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kROp: k-Means clustering based routing protocol for opportunistic networks


Routing and forwarding of messages is a challenging task in Opportunistic networks (Oppnets) due to no fixed architecture and the intermittent connectivity of the nodes. This paper proposes a context-aware routing protocol named kROp, which uses a variety of network features generated dynamically for making routing decisions. Four network features have been defined in this work which is vital for identifying potentially good message forwarders. kROp utilizes unsupervised machine learning in the form of an optimized k-Means clustering algorithm to train on these features and make next hop selection decisions. The performance of kROp is evaluated and compared with HBPR, ProWait and PRoPHET routing protocols in terms of hop count, dropped messages, delivery probability, and overhead ratio. Index terms: kROp, Opportunistic Routing, Unsupervised Learning, k-Means Clustering, Context-Aware Routing, Delivery Probability, Average Hop Count, Wireless Networking.

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  1. Arthur D, Vassilvitskii S (2007) k-means++: the advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on discrete algorithms. Society for Industrial and Applied Mathematics, New Orleans, LA, 7–9 January 2007, pp 1027–1035

  2. Boldrini C, Conti M, Jacopo J, Andrea P (2007) Hibop: a history based routing protocol for opportunistic networks. In: 2007 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, pp 1–12

  3. Borah SJ, Dhurandher SK, Woungang I, Kumar V (2017) A game theoretic context-based routing protocol for opportunistic networks in an IoT scenario. Comput Netw 129:572–584

    Article  Google Scholar 

  4. Borah SJ, Dhurandher SK, Woungang I et al (2017) A multi-objectives based technique for optimized routing in opportunistic networks. J Ambient Intell Human Comput.

    Google Scholar 

  5. Burgess J, Gallagher B, Jensen D, Levine BN (2006) MaxProp: routing for vehicle-based disruption-tolerant networks. INFOCOM 6:1–11

    Google Scholar 

  6. Burns B, Brock O, Levine B (2005) MV routing and capacity building in disruption tolerant networks. In: Proceedings of IEEE 24th annual joint conference of the IEEE Computer and Communications Societies, Miami, FL, USA, 13–17 March 2005, pp 398–408

  7. Celebi ME, Kingravi HA, Vela PA (2013) A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Syst Appl 40(1):200–210

    Article  Google Scholar 

  8. Conti M, Crowcroft J, Giordano S et al (2009) Routing issues in opportunistic networks. In: Middleware for network eccentric and mobile applications. Springer, Berlin, Heidelberg, pp 121–147

  9. Dhurandher SK, Borah SJ, Obaidat MS, Sharma DK, Gupta S, Baruah B (2015) Probability-based controlled flooding in opportunistic networks. In: Proceedings of WINSYS 2015 international conference on wireless information networks and systems, Colmar, Alsace, France, 20–22 July 2015, pp 3–8

  10. 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: Proceedings of 30th IEEE international conference of advanced information networking and applications (AINA), Crans-Montana, Switzerland, 23–25 March 2016, pp 297–302

  11. Dhurandher SK, Sharma DK, Woungang I, Bhati S (2013) HBPR: history based prediction for routing in infrastructure-less opportunistic networks. In: Proceedings of 27th IEEE international conference on advanced information networking and applications (AINA-2013), Barcelona, Spain, 25–28 March 2013, pp 931–936

  12. Fall K (2003) A delay-tolerant network architecture for challenged internets. In: Proceedings of SIGCOMM 2003, conference on applications, technologies, architectures, and protocols for computer communications, Karlsruhe, Germany, 25–29 August 2003, pp 27–34

  13. Hartigan JA, Wong MA (1979) Algorithm AS 136: a k-means clustering algorithm. J R Stat Soc Series C (Appl Stat) 28(1):100–108

    MATH  Google Scholar 

  14. Huang CM, Lan KC, Tsai CZ (2008) A survey of opportunistic networks. In: Proceedings of the 22nd international conference on advanced information networking and applications-workshops, 2008 (AINAW 2008), Okinawa, Japan, 25–28 March 2008, pp 1672–1677

  15. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv (CSUR) 31(3):264–323

    Article  Google Scholar 

  16. Keranen A, Andott J (2007) Increasing reality for DTN protocol simulations. Special Technical Report, Helsinki University of Technology, Networking Laboratory.

  17. Keranen A, Ott J, Karkkainen T (2009) The ONE simulator for DTN protocol evaluation. In: Proceedings of the 2nd international conference on simulation tools and techniques. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)

  18. Lindgren A, Avri D, Olov S (2003) Probabilistic routing in intermittently connected networks. ACM SIGMOBILE Mob Comput Commun Rev 7(3):19–20

    Article  Google Scholar 

  19. Mehmood A, Lv Z, Lloret J, Umar MM (2017) ELDC: an artificial neural network based energy-efficient and robust routing scheme for pollution monitoring in WSNs. IEEE Trans Emerg Topics Comput 99:1–1

    Article  Google Scholar 

  20. Musolesi M, Cecilia M (2009) Car: context-aware adaptive routing for delay-tolerant mobile networks. IEEE Trans Mob Comput 8(2):246–260

    Article  Google Scholar 

  21. Nelson SC, Bakht M, Kravets R (2018) Encounter based routing in DTNs. ACM SIGMOBILE Mob Comput Commun Rev 13(1):56–59

  22. Pelusi L, Passarella A, Conti M (2006) Opportunistic networking: data forwarding in disconnected mobile ad hoc networks. IEEE Commun Mag 44(11):134–141

    Article  Google Scholar 

  23. Sonnenburg S, Raetsch G, Henschel S, Widmer C, Behr J, Zien A, de Bona Fabio, Binder A, Gehl C, Franc V (2010) The SHOGUN machine learning toolbox. J Mach Learn Res 11:1799–1802

    MATH  Google Scholar 

  24. Spyropoulos T, Psounis K, Raghavendra CS (2005) Spray and wait: an efficient routing scheme for intermittently connected mobile networks. In: Proceedings of the 2005 ACM SIGCOMM workshop on delay-tolerant networking (WDTN ’05), Philadelphia, PA, USA, 22–26 August 2005, pp 252–259

  25. 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 Science

  26. Zhang Z (2006) Routing in intermittently connected mobile ad hoc networks and delay tolerant networks: overview and challenges. IEEE Commun Surv Tutor 8(1):24–37

    Article  Google Scholar 

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Correspondence to Deepak Kumar Sharma.

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Sharma, D.K., Dhurandher, S.K., Agarwal, D. et al. kROp: k-Means clustering based routing protocol for opportunistic networks. J Ambient Intell Human Comput 10, 1289–1306 (2019).

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  • Probabilistic Routing Protocol Using History Of Encounters And Transitivity (PRoPHET)
  • OppNets
  • Delivery Probability
  • Context-aware Protocols
  • Overhead Ratio