Self-organization and management of wireless sensor networks
type: CreativeWork
description: Wireless sensor networks (WSNs) are a newly deployed networking technology consisting of multifunctional sensor nodes that are small in size and communicate over short distances. These sensor nodes are mainly in large numbers and are densely deployed either inside the phenomenon or very close to it. They can be used for various application areas (e.g. health, military, home). WSNs provide several advantages over traditional networks, such as large-scale deployment, highresolution sensed data, and application adaptive mechanisms. However, due to their unique characteristics (having dynamic topology, ad-hoc and unattended deployment, huge amount of data generation and traffic flow, limited bandwidth and energy), WSNs pose considerable challenges for network management and make application development nontrivial. Management of wireless sensor networks is extremely important in order to keep the whole network and application work properly and continuously. Despite the importance of sensor network management, there is no generalize solution available for managing and controlling these resource constrained WSNs. In network management of WSNs, energy-efficient network selforganization is one of the main challenging issues. Self-organization is the property which the sensor nodes must have to organize themselves to form the network. Selforganization of WSNs is challenging because of the tight constraints on the bandwidth and energy resources available in these networks. A self organized sensor network can be clustered or grouped into an easily manageable network. However, existing clustering schemes offer various limitations. For example, existing clustering schemes consume too much energy in cluster formation and re-formation. This thesis presents a novel cellular self-organizing hierarchical architecture for wireless sensor networks. The cellular architecture extends the network life time by efficiently utilizing nodes energy and support the scalability of the system. We …
abstract: Wireless sensor networks (WSNs) are a newly deployed networking technology consisting of multifunctional sensor nodes that are small in size and communicate over short distances. These sensor nodes are mainly in large numbers and are densely deployed either inside the phenomenon or very close to it. They can be used for various application areas (e.g. health, military, home). WSNs provide several advantages over traditional networks, such as large-scale deployment, highresolution sensed data, and application adaptive mechanisms. However, due to their unique characteristics (having dynamic topology, ad-hoc and unattended deployment, huge amount of data generation and traffic flow, limited bandwidth and energy), WSNs pose considerable challenges for network management and make application development nontrivial. Management of wireless sensor networks is extremely important in order to keep the whole network and application work properly and continuously. Despite the importance of sensor network management, there is no generalize solution available for managing and controlling these resource constrained WSNs. In network management of WSNs, energy-efficient network selforganization is one of the main challenging issues. Self-organization is the property which the sensor nodes must have to organize themselves to form the network. Selforganization of WSNs is challenging because of the tight constraints on the bandwidth and energy resources available in these networks. A self organized sensor network can be clustered or grouped into an easily manageable network. However, existing clustering schemes offer various limitations. For example, existing clustering schemes consume too much energy in cluster formation and re-formation. This thesis presents a novel cellular self-organizing hierarchical architecture for wireless sensor networks. The cellular architecture extends the network life time by efficiently utilizing nodes energy and support the scalability of the system. We …
author: Asim, M
keywords: QA75 Electronic computers. Computer science
date published: 2010-01
date modified: 2026-07-24T06:30:16Z
provider: Liverpool Jon Moores University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Liverpool John Moores University

PropertyValue
property ID: DOI
value: 10.24377/LJMU.t.00005998