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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

Influence of node at CSIRO Laboratory.
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f11-sensors-12-02874: Influence of node at CSIRO Laboratory.

Mentions: Figure 11 shows the footprint of influence of the location of our laboratory and one of our permanent sensor nodes. It is clear that closer to the location itself the stronger the similarity. However, we have noted that there is a reasonably strong similarity between the two major rivers, the Derwent (42°50′S, 147°15′E) and Huon (43°10′S, 147°E).


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

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

Influence of node at CSIRO Laboratory.
© Copyright Policy
Related In: Results  -  Collection

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

f11-sensors-12-02874: Influence of node at CSIRO Laboratory.
Mentions: Figure 11 shows the footprint of influence of the location of our laboratory and one of our permanent sensor nodes. It is clear that closer to the location itself the stronger the similarity. However, we have noted that there is a reasonably strong similarity between the two major rivers, the Derwent (42°50′S, 147°15′E) and Huon (43°10′S, 147°E).

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