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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

Estimated duration in minutes from our laboratory using available roads.
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f3-sensors-12-02874: Estimated duration in minutes from our laboratory using available roads.

Mentions: The distance-by-land was calculated using Google Maps Directions API [18], which determines the shortest distance by road and the expected duration of the trip. Many of the grid points were too far out to sea for this calculation to be valid and are given a maximum cost. We are more interested in the duration of the trip rather than the distance as some journeys would be through busy city areas or require waiting for a ferry in the case of points off Bruny Island (43°25′S, 147°10′E). The results from this analysis is shown in Figure 3.


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

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

Estimated duration in minutes from our laboratory using available roads.
© Copyright Policy
Related In: Results  -  Collection

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

f3-sensors-12-02874: Estimated duration in minutes from our laboratory using available roads.
Mentions: The distance-by-land was calculated using Google Maps Directions API [18], which determines the shortest distance by road and the expected duration of the trip. Many of the grid points were too far out to sea for this calculation to be valid and are given a maximum cost. We are more interested in the duration of the trip rather than the distance as some journeys would be through busy city areas or require waiting for a ferry in the case of points off Bruny Island (43°25′S, 147°10′E). The results from this analysis is shown in Figure 3.

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