If appropriately equipped, aircraft traveling throughout the global air space have the unique capability to measure and report high-resolution meteorological data, under all weather conditions. These data can have high economic value for both aviation and nonaviation uses by communicating a dense picture of weather conditions from thousands of ad hoc sensors operating daily in the global air space over areas of an aviation operational interest. However, ensuring that the technology provides adequate levels of service and safety requires a clear understanding of system performance characteristics in conjunction with sensor and data link capabilities, broadcast frequencies, equipage rates, and operational service environment requirements. This paper explores the density of coverage as a measure of performance by presenting the problem as a stochastic area coverage model of a homogeneous dynamic wireless ad hoc sensor network. A model was developed to relate the density needs of various advanced air traffic management applications to the spatial and temporal coverage of aircraft-sensed meteorological data in the airport terminal area. A comprehensive experimental design is presented with varying aircraft equipage rates, message broadcast frequencies, coverage granularities, and traffic demand levels. Infrastructure and sensor limitations are included to capture some realism within the model. Results are determined by solving this variation on a traditional set covering model using Monte Carlo simulation techniques.
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References
1.
EngholmC.Suitability of Aircraft Wind Observations for Wind Forecasting in the Terminal Area. Report 43PM-Wx-0109. Massachusetts Institute of Technology, Lincoln Laboratory, Lexington, Mass., July 22, 2009.
2.
DO-339: Aircraft Derived Meteorological Data via Data Link for Wake Vortex, Air Traffic Management and Weather Applications Operational Services and Environmental Definition (OSED). RTCA, Inc., Washington, D.C., 2012.
3.
AudenaerdL., and LunsfordC.Analysis of Design Alternatives for Aircraft Based Weather Input to Wake Turbulence Mitigation for Arrivals–System (WTMA-S) at San Francisco International Airport (SFO) MTR 130283, MITRE Corporation, McLean, Va., June 2013.
4.
AudenaerdL., DominoD., LangS., LunsfordC., SmithA. P., and TittsworthJ.Increasing Airport Arrival Capacity in NextGen with Wake Turbulence Avoidance. Proc., 9th ICNS Conference, Arlington, Va., 2009.
5.
LunsfordC., MundraA., AudenaerdL., ChengJ., DevlinC., GrossA., MayerR., SherryJ., BryantW., JohnsonE., and McKissickB.Wind Dependent Concepts for Wake Avoidance: A Comparative Analysis of Capacity Benefits and Implementation Risks. Proc., 24th Digital Avionics Systems Conference, Arlington, Va., Nov. 2005.
6.
RobaskyF., and ClarkD.A Wind Forecast Algorithm to Support Wake Turbulence Mitigation for Departures (WTMD). Proc., 13th Conference on Aviation, Range and Aerospace Meteorology, American Meteorological Society, New Orleans, La., Jan. 2008.
7.
AudenaerdL.A Characterization of Surface and Aloft Winds for Advanced Parallel Runway Operations. In Transportation Research Record: Journal of the Transportation Research Board, No. 2300, Transportation Research Board of the National Academies, Washington, D.C., 2012, pp. 49–58.
8.
LunsfordC., and AudenaerdL.Recovering Airport and System Capacity Lost During Weather: Trends in Wake Turbulence Mitigation and Closely Spaced Parallel Operations. Presented at 89th Annual Meeting of the Transportation Research Board, Washington, D.C., 2010.
9.
CardeiM., and WuJ.Coverage in Wireless Sensor Networks. In Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems (IllyasM., and MohgoubI., eds.), CRC Press, Boca Raton, Fla., 2005, pp. 423–446.
10.
Sameer TilakS., Abu-GhazalehN., and HeinzelmanW.A Taxonomy of Wireless Micro-Sensor Network Models. Mobile Computing and Communications Review, Vol. 6 No. 2, 2002, pp. 28–36.
11.
GabrieleS., and Di GiamberardinoP.The Area Coverage Problem for Dynamic Sensor Networks. In New Developments in Robotics, Automation and Control (LazinicaA., ed.), In-Tech, Rijeka, Croatia, 2008, pp. 1–26.
12.
Simplot-RylD., StojmenovicI., and WuJ.Energy-Efficient Backbone Construction, Broadcasting, and Area Coverage in Sensor Networks. In Handbook of Sensor Networks: Algorithms and Architectures (StojmenovicI., ed.), John Wiley & Sons, Inc., New York, 2005, pp. 343–379.
13.
ShakkottaiS., SrikantR., and ShroffN.Unreliable Sensor Grids: Coverage, Connectivity and Diameter. Ad Hoc Networks, Vol. 3, No. 6, 2005, pp. 702–716.
14.
RömerK., and MatternF.The Design Space of Wireless Networks. IEEE Wireless Communications, Dec. 2004, pp. 54–61.
15.
FeiX., MahmassaniH. S., and EisenmanS. M.Sensor Coverage and Location for Real-Time Traffic Prediction in Large-Scale Networks. In Transportation Research Record: Journal of the Transportation Research Board, No. 2039, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 1–15.
16.
BoyceD., HicksJ., and SenA.In-Vehicle Navigation System Requirements for Monitoring Link Travel Time in a Dynamic Route Guidance System. Operations Review, Vol. 8, No. 1, 1991, pp. 17–24.
17.
GreenM. W., FontaineM. D., and SmithB. L.Investigation of Dynamic Probe Sample Requirements for Traffic Condition Monitoring. In Transportation Research Record: Journal of the Transportation Research Board, No. 1870, Transportation Research Board of the National Academies, Washington, D.C., 2004, pp. 55–61.
18.
O'RourkeJ.Art Gallery Theorems and Algorithms.Oxford University Press, Oxford, United Kingdom, 1987.
19.
DaskinM.Network and Discrete Location: Models, Algorithms, and Applications.Wiley-Interscience, New York, 1995.
20.
LazosL., and PoovendranR.Stochastic Coverage in Heterogeneous Sensor Networks. ACM Transactions on Sensor Networks, Vol. 2, No. 3, 2006, pp. 325–358.
21.
Di GiamberardinoP., and GabrieleS.Mobile Sensors Networks: A Distributed Solution to the Area Coverage Problem. Proc., 16th IEEE Mediterranean Conference on Control and Automation, Ajaccio, Corsica, 2008.
22.
MaY.Air Pollution Monitoring and Mining Based on Sensor Grid in London. Sensors, Vol. 8, 2008, pp. 3601–3623.
23.
RohdeS., GoddemeierN., DanielK., and WietfeldC.Link Quality Dependent Mobility Strategies for Distributed Aerial Sensor Networks. IEEE GLOBECOM Workshops on Wireless Networking for Unmanned Aerial Vehicles, 2010.
24.
SgorceaR.Viability of Aircraft Communication Addressing and Reporting System (ACARS) to Support Mid-Term RTA Concepts. Report MP120211. MITRE Corporation, McLean, Va., April 2012.
25.
WynnykC., and GouldeyD.2011 Seattle Required Time of Arrival (RTA) Flight Trials Analysis Report. Report MP120061S1. MITRE Corporation, McLean, Va., July 2012.
26.
WolffJ., and SharmanR.Climatology of Upper-Level Turbulence over the Continental United States. Journal of Applied Meteorology and Climatology, Vol. 47, 2008, pp. 2198–2214.
27.
GlinaY., ReynoldsT. G., TroxelS., and McPartlandM.Wind Information Requirements to Support Four-Dimensional Trajectory-Based Operations. Proc., 12th AIAA Aviation Technology, Integration, and Operations Conference, Indianapolis, Ind., 2012.
28.
SandbergM., ReynoldsT. G., McPartlandM., TroxelS., and GlinaY.Assessing Wind Impacts on Flight Interval Management Performance. Proc., 13th AIAA Aviation Technology, Integration, and Operations Conference, Los Angeles, Calif., 2013.
SpinosoL., and LunsfordC.Development of New Final Approach Speed Profiles for Wake Turbulence Procedure and Design. Presented at 93rd Annual Meeting of the Transportation Research Board, Washington, D.C., 2014.
