Redundancy estimation and adaptive density control inwireless sensor networks

Name: Redundancy estimation and adaptive density control inwireless sensor networks
Author: Machado, R., He, H., Wang, G., Tekinay, Şirin

İsim	Redundancy estimation and adaptive density control inwireless sensor networks
Yazar	Machado, R., He, H., Wang, G., Tekinay, Şirin
Basım Tarihi:	2010
Basım Yeri	- Old City Publishing
Konu	Wireless sensor networks, Redundancy estimation, Environment variation, Neural networks
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2-s2.0-78651467532
Kayıt Numarası	d5573a3a-1854-405d-86cb-d5208c3bbe7c
Lokasyon	Electrical & Electronics Engineering
Tarih	2010
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	While dense random deployment satisfies coverage and sensing requirements, constructing dense networks of sensor nodes poses the problems of obtaining node location information.We provide an analytical framework for estimating the redundancy in a single-hop WSN of random deployment of nodes without the need of location information of nodes. We use an information theoretic approach to estimate the redundancy and provide the Cramer-Rao bound on the error in the estimation. We illustrate this redundancy estimation approach and calculate the bounds on the error in the estimation for a WSN with 1-redundancy. We also analytically show the inter-dependence between redundancy and network lifetime for random deployment. We further study the energy model of a WSN as interdependence between the environmental variation and its impact on the energy consumption at individual nodes. Defining network energy as the sum of residual battery energy at nodes, we provide an analytical framework for the dependence of node energy and sensitivity of network energy as a function of environmental variation and reliability parameters. Using a neural network based approach, we perform adaptive density control and show how reliability requirements and environment variation influences the rate of change of network energy.
Cilt	10

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

Redundancy estimation and adaptive density control inwireless sensor networks

Yazar Machado, R., He, H., Wang, G., Tekinay, Şirin

Basım Tarihi 2010

Basım Yeri - Old City Publishing

Konu Wireless sensor networks, Redundancy estimation, Environment variation, Neural networks

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2-s2.0-78651467532

Kayıt Numarası d5573a3a-1854-405d-86cb-d5208c3bbe7c

Lokasyon Electrical & Electronics Engineering

Tarih 2010

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin While dense random deployment satisfies coverage and sensing requirements, constructing dense networks of sensor nodes poses the problems of obtaining node location information.We provide an analytical framework for estimating the redundancy in a single-hop WSN of random deployment of nodes without the need of location information of nodes. We use an information theoretic approach to estimate the redundancy and provide the Cramer-Rao bound on the error in the estimation. We illustrate this redundancy estimation approach and calculate the bounds on the error in the estimation for a WSN with 1-redundancy. We also analytically show the inter-dependence between redundancy and network lifetime for random deployment. We further study the energy model of a WSN as interdependence between the environmental variation and its impact on the energy consumption at individual nodes. Defining network energy as the sum of residual battery energy at nodes, we provide an analytical framework for the dependence of node energy and sensitivity of network energy as a function of environmental variation and reliability parameters. Using a neural network based approach, we perform adaptive density control and show how reliability requirements and environment variation influences the rate of change of network energy.

Cilt 10