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

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

Nilayam Kumar Kamila (Capital One, Newark, DE, USA) and Sunil Dhal (Sri Sri University, Cuttack, India)
Copyright: © 2017 |Pages: 19
DOI: 10.4018/IJRSDA.2017100106
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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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Wireless Sensor Network is a self-built network infrastructure (Dehni, Kief, and Bennani, 2005, 31-40) where the sensor nodes communicate with each other and build the network topology based on which the sensor nodes communicate. Sensor nodes are tiny devices consists of four components namely sensing unit, processing unit, transmission unit and power unit. The sensing unit sense the behavior of the target object in scope and collects the properties information. The processing unit is responsible to process the gathered data, mainly intent to frame the data in required specifications and pass onto transmission unit. The transmission unit is then transfer the data through the available communication channel. The Sensing unit and Transmission unit are controlled by Processing unit (Heinzelman & Balakrishnan, 2000). In short, the main functionality of sensor device is accomplished through these three units by sensing the environment change or target object behavior change through Sensor unit, minimally process through Processing unit and finally communicate to other nodes or base station through transmission unit (Heinzelman & Balakrishnan, 2000). All these units run through the power unit. This power unit is the central source of power which supplies the power to all unit to run and execute their respective task smoothly. The sensor nodes are deployed in large scale in mission critical areas such as deep forests, mountain ranges, battle fields, natural calamities areas where it is impossible to maintain these nodes or to supply the battery power continuously.

Figure 1.

Wireless Sensor Network Environment

Base station is a separate device which is placed in a continuous power supply areas and collects the sensor information from the deployed sensor nodes (refer Figure 1). All the sensor nodes primary attempt to sense the information and send the information to the base station. Base station processes the collected information and then forwards to the target transmission network such as VPC, or public cloud or internets to make use of the information by real time user as shown in above figure.

The primary research objective is to conserve the battery energy to sustain the network operations through a prolonged time. As it is mentioned above, the power unit is used to supply power to all other three units and the device is deployed in such a mission critical areas where continuous supply of power to the sensor devices is impossible.

We have discussed the related works in this area in next successive section with their pros and cons. In next successive section, the radio transmission model and its energy equations is shown. Our proposed System approach and design is discussed in next followed by Mathematical Analysis and Performance Analysis. We summarize our findings in conclusion section.


LEACH (Heinzelman & Balakrishnan, 2000) is a cluster-based protocol, which includes distributed cluster formation. The cluster head role is rotated among a few sensor nodes to evenly distribute the energy dissipation in the network. In LEACH, the cluster head (CH) nodes compress the received data, aggregate packet before sending to the base station in order to reduce the amount of information that must be transmitted to the base station.

Disadvantage of LEACH Protocol

  • A.

    No centralized clustering mechanism. It’s only based on probability model for Cluster Head selection.

  • B.

    No suggestion is made concerning the time of CHs reelection (iterations period).

  • C.

    The further a CH is from the BS, the more quickly it dies.

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