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Relocatable, automated cost-benefit analysis for marine sensor network design.

D'Este C, de Souza P, Sharman C, Allen S - Sensors (Basel) (2012)

Bottom Line: We describe a novel automated method for generating and combining cost and benefit values to decide on the best sensor locations using information about the specific constraints available in most coastal locations.Benefits in maximum coverage and reducing overall error are also determined using model output.This method demonstrates equivalent accuracy at predicting the whole system to expert-chosen locations, whilst significantly reducing the estimated costs.

View Article: PubMed Central - PubMed

Affiliation: Tasmanian ICT Centre, CSIRO, Castray Esplanade, Hobart, TAS 7000, Australia. Claire.DEste@csiro.au

ABSTRACT
When designing sensor networks, we need to ensure they produce representative and relevant data, but this must be offset by the financial cost of placing sensors. We describe a novel automated method for generating and combining cost and benefit values to decide on the best sensor locations using information about the specific constraints available in most coastal locations. Costs in maintenance, negotiation, equipment, exposure and communication are estimated using hydrodynamic models and Electronic Navigation Charts. Benefits in maximum coverage and reducing overall error are also determined using model output. This method demonstrates equivalent accuracy at predicting the whole system to expert-chosen locations, whilst significantly reducing the estimated costs.

No MeSH data available.


Related in: MedlinePlus

Percentage temperature and salinity error after interpolation using the hydrodynamic model for South-East Tasmania.
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f9-sensors-12-02874: Percentage temperature and salinity error after interpolation using the hydrodynamic model for South-East Tasmania.

Mentions: Figure 9 shows the areas of high error when we interpolate temperature readings from the existing three nodes. To the south west, we can see that upriver in the Huon (43°10′S, 147°E) is being represented the most poorly. This will increase the benefit of adding a node at this location. However, if we would like to add another node we recalculate these error scores. Placing a node should reduce the error in this area and reduce the likelihood of placing another there.


Relocatable, automated cost-benefit analysis for marine sensor network design.

D'Este C, de Souza P, Sharman C, Allen S - Sensors (Basel) (2012)

Percentage temperature and salinity error after interpolation using the hydrodynamic model for South-East Tasmania.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376612&req=5

f9-sensors-12-02874: Percentage temperature and salinity error after interpolation using the hydrodynamic model for South-East Tasmania.
Mentions: Figure 9 shows the areas of high error when we interpolate temperature readings from the existing three nodes. To the south west, we can see that upriver in the Huon (43°10′S, 147°E) is being represented the most poorly. This will increase the benefit of adding a node at this location. However, if we would like to add another node we recalculate these error scores. Placing a node should reduce the error in this area and reduce the likelihood of placing another there.

Bottom Line: We describe a novel automated method for generating and combining cost and benefit values to decide on the best sensor locations using information about the specific constraints available in most coastal locations.Benefits in maximum coverage and reducing overall error are also determined using model output.This method demonstrates equivalent accuracy at predicting the whole system to expert-chosen locations, whilst significantly reducing the estimated costs.

View Article: PubMed Central - PubMed

Affiliation: Tasmanian ICT Centre, CSIRO, Castray Esplanade, Hobart, TAS 7000, Australia. Claire.DEste@csiro.au

ABSTRACT
When designing sensor networks, we need to ensure they produce representative and relevant data, but this must be offset by the financial cost of placing sensors. We describe a novel automated method for generating and combining cost and benefit values to decide on the best sensor locations using information about the specific constraints available in most coastal locations. Costs in maintenance, negotiation, equipment, exposure and communication are estimated using hydrodynamic models and Electronic Navigation Charts. Benefits in maximum coverage and reducing overall error are also determined using model output. This method demonstrates equivalent accuracy at predicting the whole system to expert-chosen locations, whilst significantly reducing the estimated costs.

No MeSH data available.


Related in: MedlinePlus