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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

ACBA versus Random results for (a) South East Tasmania and (b) Macquarie Harbour. Cost values are in cost score arbitrary units and Error values are in percentage error.
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f18-sensors-12-02874: ACBA versus Random results for (a) South East Tasmania and (b) Macquarie Harbour. Cost values are in cost score arbitrary units and Error values are in percentage error.

Mentions: Figures 18 contains the results from comparing the cost and benefit scores between our ACBA method and simply placing the nodes randomly. The ACBA method again had all weights set at one. Experiments were ran adding 10, 20, 50 and 100 nodes. The random cost and overall error were averaged over 10 runs and also began with the existing nodes in both regions. Once we have randomly selected the specified number of locations, the cost is determined by the combined costs we have quantified for that location. The overall error is calculated by the average of the difference between the interpolated values at each grid point and what the model actually forecast for that grid point. These were calculated using the separate test set.


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

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

ACBA versus Random results for (a) South East Tasmania and (b) Macquarie Harbour. Cost values are in cost score arbitrary units and Error values are in percentage error.
© Copyright Policy
Related In: Results  -  Collection

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

f18-sensors-12-02874: ACBA versus Random results for (a) South East Tasmania and (b) Macquarie Harbour. Cost values are in cost score arbitrary units and Error values are in percentage error.
Mentions: Figures 18 contains the results from comparing the cost and benefit scores between our ACBA method and simply placing the nodes randomly. The ACBA method again had all weights set at one. Experiments were ran adding 10, 20, 50 and 100 nodes. The random cost and overall error were averaged over 10 runs and also began with the existing nodes in both regions. Once we have randomly selected the specified number of locations, the cost is determined by the combined costs we have quantified for that location. The overall error is calculated by the average of the difference between the interpolated values at each grid point and what the model actually forecast for that grid point. These were calculated using the separate test set.

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