Research on Energy-Saving Technology of Wireless Sensor Network

2014 ◽  
Vol 513-517 ◽  
pp. 2257-2260
Author(s):  
Shao Feng Lin ◽  
Zhen Gui Zhang ◽  
Shu Gen Wang ◽  
Yao Zhou
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Energy Conservation ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Network Node ◽  
Signal Isolation ◽  
Compression Coding ◽  
Hardware Circuit ◽  
Conservation Design

As the wireless sensor network node battery is limited and changing battery is difficult, we did research on feasible energy conservation design technology. Paper analyzed energy consumption and the proportion of network node in work station of dormancy, data dealing, and communication and so on. Hardware circuit was promoted through power partition, signal isolation and low energy consumption chip application. Software program was optimized through state machine combined with differential compression coding. Then the purpose of extending network life can be reached. The experiment result shows that the energy conservation design can reduce the energy consumption greatly and extend the network life 2~3 times.

WSN Management Self-Silence Design and Data Analysis for Neural Network Based Infrastructure

2020 ◽  
pp. 1440-1458
Author(s):  
Nilayam Kumar Kamila ◽  
Sunil Dhal
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Energy Conservation ◽  
Sensor Network ◽  
Life Time ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Network Infrastructure ◽  
Network Life Time

In recent Wireless Sensor Network environment, battery energy conservation is one of the most important focus of research. The non-maintainable wireless sensor nodes need modern innovative ideas to save energy in order to extend the network life time. Different strategy in wireless sensor routing mechanism has been implemented to establish the energy conservation phenomenon. In earlier days, the nodes are dissipating maximum energy to communicate with each other(flooding) to establish the route to destination. In the next evolution of this research area, a clustering mechanism introduced which confirms the energy saving over the flooding mechanism. Neural Network is an advanced approach for self-clustering mechanism and when applied on wireless sensor network infrastructure, it reduces the energy consumption required for clustering. Neural network is a powerful concept with complex algorithms and capable to provide clustering solutions based on the wireless sensor network nodes properties. With the implementation of Neural Network on Wireless Sensor Network resolves the issues of high energy consumption required for network clustering. The authors propose a self-silence wireless sensor network model where sensor nodes change the sensing and transmitting mechanism by making self-silent in order to conserve the energy. This concept is simulated in neural network based wireless sensor network infrastructure of routing methodology and the authors observe that it extends the network life time. The mathematical analysis and simulation study shows the improved performance over the existing related neural network based wireless sensor routing protocols. Furthermore, the performance & related model parameters data set analysis provides the respective dependent relation information.

Node Deployment Algorithm Based on Perception Model of Wireless Sensor Network

2015 ◽  
Vol 9 (3) ◽  
pp. 210-215 ◽  
Author(s):  
Hengchang Jing ◽  
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Network Node ◽  
Coverage Problem ◽  
Node Deployment ◽  
Perception Model ◽  
Simulation Results

With the aim of solving the coverage problem of a wireless sensor network, a node deployment algorithm for the wireless sensor network, one based on a perception model, is designed in this work. The simulation results show that this algorithm can effectively deploy the wireless sensor network node, improve the network’s coverage, reduce the energy consumption of the network node, and help the network to function longer.

WSN Management Self-Silence Design and Data Analysis for Neural Network Based Infrastructure

2017 ◽  
Vol 4 (4) ◽  
pp. 82-100 ◽  
Author(s):  
Nilayam Kumar Kamila ◽  
Sunil Dhal
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Energy Conservation ◽  
Sensor Network ◽  
Life Time ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Network Infrastructure ◽  
Network Life Time

In recent Wireless Sensor Network environment, battery energy conservation is one of the most important focus of research. The non-maintainable wireless sensor nodes need modern innovative ideas to save energy in order to extend the network life time. Different strategy in wireless sensor routing mechanism has been implemented to establish the energy conservation phenomenon. In earlier days, the nodes are dissipating maximum energy to communicate with each other(flooding) to establish the route to destination. In the next evolution of this research area, a clustering mechanism introduced which confirms the energy saving over the flooding mechanism. Neural Network is an advanced approach for self-clustering mechanism and when applied on wireless sensor network infrastructure, it reduces the energy consumption required for clustering. Neural network is a powerful concept with complex algorithms and capable to provide clustering solutions based on the wireless sensor network nodes properties. With the implementation of Neural Network on Wireless Sensor Network resolves the issues of high energy consumption required for network clustering. The authors propose a self-silence wireless sensor network model where sensor nodes change the sensing and transmitting mechanism by making self-silent in order to conserve the energy. This concept is simulated in neural network based wireless sensor network infrastructure of routing methodology and the authors observe that it extends the network life time. The mathematical analysis and simulation study shows the improved performance over the existing related neural network based wireless sensor routing protocols. Furthermore, the performance & related model parameters data set analysis provides the respective dependent relation information.

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