Abstract
When two opportunistic network peers encounter, utility functions are generally employed to select the messages that have to be exchanged, with the purpose of maximizing message delivery probability and reduce congestion. These functions compute weighted sums of various parameters, like centrality, similarity, and trust. Most of the existing solutions statically compute the weights based on offline observations and apply the same values regardless of a node’s context. However, mobile networks are not necessarily constant in terms of behavior and characteristics, so the classic approach might not be suitable. The network might be split into sub-networks, which behave differently from each other. Thus, in this paper, we show that, by dynamically adjusting the behavior of a node based on its context, through the adjustment of the utility function on the fly, the opportunistic forwarding process can be improved. We show that nodes behave differently from each other and have different views of the network. Through real-life trace-based simulations, we prove that our solution is feasible and is able to improve an opportunistic network’s performance from the standpoint of hit rate, latency, and delivery cost.







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References
Pelusi L, Passarella A, Conti M (2006) Opportunistic networking: data forwarding in disconnected mobile ad hoc networks. Comm Mag 44(11):134–141
Ciobanu RI, Dobre C, Cristea V, Pop F, Xhafa F (2015) Sprint-self: social-based routing and selfish node detection in opportunistic networks. Mob Inf Syst, 2015
Wei K, Liang X, Xu K (2014) A survey of social-aware routing protocols in delay tolerant networks applications, taxonomy and design-related issues. IEEE Commun Survey Tutor 16(1):556–578
Zhang Z. (2006) Routing in intermittently connected mobile ad hoc networks and delay tolerant networks: overview and challenges. IEEE Commun Survey Tutor 8(1):24–37
Khabbaz MJ, Assi CM, Fawaz WF (2012) Disruption-tolerant networking: a comprehensive survey on recent developments and persisting challenges. IEEE Commun Survey Tutor 14(2):607–640
Zhu Y, Xu B, Shi X, Wang Y (2013) A survey of social-based routing in delay tolerant networks positive and negative social effects. IEEE Commun Survey Tutor 15(1):387–401
Vahdat A, Becker D (2000) Epidemic routing for partially-Connectedad hoc networks. Technical report, Duke University
Hui P, Crowcroft J, Yoneki E (2008) BUBBLE: rap social-based forwarding in delay tolerant networks. In: Proceedings of the 9th ACM International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc ’08. ACM, New York, pp 241–250
Reina D, Ciobanu RI, Toral SL, Dobre C (2016) A multi-objective optimization of data dissemination in delay tolerant networks. Expert Syst Appl 57(C):178–191
Guo S, Derakhshani M, Falaki MH, Ismail U, Luk R, Oliver EA, Ur Rahman S, Seth A, Zaharia MA, Keshav S (2011) Design and implementation of the kiosknet system. Comput Netw 55(1):264–281
Juang P, Oki H, Wang Y, Martonosi M, Li SP, Rubenstein D (2002) Energy-efficient computing for wildlife tracking Design tradeoffs and early experiences with zebranet. SIGARCH Comput Archit News 30(5):96–107
Reina DG, Askalani M, Toral SL, Barrero F, Asimakopoulou E, Bessis N (2015) A survey on multihop ad hoc networks for disaster response scenarios. In: International Journal of Distributed Sensor Networks, pp 1–16
Ciobanu RI, Reina DG, Dobre C, Toral SL, Johnson P (2014) Jder: a history-based forwarding scheme for delay tolerant networks using jaccard distance and encountered ration. J Netw Comput Appl 40:279–291
Daly EM, Haahr M (2007) Social network analysis for routing in disconnected delay-tolerant manets. In: Proceedings of the 8th ACM International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc ’07. ACM, New York, pp 32–40
Lindgren A, Doria A, Schelén O (2003) Probabilistic routing in intermittently connected networks. SIGMOBILE Mobile Comput Commun Rev 7(3):19–20
Zeng W, Li F, Zhao N (2017) Effective social relationship measurement and cluster based routing in mobile opportunistic networks. Sensors
Dou W, Li Y, Zeng F (2017) Energy-efficient contact detection model in mobile opportunistic networks. In: International conference on wireless algorithms, systems, and applications, pp 60–70
Boldrini C, Passarella A (2010) Hcmm: modelling spatial and temporal properties of human mobility driven by users’ social relationships. Comput Commun 33(9):1056–1074
Ciobanu R-I, Dobre C, Reina DG, Toral SL (2017) A dynamic data routing solution for opportunistic networks. In: 2017 14Th international conference on telecommunications (conTEL). IEEE, pp 83–90
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The work presented in this paper is co-funded by the European Union, under the project MONROE “Measuring Mobile Broadband Networks in Europe”.
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Ciobanu, RI., Reina, D.G., Dobre, C. et al. Context-adaptive forwarding in mobile opportunistic networks. Ann. Telecommun. 73, 559–575 (2018). https://doi.org/10.1007/s12243-018-0654-3
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DOI: https://doi.org/10.1007/s12243-018-0654-3