dc.contributor.author |
Kolega, E |
en |
dc.contributor.author |
Vescoukis, V |
en |
dc.contributor.author |
Voutos, D |
en |
dc.date.accessioned |
2014-03-01T02:47:17Z |
|
dc.date.available |
2014-03-01T02:47:17Z |
|
dc.date.issued |
2011 |
en |
dc.identifier.uri |
https://dspace.lib.ntua.gr/xmlui/handle/123456789/33056 |
|
dc.subject |
Climate Change |
en |
dc.subject |
Environmental Conditions |
en |
dc.subject |
Large Scale |
en |
dc.subject |
Logic Gate |
en |
dc.subject |
Mathematical Model |
en |
dc.subject |
Network Simulator |
en |
dc.subject |
Object-oriented Model |
en |
dc.subject |
Open Source |
en |
dc.subject |
Operations Management |
en |
dc.subject |
Path Loss |
en |
dc.subject |
Performance Comparison |
en |
dc.subject |
Performance Optimization |
en |
dc.subject |
Rural Area |
en |
dc.subject |
Seasonality |
en |
dc.subject |
High Density |
en |
dc.subject |
Wireless Sensor Network |
en |
dc.subject.other |
Environment protection |
en |
dc.subject.other |
Environmental applications |
en |
dc.subject.other |
Environmental crisis |
en |
dc.subject.other |
Forest area |
en |
dc.subject.other |
Forest environments |
en |
dc.subject.other |
High density |
en |
dc.subject.other |
High spatial density |
en |
dc.subject.other |
Human society |
en |
dc.subject.other |
Measurements and monitoring |
en |
dc.subject.other |
Network simulation tools |
en |
dc.subject.other |
Network simulators |
en |
dc.subject.other |
Open sources |
en |
dc.subject.other |
Operational management |
en |
dc.subject.other |
Other applications |
en |
dc.subject.other |
Performance optimizations |
en |
dc.subject.other |
Technological advances |
en |
dc.subject.other |
Vegetation density |
en |
dc.subject.other |
Visualization tools |
en |
dc.subject.other |
Wild fire |
en |
dc.subject.other |
Wireless sensor |
en |
dc.subject.other |
Climate change |
en |
dc.subject.other |
Sensors |
en |
dc.subject.other |
Simulators |
en |
dc.subject.other |
Visualization |
en |
dc.subject.other |
Wireless sensor networks |
en |
dc.title |
Assessment of network simulators for real world WSNs in forest environments |
en |
heal.type |
conferenceItem |
en |
heal.identifier.primary |
10.1109/ICNSC.2011.5874918 |
en |
heal.identifier.secondary |
http://dx.doi.org/10.1109/ICNSC.2011.5874918 |
en |
heal.identifier.secondary |
5874918 |
en |
heal.publicationDate |
2011 |
en |
heal.abstract |
Environment protection and preservation is an in season issue today, more than ever before. It has become a critical concern as the climate change threatens the sustainability of human societies; in this context exploitation of any technological advance in protecting the environment is more than welcome. Wireless sensor network applications appear as a promising technology, very helpful to many environmental applications. As the cost of such networks becomes more reasonable, deploying a wireless sensor network at a high spatial density can be a major aid in environmental crisis detection, prediction and even operational management. A typical such case is wild fire detection, but several other applications are also possible. In this context, network simulation tools can play a great role in WSN implementation, in performance optimization, reliability and cost, especially as the scale of deployment can become very large. However, current WSN simulation and visualization tools, most of which are not designed with WSNs consisting of tens of thousands of nodes in mind, still have some shortcomings. In this paper we report our experience from some well known open source WSN simulators and discuss their capabilities and possible weaknesses when used in the simulation of high density WSNs. We compare the results of a common simulation scenario produced by the use of three well-known simulators. These results are also matched against measurements taken out of real world WSN measurements and monitoring in a forest environment, and specifically in a forest area with high vegetation density. In this context, conclusions and assessments are attempted on the maturity of these simulation and visualization tools for large-scale WSN deployments in such environments. © 2011 IEEE. |
en |
heal.journalName |
2011 International Conference on Networking, Sensing and Control, ICNSC 2011 |
en |
dc.identifier.doi |
10.1109/ICNSC.2011.5874918 |
en |
dc.identifier.spage |
427 |
en |
dc.identifier.epage |
432 |
en |