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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 distance by boat from our laboratory in metres.
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f2-sensors-12-02874: Estimated distance by boat from our laboratory in metres.

Mentions: The A* algorithm [17] is applied to the model grid cells to calculate the distance from each grid point from our base to be traversed by boat. A* is an algorithm that attempts to find the shortest path between two points, which may include points that are inaccessible - in this case we cannot pass through locations that are on land. Once A* had chosen the shortest path P through the grid cells then the average grid cell size of each step p is summed together to create a total distance. In Equation (1), w is the width and h is the height.(1)dist(P)=∑p∈Pwp+hp2This is required because of the non-uniform grid. If we only counted the number of grid cells passed it would consider crossing the areas upriver with high resolution to have greater distance than the large grid cells out in the ocean. Figure 2 shows the results from this analysis.


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

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

Estimated distance by boat from our laboratory in metres.
© Copyright Policy
Related In: Results  -  Collection

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

f2-sensors-12-02874: Estimated distance by boat from our laboratory in metres.
Mentions: The A* algorithm [17] is applied to the model grid cells to calculate the distance from each grid point from our base to be traversed by boat. A* is an algorithm that attempts to find the shortest path between two points, which may include points that are inaccessible - in this case we cannot pass through locations that are on land. Once A* had chosen the shortest path P through the grid cells then the average grid cell size of each step p is summed together to create a total distance. In Equation (1), w is the width and h is the height.(1)dist(P)=∑p∈Pwp+hp2This is required because of the non-uniform grid. If we only counted the number of grid cells passed it would consider crossing the areas upriver with high resolution to have greater distance than the large grid cells out in the ocean. Figure 2 shows the results from this analysis.

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