alexa Estimating Node Density for Redundant Sensors in Wirele
ISSN: 2090-4886

International Journal of Sensor Networks and Data Communications
Open Access

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Research Article

Estimating Node Density for Redundant Sensors in Wireless Sensor Network

Dhruv Sharma1*, Kayiram Kavitha2 and R Gururaj3

1Software Engineer, PayPal, Chennai, India

2Researcher, Pune, India

3Department of CS and IS, BITS Pilani, Hyderabad Campus, Hyderabad, India

*Corresponding Author:
Dhruv Sharma
Software Engineer
Paypal, L404 Lancor Appt
Central Park South, Sholinganallur
Chennai, Tamil Nadu 600119, India
Tel: +919666852383
E-mail: [email protected]

Received date: October 12, 2015; Accepted date: November 03, 2015; Published date: November 06, 2015

Citation: Sharma D, Kavitha K, Gururaj R (2015) Estimating Node Density for Redundant Sensors in Wireless Sensor Network. Sensor Netw Data Commun 4:127. doi:10.4172/2090-4886.1000127

Copyright: © 2015 Sharma D, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

The lifetime of a sensor network depends on the judicious utilization of the resource-constrained nodes. Practices like data aggregation, sleep scheduling play a major role in conserving the node’s energy. But in most cases, we observe a disparity in energy consumption rates among different sensors. This disparity results from higher utilization of a small set of deployed sensors in the field leaving these sensors drained out of power. To overcome this problem, it is often required to deploy redundant sensors to act as replacements for a faulty node. Secondly, the sensor network technology, being an application oriented technology, experiences variance in network parameters from application to application because of the various dynamics in nature. It is not often viable to go for a theoretically determined sensor distribution technique. Thus, it is often required to place sensors by studying the geographical constraints. These have proven to be highly valuable in designing energy efficient routing schemes anThe lifetime of a sensor network depends on the judicious utilization of the resource-constrained nodes. Practices like data aggregation, sleep scheduling play a major role in conserving the node’s energy. But in most cases, we observe a disparity in energy consumption rates among different sensors. This disparity results from higher utilization of a small set of deployed sensors in the field leaving these sensors drained out of power. To overcome this problem, it is often required to deploy redundant sensors to act as replacements for a faulty node. Secondly, the sensor network technology, being an application oriented technology, experiences variance in network parameters from application to application because of the various dynamics in nature. It is not often viable to go for a theoretically determined sensor distribution technique. Thus, it is often required to place sensors by studying the geographical constraints. These have proven to be highly valuable in designing energy efficient routing schemes and network topologies for sensor networks. In this paper we propose a scheme to decide how the distribution of available redundant sensor nodes should take place around sensor nodes. The scheme gives the flexibility to determine sensor positions based on application and geographical constraints. We propose to use the probability estimates of the utilization of a sensor in a given deployment to achieve desired network lifetimes. We also show how in some cases we can leverage the relative position from source(s) and sink be used for the same.d network topologies for sensor networks. In this paper we propose a scheme to decide how the distribution of available redundant sensor nodes should take place around sensor nodes. The scheme gives the flexibility to determine sensor positions based on application and geographical constraints. We propose to use the probability estimates of the utilization of a sensor in a given deployment to achieve desired network lifetimes. We also show how in some cases we can leverage the relative position from source(s) and sink be used for the same.

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