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

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.