skip to main content
research-article

Optimized Cooperative Dynamic Coverage in Mixed Sensor Networks

Published:17 February 2015Publication History
Skip Abstract Section

Abstract

This article considers the problem of improving the dynamic coverage and event detection time of mixed wireless sensor networks (WSNs). We consider mixed WSNs that consist of sparse static sensor deployments and mobile sensors that move continuously to monitor uncovered (vacant) areas in the sensor field. Mobile sensors move autonomously and cooperatively by executing a path planning algorithm. Using a simplified scenario, the article derives the optimal path strategy for a single mobile sensor to search two nonconnected uncovered regions with the minimum average detection delay or with the maximum dynamic coverage. The resulting optimal strategy confirms that it is better to search areas that are less likely to hide a target but are located closer to the mobile node, rather than heading toward the most likely area. Based on the insights gained from the simplified scenario and the theory of coverage processes, the article proposes a surrogate method to approximate the best searching neighborhood radius (a design parameter of the path planning algorithm) that optimizes the dynamic coverage and event detection time capabilities of mixed WSN deployments. Extensive simulation results indicate that this approach can achieve very good results, both for a single and for multiple collaborating mobile sensors.

References

  1. V. Ablavsky and M. Snorrason. 2000. Optimal search for a moving target: A geometric approach. In Proceedings of AIAA Guidance, Navigation, and Control Conference.Google ScholarGoogle ScholarCross RefCross Ref
  2. J. Barraquand, B. Langlois, and J.-C. Latombe. 1992. Numerical potential field techniques for robot path planning. IEEE Transactions on Systems, Man and Cybernetics 22, 2 (1992), 224--241.Google ScholarGoogle ScholarCross RefCross Ref
  3. N. Bartolini, T. Calamoneri, T. F. La Porta, and S. Silvestri. 2011. Autonomous deployment of heterogeneous mobile sensors. IEEE Transactions on Mobile Computing 10, 6 (2011), 753--766. DOI:http://dx.doi.org/10.1109/TMC.2010.192 Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. T. Bektas. 2006. The multiple traveling salesman problem: An overview of formulations and solution procedures. Omega 34, 3 (2006), 209--219.Google ScholarGoogle ScholarCross RefCross Ref
  5. L. Bondesson and J. Fahlen. 2003. Mean and variance of vacancy for hard-core disc processes and applications. Scandinavian Journal of Statistics 30, 4 (2003), 797--816.Google ScholarGoogle ScholarCross RefCross Ref
  6. P. Brass. 2007. Bounds on coverage and target detection capabilities for models of networks of mobile sensors. ACM Transactions on Sensor Networks 3, 2 (2007), 1--19. DOI:http://dx.doi.org/10.1145/1240226.1240229 Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. S. Chiu, D. Stoyan, W. Kendall, and J. Mecke. 2013. Stochastic Geometry and Its Applications. John Wiley & Sons.Google ScholarGoogle Scholar
  8. T. H. Chung, G. A. Hollinger, and V. Isler. 2011. Search and pursuit-evasion in mobile robotics. Autonomous Robots 31, 4 (Nov. 2011), 299--316. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. T. Cormen, C. Leiserson, R. Rivest, and C. Stein. 2001. Introduction to Algorithms (2nd ed.). MIT Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. J. Cortes. 2010. Coverage optimization and spatial load balancing by robotic sensor networks. IEEE Transactions on Automatic Control 55, 3 (March 2010), 749--754. DOI:http://dx.doi.org/10.1109/TAC.2010.2040495Google ScholarGoogle ScholarCross RefCross Ref
  11. J. Cortes, S. Martinez, T. Karatas, and F. Bullo. 2004. Coverage control for mobile sensing networks. IEEE Transactions on Robotics and Automation 20 (2004), 243--255.Google ScholarGoogle ScholarCross RefCross Ref
  12. A. Deshpande, S. Poduri, D. Rus, and G. Sukhatme. 2009. Distributed coverage control for mobile sensors with location-dependent sensing models. In Proceedings of the IEEE ICRA’09. 3493--3498. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Y. Elmaliach, N. Agmon, and G. Kaminka. 2009. Multi-robot area patrol under frequency constraints. Annals of Mathematics and Artificial Intelligence 57, 3--4 (2009), 293--320. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. M. Erdelj, V. Loscri, E. Natalizio, and T. Razafindralambo. 2013a. Multiple point of interest discovery and coverage with mobile wireless sensors. Ad Hoc Networks 11, 8 (2013), 2288--2300. DOI:http://dx.doi.org/10.1016/j.adhoc.2013.04.017 Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. M. Erdelj, T. Razafindralambo, and D. Simplot-Ryl. 2013b. Covering points of interest with mobile sensors. IEEE Transactions on Parallel and Distributed Systems 24, 1 (2013), 32--43. DOI:http://dx.doi.org/10.1109/TPDS.2012.46 Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. A. Ghaffarkhah and Y. Mostofi. 2012. Optimal motion and communication for persistent information collection using a mobile robot. In Proceedings of the 2012 IEEE Globecom Workshop. 1532--1537.Google ScholarGoogle Scholar
  17. A. Ghaffarkhah and Y. Mostofi. 2014. Dynamic networked coverage of time-varying environments in the presence of fading communication channels. ACM Transactions on Sensor Networks (TOSN) 10, 3 (2014), 45. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. P. Hall. 1984. Mean and variance of vacancy for distribution of k-dimensional spheres within k-dimensional space. Journal of Applied Probability 21 (1984), 738--752.Google ScholarGoogle ScholarCross RefCross Ref
  19. P. Hall. 1988. Introduction to the Theory of Coverage Processes. John Wiley & Sons.Google ScholarGoogle Scholar
  20. G. Hollinger and G. Sukhatme. 2013. Sampling-based motion planning for robotic information gathering. In Proceedings of the Robotics: Science and Systems Conference (RSS’13).Google ScholarGoogle Scholar
  21. G. Hollinger, Y. Yerramalli, S. Singh, U. Mitra, and G. Sukhatme. 2011. Distributed coordination and data fusion for underwater search. In Proceedings of the IEEE International Conference on Robotics and Automation. 349--355.Google ScholarGoogle Scholar
  22. A. Howard, M. Mataric, and G. Sukhatme. 2002. Mobile sensor network deployment using potential fields: A distributed, scalable solution to the area coverage problem. In Proceedings of Distributed Autonomous Robotic Systems. 299--308.Google ScholarGoogle Scholar
  23. D. Junzhao, L. Yawei, L. Hui, and S. Kewei. 2010. On sweep coverage with minimum mobile sensors. In Proceedings of the 16th IEEE ICPADS. 283--290. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. M. G. Kendall and P. A. P. Moran. 1963. Geometrical Probability. Hafner, New York.Google ScholarGoogle Scholar
  25. B. O. Koopman. 1956. The theory of search II - Target detection. Operations Research 4 (1956), 503--531.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. T. P. Lambrou and C. G. Panayiotou. 2006. Improving area coverage using mobility in sensor networks. In Proceedings of the International Conference on Intelligent Systems And Computing: Theory And Applications (ISYC’06).Google ScholarGoogle Scholar
  27. T. P. Lambrou and C. G. Panayiotou. 2007. Collaborative event detection using mobile and stationary nodes in sensor networks. In Proceedings of the 3rd IEEE CollaborateCom 2007. New York, NY. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. T. P. Lambrou and C.G. Panayiotou. 2009a. Collaborative area monitoring using wireless sensor networks with stationary and mobile nodes. EURASIP Journal on Advances in Signal Processing (2009), 1--16. doi:10.1155/2009/750657. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. T. P. Lambrou and C. G. Panayiotou. 2009b. Distributed collaborative path planning in sensor networks with multiple mobile sensor nodes. In Proceedings of the 17th IEEE MED 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. T. P. Lambrou and C. G. Panayiotou. 2011a. Area coverage vs event detection in monitoring applications using mixed sensor networks. In Proceedings of the 8th World Congress of the International Federation of Automatic Control (IFAC WC’11).Google ScholarGoogle Scholar
  31. T. P. Lambrou and C. G. Panayiotou. 2011b. On the optimal search neighborhood in mixed wireless sensor networks. In Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC’11).Google ScholarGoogle Scholar
  32. T. P. Lambrou and C. G. Panayiotou. 2012a. Online, adaptive, and distributed multi-robot motion planning for collaborative patrolling of sparse sensor networks. In Proceedings of the 25th IEEE IROS 2012 Workshop on Robot Motion Planning: Online, Reactive, and in Real-Time.Google ScholarGoogle Scholar
  33. T. P. Lambrou and C. G. Panayiotou. 2012b. A testbed for coverage control using mixed wireless sensor networks. Journal of Network and Computer Applications 35, 2 (2012), 527--537. doi:10.1016/j.jnca.2011.05.010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. T. P. Lambrou and C. G. Panayiotou. 2013. Collaborative path planning for event search and exploration in mixed sensor networks. International Journal of Robotics Research 32, 12 (2013), 1424--1437. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. S. D. Lawrence. 1975. Theory of Optimal Search. Academic Press, New York.Google ScholarGoogle Scholar
  36. M. Li, W. Cheng, K. Liu, Y. He, X. Li, and X. Liao. 2011. Sweep coverage with mobile sensors. IEEE Transactions on Mobile Computing 10, 11 (Nov. 2011), 1534--1545. DOI:http://dx.doi.org/10.1109/TMC.2010.237 Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. W. Li and C. G. Cassandras. 2005. Distributed cooperative coverage control of sensor networks. In Proceedings of 44rd IEEE Conference on Decision and Control.Google ScholarGoogle Scholar
  38. B. Liu, P. Brass, O. Dousse, P. Nain, and D. Towsley. 2005. Mobility improves coverage of sensor networks. In Proceedings of the ACM MobiHoc 2005 Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. B. Liu, O. Dousse, P. Nain, and D. Towsley. 2013. Dynamic coverage of mobile sensor networks. IEEE Transactions on Parallel and Distributed Systems 24, 2 (2013), 301--311. DOI:http://dx.doi.org/10.1109/TPDS.2012.141 Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. S. Loizou and K. Kyriakopoulos. 2008. Navigation of multiple kinematically constrained robots. IEEE Transactions on Robotics 24, 1 (2008), 221--231. DOI:http://dx.doi.org/10.1109/TRO.2007.912092 Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. N. Nigam and I. Kroo. 2008. Persistent surveillance using multiple unmanned air vehicles. In IEEE Aerospace Conference. 1--14.Google ScholarGoogle Scholar
  42. C. Papadimitriou. 1977. The euclidean travelling salesman problem is NP-complete. Theoretical Computer Science 4, 3 (1977), 237--244.Google ScholarGoogle ScholarCross RefCross Ref
  43. M. Pavone, A. Arsie, E. Frazzoli, and F. Bullo. 2009. Equitable partitioning policies for robotic networks. In Proceedings of the IEEE ICRA ’09. 2356--2361. DOI:http://dx.doi.org/10.1109/ROBOT.2009.5152809 Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. M. Polycarpou, Y. Yang, Y. Liu, and K. Passino. 2003. Cooperative Control Design for Uninhabited Air Vehicles. In Cooperative Control: Models, Applications and Algorithms. Vol. 1. Kluwer Academic Publishers, 283--321.Google ScholarGoogle Scholar
  45. E. Rimon and D. E. Koditschek. 1992. Exact robot navigation using artificial potential fields. IEEE Transactions on Robotics and Automation 8, 5 (1992), 501--518.Google ScholarGoogle ScholarCross RefCross Ref
  46. A. Singh, A. Krause, C. Guestrin, and W. Kaiser. 2009b. Efficient informative sensing using multiple robots. Journal of Artificial Intelligence Research 34, 2 (2009), 707. Google ScholarGoogle ScholarCross RefCross Ref
  47. A. Singh, A. Krause, and W. Kaiser. 2009a. Nonmyopic adaptive informative path planning for multiple robots. In Proceedings of the International Joint Conference on Artificial Intelligence. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. D. Stoyan, W. Kendall, and J. Mecke. 1995. Stochastic Geometry and Its Applications (2nd ed.). John Wiley & Sons.Google ScholarGoogle Scholar
  49. B. Wang, H. Beng Lim, and D. Ma. 2009. A survey of movement strategies for improving network coverage in wireless sensor networks. Computer Communications 32, 13-14 (2009), 1427--1436. DOI:http://dx.doi.org/10.1016/j.comcom.2009.05.004 Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. A. R. Washburn. 2002. Search and Detection (4th ed.). INFORMS.Google ScholarGoogle Scholar
  51. A. R. Washburn and M. Kress. 2009. Combat Models. Springer.Google ScholarGoogle Scholar
  52. T. Wimalajeewa and S. K. Jayaweera. 2010. Impact of mobile node density on detection performance measures in a hybrid sensor network. IEEE Transactions on Wireless Communications 9, 5 (May 2010), 1760--1769. DOI:http://dx.doi.org/10.1109/TWC.2010.05.091012 Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. M. Zhong and C. G. Cassandras. 2011. Distributed coverage control and data collection with mobile sensor networks. IEEE Transactions on Automatic Control 56, 10 (Oct. 2011), 2445--2455. DOI:http://dx.doi.org/10.1109/TAC.2011.2163860Google ScholarGoogle ScholarCross RefCross Ref
  54. Y. Zou and K. Chakrabarty. 2003. Sensor deployment and target localization based on virtual forces. In Proceedings of the IEEE Infocom.Google ScholarGoogle Scholar

Index Terms

  1. Optimized Cooperative Dynamic Coverage in Mixed Sensor Networks

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image ACM Transactions on Sensor Networks
      ACM Transactions on Sensor Networks  Volume 11, Issue 3
      May 2015
      400 pages
      ISSN:1550-4859
      EISSN:1550-4867
      DOI:10.1145/2737802
      • Editor:
      • Chenyang Lu
      Issue’s Table of Contents

      Copyright © 2015 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 17 February 2015
      • Revised: 1 November 2014
      • Accepted: 1 November 2014
      • Received: 1 January 2014
      Published in tosn Volume 11, Issue 3

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader