在耐延遲網路中，節點與節點沒有一條端點到端點(end-to-end)之間的傳輸路徑，因為網路中節點的快速移動與傳輸距離限制使得節點分布不均及各種限制。因此節點必須要依靠節點機會性的相遇以儲存、攜帶與轉送的方式將訊息送達至目的地端。由於近年來無線與行動裝置的發展，人類擁有的行動裝置能夠儲存與攜帶訊息，藉由移動接觸其他人類轉送資料。當人類的行為相似於行動節點時，這種人類移動與相遇的情境符合以社群為基底的耐延遲網路。因此在耐延遲網路的環境中研究人類的移動特性有助於新式社群耐延遲網路路由演算法的設計。

本論文探討大量的相關研究，包含耐延遲網路路由與社群路由方法，回顧過去的文獻發現幾項人類的移動性質及社群互相接觸的特性。進而本論文以這些特性設計Leverage機制，在這個機制之中藉由節點與節點過往的接觸時間來判斷這個訊息是否要轉送。並以實驗佐證演算法的效能，使用訊息抵達率、訊息傳遞費用與成功傳遞比例進行量測。實驗結果顯示本論文提出的演算法不僅有較佳的訊息抵達率而且傳遞訊息的次數較少，能有效的降低傳輸費用。

In delay-tolerant networks, it is hardly possible to sustain any end-to-end data delivery paths between any two nodes because the networks suffer from various restrictions by non-uniform node distribution, high node mobility as well as limited transmission ranges. Nodes thus take a store-carry-and-forwarding method to send messages to destinations when they have any opportunistic contacts with other nodes in a network. Considering the recent advance of wireless and mobile networking systems, human beings possessing mobile devices are able to store data in such devices, carry the data along with them, and forward the data to encountered devices as encountering people during movement. As human beings appear like mobile nodes in a network context, the scenarios of human movement and contact may fall into the application domain of social-based delay-tolerant networks. Therefore, the research study of human mobility characteristics will contribute to the design of new social-based routing schemes in delay-tolerant network environments.

The study in this thesis investigates lots of related works, including not only delay-tolerant routing but also social-based routing methods for delay-tolerant networks. This literature review finds out several behavior characteristics about human mobility patterns and contacts by social communities. Accordingly, the study exploits these characteristics to design a leverage routing scheme. In this scheme, message forwarding decision is made by referring to the information of contacts between two nodes in the past. To examine the proposed scheme, simulations are conducted to the performance in terms of message delivery probability, overhead ratio, and successful relay ratio. Performance results indicate that the leverage routing scheme not only has better delivery probability but also results in lower amounts of message transmissions in the network.

[1] K. Fall, “A delay-tolerant network architecture for challenged internets,” in Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications, ser. SIGCOMM ’03. Karlsruhe, Germany: ACM, Aug. 25–29, 2003, pp. 27–34.
[2] P. Hui, A. Chaintreau, J. Scott, R. Gass, J. Crowcroft, and C. Diot, “Pocket switched networks and human mobility in conference environments,” in Proceedings of the 2005 ACM SIGCOMM workshop on Delay-tolerant networking, ser. WDTN ’05, Philadelphia, USA, Mar. 19–23, 2005, pp. 244–251.
[3] T. Spyropoulos, K. Psounis, and C. Raghavendra, “Spray and wait: an efficient routing scheme for intermittently connected mobile networks,” in Proceedings of the 2005 ACM SIGCOMM workshop on Delay-tolerant networking, Philadelphia, PA, Aug. 22–26, 2005, pp. 252–259.
[4] W.-J. Hsu, T. Spyropoulos, K. Psounis, and A. Helmy, “Modeling spatial and temporal dependencies of user mobility in wireless mobile networks,” IEEE/ACM Transactions on Networking, vol. 17, no. 5, pp. 1564 –1577, Oct. 2009.
[5] A. Vahdat and D. Becker, “Epidemic routing for partially connected ad hoc networks,” Department of Computer Science, Duke University, Tech. Rep. CS-2000-06, Apr. 2000.
[6] Y.-F. Hsu and C.-L. Hu, “Erasure coding-based routing for message multicasting in delay-tolerant networks,” in Proceedings of the 2012 IET International Conference on Frontier Computing - Theory, Technologies and Applications (IET FC’12), Xining,China, Aug. 16–18, 2012.
[7] T. Spyropoulos, R. N. R. T. Turletti, K. Obraczka, and A. Vasilakos, “Routing for disruption tolerant networks: taxonomy and design,” Wireless Networks,vol. 16, no. 8, pp. 2349–2370, Nov. 2010.
[8] A. Chaintreau, P. Hui, J. Crowcroft, C. Diot, R. Gass, and J. Scott, “Impact of human mobility on opportunistic forwarding algorithms,” IEEE Transactions on Mobile Computing, vol. 6, no. 6, pp. 606–620, Jun. 2007.
[9] T. Karagiannis, J.-Y. L. Boudec, and M. Vojnović, “Power law and exponential decay of intercontact times between mobile devices,” IEEE Transactions on Mobile Computing, vol. 9, no. 10, pp. 1377–1390, Oct. 2010.
[10] A. Lindgren, A. Doria, and O. Schelén, “Probabilistic routing in intermittently connected networks,” SIGMOBILE Mob. Comput. Commun. Rev., vol. 7, no. 3,pp. 19–20, Jul. 2003.
[11] T. Spyropoulos, K. Psounis, and C. S. Raghavendra, “Spray and focus: Efficient mobility-assisted routing for heterogeneous and correlated mobility,” in Proceedings of the Fifth IEEE International Conference on Pervasive Computing and Communications Workshops, New York, USA, Mar. 19–23, 2007, pp.79–85.
[12] E. Daly and M. Haahr, “Social network analysis for information flow in disconnected delay-tolerant manets,” IEEE Transactions on Mobile Computing,vol. 8, no. 5, pp. 606 –621, May 2009.
[13] J. Wu and Y. Wang, “Social feature-based multi-path routing in delay tolerant networks,” in Proceedings of IEEE INFOCOM, Florida,USA, Mar. 25–30, 2012,pp. 1368 –1376.
[14] A.-K. Pietilänen and C. Diot, “Dissemination in opportunistic social networks: The role of temporal communities,” in Proceedings of the Thirteenth ACM Tnternational Symposium on Mobile Ad Hoc Networking and Computing(MobiHoc’12), Hilton Head Island, South Carolina, Jun. 11–14, 2012, pp.165–174.
[15] J. Tang, M. Musolesi, C. Mascolo, and V. Latora, “Characterising temporal distance and reachability in mobile and online social networks,” ACM SIGCOMM Computer Communication Review (CCR), vol. 40, no. 1, pp. 118–124,Jan. 2010.
[16] M. E. J. Newman, “Modularity and community structure in networks,” Proceedings of the National Academy of Sciences, vol. 103, no. 23, pp. 8577–8582,Jun. 2006.
[17] P. Hui, J. Crowcroft, and E. Yoneki, “Bubble rap: Social-based forwarding in delay-tolerant networks,” IEEE Transactions on Mobile Computing, vol. 10,no. 11, pp. 1576 –1589, Nov. 2011.
[18] G. Palla, I. Dere´nyi, I. Farkas, and T. Vicsek, “Uncovering the overlapping community structure of complex networks in nature and society,” Nature, vol.435, no. 7043, pp. 814–818, Apr. 2005.
[19] P. Hui and J. Crowcroft, “How small labels create big improvements,” in Fifth Annual IEEE International Conference on Pervasive Computing and Communications Workshops(PerCom Workshops ’07), New York, USA, Mar. 19–23, 2007, pp. 65–70.
[20] W. Gao, G. Cao, T. L. Porta, and J. Han, “On exploiting transient social contact patterns for data forwarding in delay-tolerant networks,” IEEE Transactions on Mobile Computing, vol. 12, no. 1, pp. 151 –165, Jan. 2013.
[21] K. Lee, S. Hong, S. J. Kim, I. Rhee, and S. Chong, “Slaw: Self-similar least action human walk,” IEEE/ACM Transactions on Networking, vol. 20, no. 2,pp. 515 –529, Apr. 2012.
[22] I. Rhee, M. Shin, S. Hong, K. Lee, S. J. Kim, and S. Chong, “On the levy-walk nature of human mobility,” IEEE/ACM Transactions on Networking, vol. 19,no. 3, pp. 630–643, Jun. 2011.
[23] M. C. Gonza´lez, C. A. Hidalgo, and A.-L. Baraba´si, “Understanding individual human mobility patterns,” Nature, vol. 453, no. 7196, pp. 779–782, Jun.2008.
[24] D. Brockmann, L. Hufnagel, and T. Geisel, “The scaling laws of human travel,”Nature, vol. 439, no. 7075, pp. 462–465, Jan. 2006.
[25] C. Boldrini and A. Passarella, “Hcmm: Modelling spatial and temporal properties of human mobility driven by users’ social relationships,” Computer Communications, vol. 33, no. 9, pp. 1056 – 1074, Jun. 2010.
[26] M. Musolesi and C. Mascolo, “Designing mobility models based on social network theory,” ACM SIGMOBILE Mobile Computing and Communications Review, vol. 11, no. 3, pp. 59–70, Jul. 2007.
[27] Frans, Ekman, A. Keränen, J. Karvo, and J. Ott, “Working day movement
model,” in Proceedings of the 1st ACM SIGMOBILE workshop on Mobility models, ser. MobilityModels ’08. Hong Kong SAR, China: ACM, May 27–30, 2008, pp. 33–40.
[28] V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, “Fast unfolding of communities in large networks,” Journal of Statistical Mechanics: Theory and Experiment, vol. 2008, no. 10, p. P10008, Oct. 2008.
[29] T. Hossmann, T. Spyropoulos, and F. Legendre, “Putting contacts into context: Mobility modeling beyond inter-contact times,” in Proceedings of the Twelfth ACM International Symposium on Mobile Ad Hoc Networking and Computing(MobiHoc’11), Paris, France, May 16–20, 2011, pp. 18:1–18:11.
[30] N. P. Nguyen, T. N. Dinh, Y. Xuan, and M. T. Thai, “Adaptive algorithms for detecting community structure in dynamic social networks,” in Proceedings of IEEE INFOCOM, Shanghai, China, Apr. 10–15, 2011, pp. 2282 –2290.
[31] W. jen Hsu and A. Helmy, “On modeling user associations in wireless lan traces on university campuses,” in Proceedings of the 2nd International Workshop On Wireless Network Measurement, Boston,USA, Apr. 3, 2006.
[32] A. Keränen, J. Ott, and T. Kärkkäinen, “The one simulator for dtn protocol evaluation,” in Proceedings of the 2nd International Conference on Simulation Tools and Techniques, Rome, Italy, Mar. 2-6, 2009, pp. 55:1–55:10.
[33] J. Ellson, E. Gansner, E. Koutsofios, S. North, and G. Woodhull, “Graphviz and dynagraph – static and dynamic graph drawing tools,” in Graph Drawing Software, M. Junger and P. Mutzel, Eds. Springer-Verlag, 2003, pp. 127–148.
[34] M. Bastian, S. Heymann, and M. Jacomy, “Gephi: An open source software for exploring and manipulating networks,” 2009.