References 0 publications

Research on WSN Intelligent Routing Algorithm based on Bayesian Learning and Particle Swarm Optimization

Research on WSN Intelligent Routing Algorithm based on Bayesian Learning and Particle Swarm Optimization

Fundamentals of Wireless Sensor Networks

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