Advertisement

IICAR-Inheritance Inspired Context Aware Routing Protocol for opportunistic networks

  • Aman Bansal
  • Apoorv Gupta
  • Deepak Kr. SharmaEmail author
  • Varshika Gambhir
Original Research

Abstract

Opportunistic network (OppNet) is a network of wirelessly connected nodes with an unstable network topology where the nodes transmit messages to each other opportunistically if they fall in the same communication range. They face various challenges with respect to stable connections, high message delivery ratio, fast delivery of messages and many more aspects. The prime reason for such challenges is that the nodes are oblivious of the network topology and there never exists an end to end path between the source and the destination. Despite these issues, opportunistic networks promise to provide a highly connected world in the coming future and therefore, intense research is going on to develop efficient routing protocols for the same. In this paper, a generalized context aware routing method named as Inheritance Inspired Context Aware Routing Protocol (IICAR) based on Mendel’s Laws of Inheritance is proposed. The protocol utilizes the context information stored by the nodes and takes intelligent decisions by predicting the path a message may take. The simulation results obtained for IICAR protocol reflects that the proposed protocol provides high message delivery ratio along with less consumption of network resources when compared to the Epidemic, Prophet, Hibop and HBPR routing protocols.

Keywords

Opportunistic networks Mobile ad-hoc networks Routing protocol Laws of Inheritance One simulator 

Notes

References

  1. Alslaim MN, Alaqel HA, Zaghloul SS (2014) A comparative study ofMANET routing protocols. In: Third International Conference one-Technologies and Networks for Development (ICeND), Beirut, pp 178-182Google Scholar
  2. Boldrini C, Conti M, Jacopo J, Andrea P (2007) Hibop: a historybased routing protocol for opportunistic networks. In: 2007 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, pp 1-12Google Scholar
  3. Crawford B, Soto R, Cuesta R, Paredes F (2014) Application of the artificial bee colony algorithm for solving the set covering problem. Article Id, In: The Scientific World Journal, p 189164Google Scholar
  4. Das MK, Kumar K, Barman T, Sahoo P (2014) Application of artificial bee colony algorithm for optimization of MRR and surface roughness in EDM of EN31 tool steel. Proced Mater Sci 6(1):741–751CrossRefGoogle Scholar
  5. Dhurandher SK, Sharma DK, Woungang I (2014) GAER: genetic algorithm based energy efficient routing protocol for infrastructure less opportunistic networks. J Supercomput 69(3):1183–1214CrossRefGoogle Scholar
  6. Dhurandher SK, Borah SJ, Obaidat SJ, Sharma DK, Gupta S, Baruah B (2015) Probability-based controlled flooding in opportunistic networks. In: Proceedings of 12th International Joint Conference one-Business and Telecommunications (ICETE), pp 3-8Google Scholar
  7. Dhurandher SK, Sharma DK, Woungang I, Bhati S (2013) HBPR: History Based Prediction for Routing in infrastructure-less opportunistic networks. In: 27th IEEE International Conference on Advanced Information, Networking and Applications (AINA), pp 931-936Google Scholar
  8. Dhurandher SK, Borah S, Woungang I, Sharma DK, Arora K, Agarwal D (2016) EDR: an encounter and distance based routing protocol for opportunistic networks. In: 30th IEEE International Conference of Advanced Information Networking and Applications, pp 297-302Google Scholar
  9. Dhurandher SK, Sharma DK, Woungang I, Chao I (2011) Performance evaluation of various routing protocols in Opportunistic Networks. In: Proceedings of the IEEE GLOBECOM Workshops, Houston, pp 1067-1071Google Scholar
  10. Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimisation. IEEE Comput Intell Mag 1(4):28–39CrossRefGoogle Scholar
  11. Griffiths AJF, Miller JH, Suzuki DT (2000) An introduction to genetic analysis, Ed. 7, New York, W. H. Freeman, 2000, Figure 2-10[Punnett square showing predicted genotypic], [Online]https://www.ncbi.nlm.nih.gov/books/NBK22098/figure/A220/. Accessed 10 Apr 2017
  12. Huang CM, Lan KC, Tsai CZ (2008) A survey of opportunistic networks. In: Advanced Information Networking andApplications-Workshops, pp 1672-1677Google Scholar
  13. Karaboga D (2005) An idea based on honey bee swarm for numerical optimisation, Technical Report-TR06. Erciyes University, Turkiye, Computer Engineering DepartmentGoogle Scholar
  14. Karjaluoto H (2006) An investigation of third generation (3G) mobile technologies and services. Contemp Manag Res 2(2):91–104CrossRefGoogle Scholar
  15. Keranen A, Ott J, Karkkainen T (2009) The ONE simulator for DTNprotocol evaluation. In: Proceedings of the 2nd international conferenceon simulation tools and techniques. ICST (Institute for ComputerSciences, Social-Informatics and Telecommunications Engineering)Google Scholar
  16. Khanna K, Arora SM (2016) Ant colony optimisation towards image processing. Indian J Sci Technol 9(48):1–9CrossRefGoogle Scholar
  17. Laird NM, Lange C (2011) The fundamentals of modern statistical genetics. In: Statistics for Biology and Health, Springer Science + Business Media. LLC. https://doi.org/10.1007/978-1-4419-7338-2_2
  18. Lebedev BK, Lebedev OB, Lebedeva EM (2014) Set covering on the basis of the ant algorithm. In: IEEE East-West Design and Test Symposium (EWDTS), pp 1-4Google Scholar
  19. Lilien L, Kamal ZH, Bhuse V, Gupta A (2007) The concept of opportunistic networks and their research challenges in privacy and security. In: Mobile and wireless network security and privacy. Springer, pp 85-117Google Scholar
  20. Lindgren A, Avri D, Olov S (2003) Probabilistic routing in intermittently connected networks. ACM SIGMOBILE Mobile Comput Commun Rev 7(3):19–20CrossRefGoogle Scholar
  21. Nahideh D, Sabaei M, Rahmani AM (2016) Sharing spray and wait routing algorithm in opportunistic networks. Wireless Netw 22(7):2403–2414CrossRefGoogle Scholar
  22. Pelusi L, Passarella A, Conti M (2006) Opportunistic networking: data forwarding in disconnected mobile ad hoc networks. IEEE Commun Mag 44(11):134–141CrossRefGoogle Scholar
  23. Spyropoulos T, Psounis K, Raghavendra CS (2005) Spray and wait: anefficient routing scheme for intermittently connected mobilenetworks. In: Proceedings of the 2005 ACM SIGCOMM workshop onDelay-tolerant networking, pp 252-259Google Scholar
  24. Spyropoulos T, Psounis K, Raghavendra CS (2007) Spray and focus: efficient mobility-assisted routing for heterogeneous and correlated mobility. In: Fifth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp 79-85Google Scholar
  25. Turguner C and Sahingoz OK (2014) Solving job shop scheduling problem with Ant Colony Optimization. In: IEEE 15th International Symposium on Computational Intelligence and Informatics, pp 385-389Google Scholar
  26. Vahdat A, Becker D (2000) Epidemic routing for partially connected ad hoc networks, Technical Report CS-2000-06. Duke University, Durham, NC, USA, Dept. of Computer ScienceGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Aman Bansal
    • 1
  • Apoorv Gupta
    • 1
  • Deepak Kr. Sharma
    • 1
    Email author
  • Varshika Gambhir
    • 2
  1. 1.Department of Information TechnologyNetaji Subhas Institute of Technology, University of DelhiNew DelhiIndia
  2. 2.Department of Computer EngineeringNetaji Subhas Institute of Technology, University of DelhiNew DelhiIndia

Personalised recommendations