Keyword-based mobile application sharing

ABSTRACT

The advent and wide adoption of smartphones in the second half of '00s has completely changed our everyday mobile computing experience. Tens of applications are being introduced every day in the application markets. Given the technology progress and the fact that mobile devices are becoming strong computing devices, mobile applications are expected to follow suit and become computation-heavy, bandwidth-hungry and latency-sensitive. In this paper, we introduce a new mobile computing paradigm to alleviate some of the network stress that mobile applications are already putting into the network, e.g., in case of crowded areas and events, where the mobile network effectively collapses. According to this paradigm, users can share the applications that they have on their mobile devices with nearby users that want access to processed information, which their own applications cannot provide. In a sense, then, the client application instance is also acting as a server instance in order to serve requests from nearby users. A representative example is a route-finder application in a busy station, airport, stadium or festival, or a gaming application onboard a flight. Our paradigm builds on Information-Centric Networking (ICN) and uses keyword-based requests to discover shared applications in the vicinity.

References

1. Q. Xu, J. Erman, A. Gerber, Z. Mao, J. Pang, and S. Venkataraman, "Identifying diverse usage behaviors of smartphone apps," in ACM ICN'11, pp. 329--344.
2. I. Wakeman, S. Naicken, J. Rimmer, D. Chalmers, and C. Fisher, "The fans united will always be connected: building a practical dtn in a football stadium," in ADHOCNETS 2013.
3. U. Drolia, N. Mickulicz, R. Gandhi, and P. Narasimhan, "Krowd: A key-value store for crowded venues," in MobiArch 2015, pp. 20--25.
4. J. Scott, P. Hui, J. Crowcroft, and C. Diot, "Haggle: A networking architecture designed around mobile users," in WONS 2006).
5. C. Anastasiades, T. Braun, and V. Siris, "Information-centric networking in mobile and opportunistic networks," in Wireless Networking for Moving Objects, pp. 14--30, 2014.
6. J. Ott and M. J. Pitkanen, "Dtn-based content storage and retrieval," in IEEE WoWMoM 2007.
7. E. Trono, Y. Arakawa, M. Tamai, and K. Yasumoto, "Dtn mapex: Disaster area mapping through distributed computing over a delay tolerant network," in ICMU 2015.
8. J. Ott, E. Hyytia, P. Lassila, T. Vaegs, and J. Kangasharju, "Floating content: Information sharing in urban areas," in IEEE PerCom 2011.
9. G. Xylomenos and et al., "A survey of information-centric networking research," Communications Surveys Tutorials, IEEE, vol. 16, no. 2, 2014.
10. M. Sifalakis, B. Kohler, C. Scherb, and C. Tschudin, "An information centric network for computing the distribution of computations," in ACM ICN 2014.
11. Y. Coady, O. Hohlfeld, J. Kempf, R. McGeer, and S. Schmid, "Distributed cloud computing: Applications, status quo, and challenges," SIGCOMM CCR, vol. 45, Apr. 2015.
12. V. Jacobson, D. K. Smetters, J. D. Thornton, M. F. Plass, N. H. Briggs, and R. L. Braynard, "Networking named content," in ACM CoNEXT'09.
13. D. Camps-Mur and et al., "Enabling always on service discovery: Wifi neighbor awareness networking," Wireless Communications, IEEE, vol. 22, pp. 118--125, April 2015.
14. A. J. Nicholson, S. Wolchok, and B. D. Noble, "Juggler: Virtual Networks for Fun and Profit," IEEE Transactions on Mobile Computing, vol. 9, pp. 31--43, Jan. 2010.