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An approach for safer navigation under severe hurricane damage

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Abstract

The US are increasingly prone to sustaining severe damage from climate change as hurricanes are predicted to become more severe. During and after hurricanes, navigation around flooded areas is a primary objective for both first responders and hurricane victims. Printed or cached maps, because of their static nature, are not ideal for conveying network availability that will be constantly in flux post-disaster. And online services may not be available or may provide unreliable information. We propose a relatively cost-effective decentralized sensing and navigation system for regular grid networks that allows effective and efficient navigation around flooded areas. Using the proposed algorithm, about 59% of the cars with reachable destination were successful in reaching their destinations for the worst case scenario, that is, a dynamically spreading flood. On average, the path length taken by successful cars is 33% longer than the shortest path without flooding. The results of the experiments show that the proposed algorithm has a low computational complexity which makes it a good fit for real-time safe path finding in regular grid networks and it has the potential for extension to other types of road networks.

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Notes

  1. Using network analysis protocols like in ArcGIS means having an extra application on users phone which a) is costly and b) prevents our algorithm to be used by a wider range of people. Users need a light weight, simple and to the point app.

  2. In real scenarios, GPS usually has a 5-m one-sigma error in positioning [19], which mainly affects the calculated angles. This is the less an issue the further the car and its destination are from each other. However, we would have a high error in calculating the angles in close distances, which could be mitigated by alerting the user to the inaccuracy and relying on their visual assistance.

  3. The car operating on geographic coordinates simply computes Euclidean distance on the map in our simulation. However, for the simple decision we have to make and given the assumptions we make, we could as well use Manhattan distance.

  4. If sensor nodes have additional sensors, such as accelerometers, we could also detect other hazards, e.g., the lamppost felled by a storm or earthquake. One could also add an interface, e.g., a button or special signal, that allows a first responder to mark the position as REGN, i.e., impassable.

  5. An empty set is represented by the bit-vector 0. A singleton set is represented by a bit-vector v with the properties: \(v\, >\, 0\) and \(v \wedge (v - 1) = 0,\) where \(\wedge \) is here the bit-wise and operator.

References

  1. Ahmetovic D, Gleason C, Kitani K M, Takagi H, Asakawa C (2016, April) NavCog: turn-by-turn smartphone navigation assistant for people with visual impairments or blindness. In Proceedings of the 13th Web for All Conference (p. 9). ACM, https://doi.org/10.1145/2899475.2899509

  2. Ashley ST, Ashley WS (2008) Flood fatalities in the United States. J Appl Meteorol Climatol 47(3):805–818. https://doi.org/10.1175/2007JAMC1611.1

    Article  Google Scholar 

  3. Balaguru K, Judi DR, Leung LR (2016) Future hurricane storm surge risk for the US gulf and Florida coasts based on projections of thermodynamic potential intensity. Clim Change 138(1–2):99–110. https://doi.org/10.1007/s10584-016-1728-8

    Article  Google Scholar 

  4. Bose P, Morin P, Stojmenović I, Urrutia J (2001) Routing with guaranteed delivery in ad hoc wireless networks. Wirel netw 7(6):609–616

    Article  MATH  Google Scholar 

  5. Chen LW, Cheng JH, Tseng YC (2016) Distributed emergency guiding with evacuation time optimization based on wireless sensor networks. IEEE Trans Parallel Distrib Syst 27(2):419–427. https://doi.org/10.1109/tpds.2015.2500722

    Article  Google Scholar 

  6. City of Houston (2017) City of Houston Street Lighting Program. https://edocs.publicworks.houstontx.gov/documents/divisions/traffic/snap_street_light_procedures.pdf. Accessed 28 July 2018

  7. Duckham M (2013) Decentralized spatial computing: foundations of geosensor networks. Springer Science & Business Media, Berlin, Heidelberg

    Book  Google Scholar 

  8. Duckham M, Kulik L (2003) Simplest Paths: automated route selection for navigation. COSIT, Springer, Berlin, Heidelberg, pp 169–185

    Google Scholar 

  9. Duckham M, Nittel S, Worboys M (2005) Monitoring dynamic spatial fields using responsive geosensor networks. In: Proceedings of the 2005 international workshop on Geographic information systems—GIS ’05, ACM Press. https://doi.org/10.1145/1097064.1097073

  10. Feinmann J (2014) How MSF is mapping the worlds medical emergency zones. BMJ 349:g7540. https://doi.org/10.1136/bmj.g7540

    Article  Google Scholar 

  11. Feng L, Miller-Hooks E (2014) A network optimization-based approach for crowd management in large public gatherings. Transp Res Part C Emerg Technol 42:182–199. https://doi.org/10.1016/j.trc.2014.01.017

    Article  Google Scholar 

  12. Grandoni D (2013) Advantages and limitations of using satellite images for flood mapping. In: Workshop on the use of the Copernicus emergency service for floods, Brussel. The Copernicus website. http://www.copernicus.eu/sites/default/files/documents/User_uptake/Emergency_Events/Athens/03_Session_01_AdvantageAndLimitationsOfUsingSatelliteImagery.pdf. Accessed 31 Jan 2018

  13. Hart P, Nilsson N, Raphael B (1968) A formal sasis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybern 4(2):100–107. https://doi.org/10.1109/tssc.1968.300136

    Article  Google Scholar 

  14. Heydon R, Tanner J, Zhodzishsky V, Shen W, Jerkeby C, Alexandru B, Turon M, Hughes RD, Holtmann M, Gix B, Slupik S, Winiarczyk P, Blasi D, Wang Y, Kovyazin R, Agarwal U, Aleksandrov V, Ko L, Kulkarni O (2017) Mesh profile Bluetooth specification v1.0. Technical report, Bluetooth SIG

  15. Hochmair H (2000) Least angle heuristic: consequences of errors during navigation (abstract). In: GIScience, University of California Regents, Savannah, Georgia, USA, pp 282–285

  16. Hochmair H, Frank AU (2000) Influence of estimation errors on wayfinding-decisions in unknown street networks-analyzing the least-angle strategy. Spat Cognit Comput 2(4):283–313

    Article  Google Scholar 

  17. Jamsa J, Luimula M, Schoning J, Schulte J, Stasch C, Jirka S (2010) A mobile data collection framework for the Sensor Web. In: Proceedings of the ubiquitous positioning indoor navigation and location based service, Helsinki (Kirkkonummi), Finland, pp 1–8. https://doi.org/10.1109/upinlbs.2010.5654036

  18. Karp B, Kung HT (2000) GPSR: greedy perimeter stateless routing for wireless networks. In: Proceedings of the 6th annual international conference on Mobile computing and networking, ACM, pp 243–254

  19. LaMarca A, De Lara E (2008) Location systems: an introduction to the technology behind location awareness. Synth Lect Mobile Pervasive Comput 3(1):1–122. https://doi.org/10.2200/S00115ED1V01Y200804MPC004

    Article  Google Scholar 

  20. Laube P (2014) Decentralized movement analysis. In: Computational movement analysis. Springer briefs in computer science. Springer, Berlin, pp. 59–80. https://doi.org/10.1007/978-3-319-10268-9_4

  21. Longley PA, Goodchild MF, Maguire DJ, Rhind DW (2015) Geographic information science and systems. John Wiley and Sons, Hoboken

    Google Scholar 

  22. Nedkov S, Zlatanova S (2011) Enabling obstacle avoidance for Google maps’ navigation service. In: Proceedings Gi4DM 2011: GeoInformation for Disaster Management

  23. Nittel S (2009) A survey of geosensor networks: advances in dynamic environmental monitoring. Sensors 9(7):5664–5678. https://doi.org/10.3390/s90705664

    Article  Google Scholar 

  24. Nittel S, Duckham M, Kulik L (2004) Information dissemination in mobile ad-hoc geosensor networks. Geographic information science. Springer, Berlin, Heidelberg, pp 206–222

    Chapter  Google Scholar 

  25. Pew Research Center (2015) “The Smartphone Difference”. http://www.pewinternet.org/2015/04/01/us-smartphone-use-in-2015/. Accessed 28 July 2018

  26. Resch B, Blaschke T, Mittlboeck M (2010) Live geography: interoperable geosensor webs facilitating the vision of digital earth. Int J Adv Netw Serv 3:323–332

    Google Scholar 

  27. Richter KF, Shi M, Gan HS, Winter S (2013) Decentralized evacuation management. Transp Res Part C Emerg Technol 31:1–17. https://doi.org/10.1016/j.trc.2013.02.013

    Article  Google Scholar 

  28. Skraba P, Fang Q, Nguyen A, Guibas L (2006) Sweeps over wireless sensor networks. In: Proceedings of the fifth international conference on Information processing in sensor networks - IPSN ’06, ACM Press, https://doi.org/10.1145/1127777.1127802

  29. So SK, Daganzo CF (2010) Managing evacuation routes. Transp Res Part B Methodol 44(4):514–520. https://doi.org/10.1016/j.trb.2009.11.002

    Article  Google Scholar 

  30. Trotter CM (1991) Remotely-sensed data as an information source for geographical information systems in natural resource management a review. Int J Geogr Inf Syst 5(2):225–239. https://doi.org/10.1080/02693799108927845

    Article  Google Scholar 

  31. Tseng YC, Pan MS, Tsai YY (2006) Wireless sensor networks for emergency navigation. Computer 39(7):55–62. https://doi.org/10.1109/mc.2006.248

    Article  Google Scholar 

  32. Wiener JM, Mallot HA (2003) ‘Fine-to-coarse’ route planning and navigation in regionalized environments. Spat Cognit Comput 3(4):331–358. https://doi.org/10.1207/s15427633scc0304_5

    Article  Google Scholar 

  33. Wilensky U (1999) NetLogo. Tech. rep., Center for connected learning and computer-based modeling, Northwestern University, http://ccl.northwestern.edu/netlogo/. Accessed 28 July 2018

  34. Winter S, Richter KF, Shi M, Gan HS (2011) Get me out of here: Collaborative evacuation based on local knowledge. In: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness, ACM, pp 35–42

  35. Wolfson O, Xu B (2004) Opportunistic dissemination of spatio-temporal resource information in mobile peer to peer networks. In: Database and Expert Systems Applications, 2004. Proceedings. 15th International Workshop on, IEEE, pp 954–958

  36. Wuebbles DJ, Kunkel K, Wehner M, Zobel Z (2014) Severe weather in United States under a changing climate. Eos Trans Am Geophys Union 95(18):149–150. https://doi.org/10.1002/2014eo180001

    Article  Google Scholar 

  37. Yao Z, Gupta K (2008) Backbone-based roadmaps for robot navigation in sensor networks. In: Proceedings of international conference on robotics and automation, pp 1023–1029

  38. Zhao H, Winter S, Tomko M (2017) Integrating decentralized indoor evacuation with information depositories in the field. ISPRS Int J Geo Inf 6(7):213

    Article  Google Scholar 

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Acknowledgements

We are grateful to the reviewers of this article for helpful comments and suggestions.

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Authors and Affiliations

  1. University of Oregon, Eugene, OR, 97403-1251, USA

    Mohammad Eshghi & Hedda R. Schmidtke

Authors
  1. Mohammad Eshghi
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  2. Hedda R. Schmidtke
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Corresponding author

Correspondence to Hedda R. Schmidtke.

Appendix

Appendix

This section illustrates the results of the experiments for the other three groups of flood type. It should be mentioned that, as shown in Table 4, because the flood happens after creating the cars, there are flooded cars for static flood type as well. Therefore, these cars are not flooded due to the proposed algorithm, and we can say that, in these cases, the performance of the algorithm should be evaluated without counting the number of flooded cars. Hence, as expected, 100% of cars with reachable destination could successfully reach their destinations. In addition, our algorithm cannot identify all the destinations that are on an island. As shown in Table 4, our algorithm could just identify some of the destinations on islands with certainty.

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Eshghi, M., Schmidtke, H.R. An approach for safer navigation under severe hurricane damage. J Reliable Intell Environ 4, 161–185 (2018). https://doi.org/10.1007/s40860-018-0066-1

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