WSN ENERGY OPTIMIZATION WITH ENHANCEMENT OF THROUGHOUT USING CLUSTRING ALGORITHM

Authors: Nikita Sharma, Ass.Prof. Mohit Jain

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Abstract

The main concern in Wireless Sensor Networks is how to handle with their limited energy resources. The performance of Wireless Sensor Networks strongly depends on their lifetime. As a result, Dynamic Power Management approaches with the purpose of reduction of energy consumption in sensor nodes, after deployment and designing of the network, have drawn attentions of many research studies. Recently, there have been a strong interest to use intelligent tools especially Neural Networks in energy efficient approaches of Wireless Sensor Networks, due to their simple parallel distributed computation, distributed storage, data robustness, autoclassification of sensor nodes and sensor reading. Dimensionality reduction and prediction of sensor data obtained simply from the outputs of the neural-networks algorithms can lead to lower communication costs and energy conservation. All these characteristics show great analogy and compatibility between wireless sensor networks and neural networks. This paper aims to present the most important

Introduction

With advancements in wireless and related technologies in last two decades, Wireless Sensor Networks (WSNs) become an integral part of our daily life as these networks are being used in wide areas of applications. WSNs consist of Sensor Nodes (SNs) which are equipped with low-power microcontrollers and transceivers to perform various operations in the network field [1]. There is large range of applications such as monitoring of environment, pollution control system, military operations, control of vehicle motion, detection of earthquake, tracking of target and surveillance system, monitoring system for patients [2], where WSNs can play an important role. Routing is one of the critical technologies in WSNs. Opposed to traditional ad hoc networks, routing in WSNs is more challenging as a result of their inherent characteristics [3, 4]. Firstly, resources are greatly constrained in terms of power supply, processing capability and transmission bandwidth. Secondly, it is difficult to design a global addressing scheme as Internet Protocol (IP). Furthermore, IP cannot be applied to WSNs, since address updating in a largescale or dynamic WSN can result in heavy overhead. Thirdly, due to the limited resources, it is hard for routing to cope with unpredictable and frequent topology changes, especially in a mobile environment. Fourthly, data collection by many sensor nodes usually results in a high probability of data redundancy, which must be considered by routing protocols. Fifthly, most applications of WSNs require the only communication scheme of many-to-one, i.e.

Conclusion

The intelligent routing protocol proposed in this paper clearly outperforms the existing routing protocols of WSN. Hence, the intelligent routing protocol is considered as a better routing protocol than other existing protocols in WSN. In this paper, we propose coverage and connectivity aware neural network based routing for WSNs. The problem is formulated as LP with specified constraints. The selection of CH is proposed using neural network with adaptive learning. The neurons are assigned weight according to the residual energy of the nodes in the network. A coverage aware routing metric is also included to choose the best route from the available ones