Soft Computing

, Volume 18, Issue 9, pp 1745–1756 | Cite as

Multi-objective performance optimization of a probabilistic similarity/dissimilarity-based broadcasting scheme for mobile ad hoc networks in disaster response scenarios

  • D. G. Reina
  • J. M. León-Coca
  • S. L. Toral
  • E. Asimakopoulou
  • F. Barrero
  • P. Norrington
  • N. Bessis
Focus

Abstract

Communications among crewmembers in rescue teams and among victims are crucial to relieve the consequences and damages of a disaster situation. A common communication system for establishing real time communications between the elements (victims, crewmembers, people living in the vicinity of the disaster scenario, among others) involved in a disaster scenario is required. Ad hoc networks have been envisioned for years as a possible solution. They allow users to establish decentralized communications quickly and using common devices like mobile phones. Broadcasting is the main mechanism used to disseminate information in all-to-all fashion in ad hoc networks. The objective of this paper is to optimize a broadcasting scheme based on similarity/dissimilarity coefficient designed for disaster response scenarios through a multi-objective optimization problem in which several performance metrics such as reachability, number of retransmissions and delay are optimized simultaneously.

Keywords

MANETs Disaster response scenarios Similarity/dissimilarity coefficients Multi-objective optimization 

References

  1. Aschenbruck N, Gerhaps-Padilla E, Gerharz M, Frank M, Martini P (2009) Modelling mobility in disaster area scenarios. Perform Eval 66:773–790CrossRefGoogle Scholar
  2. Aschenbruck N, Ernst R, Gerhards-Padilla G, Schwamborn M (2010) BonnMotion—a mobility scenario generation and analysis tool. SimutoolGoogle Scholar
  3. Aschenbruck N, Frank M, Martini P, Tölle J (2004) Human mobility in MANET disaster area simulation—a realistic approach. In: 29th Annual IEEE international conference on local computer, network (LCN’04)Google Scholar
  4. Burgess J, Gallagher B, Jensen D, Levine B (2006) MaxProp: routing for vehicle-based disruption-tolerant networks. In: 25th Proceedings of IEEE international conference on computer communications (INFOCOM 2006), pp 1–11Google Scholar
  5. Camp T, Williams B (2002) Comparison of broadcasting techniques for mobile ad hoc networks. In: Proceeding of the ACM international symposium on mobile ad hoc networking and computing, pp 194–205Google Scholar
  6. Cartigny J, Simplot D (2003) Border node retransmission based probabilistic broadcast protocols in ad-hoc networks. Telecommun Syst 22:189–204CrossRefGoogle Scholar
  7. Chakeres I, Perkins C (2007) Dynamic MANET on-demand routing protocol. IETF draft-ietf-manet-dymo-10Google Scholar
  8. Ciobanu RI, Reina DG, Dobre C, Toral SL, Johnson P (2013) JDER: a history-based forwarding scheme for delay tolerant networks using Jaccard distance and encountered ration. J Netw Comput Appl. doi:10.1016/j.jnca.2013.09.012
  9. Clausen T, Jacquet P (2003) Optimized link state routing protocol (OLSR). IETF RFC 3626Google Scholar
  10. Coello CA, Lamont GB, Van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems., Genetic and Evolutionary Computation SeriesSpringer, BerlinMATHGoogle Scholar
  11. Conti M, Giordano S, Martin M, Passarella A (2010) From opportunistic networks to opportunistic computing. IEEE Commun Mag 48:126–139CrossRefGoogle Scholar
  12. Deb E, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elistist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput 6:182–186CrossRefGoogle Scholar
  13. Fall K, Varadhan K (2011) The ns manual (formerly ns notes and documentation). http://www.isi.edu/nsnam/ns/ns-documentation.html
  14. Fortin F, De Rainville F, Gardner M, Parizeau M, Gagne C (2012) DEAP: evolutionary algorithms made easy. J Mach Learn Res 13:2171–2175MATHMathSciNetGoogle Scholar
  15. Haas ZJ, Halpern JY, Li L (2002) Gossip-based routing. In: IEEE InfoCom proceedings, pp 1707–1716Google Scholar
  16. Haas ZJ, Halpern JY, Li L (2006) Gossip-based routing. IEEE/ACM Trans Netw 14:479–491CrossRefGoogle Scholar
  17. Hardle W, Simar L (2003) Applied multivariate statistical analysis. In: Method & Data Technologies, SpringerGoogle Scholar
  18. Heissenbuttel M, Braun T, Walchli M, Bernoulli T (2006) Optimized stateless broadcasting in wireless multi-hop networks. In: Proceedings 25th IEEE international conference on computer communications (INFOCOM 2006), pp 1–12Google Scholar
  19. Jianwu L, Yulong S (2013) Community detection in complex networks using extended compact genetic algorithm. Soft Comput 17:925–937CrossRefGoogle Scholar
  20. Jonshon DB, Maltz DA, Broch J (2001) DSR: the dynamic source routing protocol for multi-hop wireless ad hoc networks. In: Perkins CE (ed) Ad hoc networking, (Addison-Wesley, 2001), pp 139–172Google Scholar
  21. Lakshmi Narayanan RG, Ibe OC (2012) A joint network for disaster recovery and search and rescue operations. Comput Netw 56:3347–3373CrossRefGoogle Scholar
  22. Liang O, Sekercioglu YA, Mani N (2006) A survey of multipoint relay based broadcast schemes in wireless ad hoc networks. IEEE Commun Surv Tutor 8:30–46CrossRefGoogle Scholar
  23. Lindgren A, Doria A, Schelén O (2003) Probabilistic routing in intermittently connected networks. ACM SIGMOBILE Mobile Comput Commun Rev 7:19–20CrossRefGoogle Scholar
  24. Martí R, Robles S, Martín-Campillo A, Cucurull J (2009) Providing early resource allocation during emergencies: the mobile triage tag. J Netw Compu Appl 32:1167–1182CrossRefGoogle Scholar
  25. Martín-Campillo A, Crowcroft J, Yoneki E, Martí R (2013) Evaluating opportunistic networks in disaster scenarios. J Netw Comput Appl 36:870–880CrossRefGoogle Scholar
  26. McEntire DA (2007) Disaster response and recovery. WileyGoogle Scholar
  27. Neumann N, Aichele C, Lindner M, Wunderlich S (2008) Better approach to mobile ad-hoc networking (B.A.T.M.A.N). IETF draftopenmesh-b-a-t-m-a-n-00Google Scholar
  28. Palmeri F, Castigliore A (2012) Condensation-based routing in mobile ad-hoc networks. Mobile Inform Syst 3:199–211Google Scholar
  29. Palmeri F (2013) Scalable service discovery in ubiquitous and pervasive computing architectures: a percolation-driven approach. Futur Gener Comput Syst 29:693–703CrossRefGoogle Scholar
  30. Panichpapiboon S, Cheng L (2013) Irresponsible forwarding under real inter-vehicle spacing distribution. IEEE Trans Veh Technol 62:2264–2272CrossRefGoogle Scholar
  31. Perkins CE, Royer ME (1999) Ad-hoc on-demand distance vector routing. In: Proceedings of IEEE workshop on mobile computing systems and applications (WMCSA), pp 1–11Google Scholar
  32. Raffelsberger C, Hellwagner H (2012) Evaluation of MANET routing protocols in a realistic emergency response scenario. In: 10th International workshop on intelligent solutions in embedded systems, pp 88–92Google Scholar
  33. Reina DG, Toral SL, Barrero F, Bessis N, Asimakopoulou E (2011) Evaluation of ad hoc networks in disaster scenarios. In: 3rd international conference on intelligent networking and collaborative systems (INCOS, 2011), pp 759–764Google Scholar
  34. Reina DG, Toral SL, Bessis N, Barrero F, Asimakopoulou E (2013) An evolutionary computation approach for optimizing broadcasting in disaster response scenarios. In: 7th International conference on innovative mobile and internet services in ubiquitous, computing, pp 94–100Google Scholar
  35. Reina DG, Toral SL, Johnson P, Barrero F (2013) Improving discovery phase of reactive ad hoc routing protocols using Jaccard distance. J Supercomput. doi:10.1007/s11227-013-0992-x
  36. Reina DG, Hinojo JM, Toral SL, Barrero F, Cortés F, Soto M, Marsal E (2010) A wireless in-door system for assisting victims and rescue equipments in a disaster management. Intell Netw Collab Syst (INCOS, 2010)Google Scholar
  37. Reina DG, Toral SL, Barrero F, Bessis N, Asimakopoulou E (2012) Modelling and assessing ad hoc networks in disaster scenarios. J Amb Intell Humaniz Comput. doi:10.1007/s12652-012-0113-3
  38. Reina DG, Toral SL, Johnson P, Barrero F (2012) Route duration improvement in wireless sensor and actuator networks based on mobility parameters and flooding control. EURASIP J Wirel Commun Netw 147:1–25Google Scholar
  39. Reina DG, Toral SL, Johnson P, Barrero F (2013) Hybrid flooding scheme for mobile ad hoc networks. IEEE Commun Lett 17:592–595CrossRefGoogle Scholar
  40. Reina DG, Toral SL, Barrero F, Bessis N, Asimakopoulou E (2013) The role of ad hoc networks in the internet of things. Internet Things Inter Co-op Comput Technol Collect Intell 340:89–113Google Scholar
  41. Reina DG, Toral SL, Bessis N, Barrero F, Asimakopoulou E (2013) An evolutionary computation approach for optimizing connectivity in disaster response scenarios. Appl Soft Comput 13:833–845CrossRefGoogle Scholar
  42. Sakar SK, Basavaraju TG, Puttamadappa C (2008) Ad hoc mobile wireless networks: principles protocols, and applications. Auerbach Publications, Taylor & Francis Group, NY, Oxford 24Google Scholar
  43. Stojmenovic I, Seddigh M, Zunic J (2002) Dominating sets and neighbor elimination-based broadcasting algorithms in wireless networks. IEEE Trans Parallel Distrib Syst 13:14–25CrossRefGoogle Scholar
  44. Terrestrial Trunked Radio (2010) Voice plus data (V+D), part 2: air interface (AI), European standard (telecommunication series). ETSI EN 300:392Google Scholar
  45. Tetrapol specifications (1998) Part 1: general network design, part 2: voice and data services in network and direct mode. http://www.tetrapol.com. Accessed 19 Aug 2013
  46. Tseng YC, Ni SY., Chen YS, Sheu JP (2002) The broadcast storm problem in a mobile ad hoc network. Wirel Netw 8:153–167Google Scholar
  47. Wisitpongphan N, Tonguz OK, Parikh JS, Mudalige P, Bai F, Sadekar V (2007) Broadcast storm mitigation techniques in vehicular ad hoc networks. IEEE Wirel Commun 14(6):84–94CrossRefGoogle Scholar
  48. Yi-Chun X, Bangjun L, Emile AH (2013) Constrained particle swarm algorithms for optimizing coverage of large-scale camera networks with mobile nodes. Soft Comput 17:1047–1057 Google Scholar
  49. Yuan Y, Chen H, Jia M (2005) An optimized ad-hoc on-demand multipath distance vector (AOMDV) routing protocol. In: Asia-Pacific conference on communications, pp 569–573Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • D. G. Reina
    • 1
  • J. M. León-Coca
    • 1
  • S. L. Toral
    • 1
  • E. Asimakopoulou
    • 2
  • F. Barrero
    • 1
  • P. Norrington
    • 3
  • N. Bessis
    • 2
    • 4
  1. 1.Electronic Engineering DepartmentUniversity of SevilleSevilleSpain
  2. 2.School of Computing and MathsUniversity of DerbyDerbyUK
  3. 3.Institute for Research in Applicable ComputingUniversity of BedfordshireLutonUK
  4. 4.Department of Computer Science and TechnologyUniversity of BedfordshireBedfordshireUK

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