Abstract
Mobile opportunistic networking is a promising technology that can supplement existing cellular and WiFi networks to provide desirable services for smart and connected communities. Message routing is the most compelling challenge in mobile opportunistic networks due to the lack of contemporaneous end-to-end paths and the resource constraints at mobile devices. To improve the probability of successful message delivery, most existing routing schemes use the past contact history to predict future contacts for message forwarding, and exploit message replication and redundancy for multicopy routing. However, most existing prediction-based routing schemes simply use the average pairwise contact probability as the routing metric and neglect the benefits of exploring fine-grained contact information such as pairwise repeated contact patterns to improve the accuracy of predicting future contacts. Moreover, there is no efficient mechanism that can adaptively control message replication in a decentralized manner to achieve both high probability of successful message delivery and low message overhead. To address these problems, we present FGAR, a routing protocol designed for mobile opportunistic networks by leveraging fine-grained contact characterization and adaptive message replication. In FGAR, contact history is characterized in a fine-grained manner with timing information using a sliding window mechanism, and future contacts are predicted based on the fine-grained contact information, thereby improving the accuracy of contact prediction. We further design an efficient message replication scheme in which message replication is controlled in a fully decentralized manner by taking into account the expected message delivery probability, the replication history, and the quality of the encountered device. A replica is generated only when it is necessary to fulfill the expected message delivery probability. We evaluate our scheme through trace-driven simulations, and the simulation results show that FGAR outperforms existing schemes. In comparison with PRoPHET, FGAR can achieve more than 20% improvement on average on successful message delivery, whereas the message overhead has been reduced by a factor up to 15.
- Aruna Balasubramanian, Brian Levine, and Arun Venkataramani. 2007. DTN routing as a resource allocation problem. ACM SIGCOMM Computer Communication Review 37, 4, 373--384. Google Scholar
Digital Library
- Chiara Boldrini, Marco Conti, Jacopo Jacopini, and Andrea Passarella. 2007. HiBOp: A history based routing protocol for opportunistic networks. In Proceedings of the IEEE International Symposium on a World of Wireless, Mobile, and Multimedia Networks (WoWMoM’07). 1--12. Google Scholar
Cross Ref
- John Burgess, Brian Gallagher, David Jensen, and Brian Neil Levine. 2006. MaxProp: Routing for vehicle-based disruption tolerant networks. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM’06). 1--11.Google Scholar
Cross Ref
- John Burgess, John Zahorjan, Ratul Mahajan, Brian Neil Levine, Aruna Balasubramanian, Arun Venkataramani, Yun Zhou, Bruce Croft, Nilanjan Banerjee, Mark Corner, and Don Towsley. 2008. The Umass/Diesel Dataset (v. 2008-09-14). Retrieved September 19, 2017, from http://crawdad.org/umass/diesel/20080914Google Scholar
- Kang Chen and Haiying Shen. 2014. SMART: Utilizing distributed social map for lightweight routing in delay-tolerant networks. IEEE/ACM Transactions on Networking 22, 5, 1545--1558. Google Scholar
Digital Library
- X. Chen, J. Shen, T. Groves, and J. Wu. 2009. Probability delegation forwarding in delay tolerant networks. In Proceedings of the IEEE International Conference on Computer Communications and Networks (ICCCN’09). 1--6. Google Scholar
Digital Library
- Cisco. 2010. Smart+Connected Communities: Changing a City, a Country, the World. Retrieved September 19, 2017, from http://www.cisco.com/c/dam/en_us/solutions/industries/docs/scc/09CS2326_SCC_BrochureForWest_r3_112409.pdf.Google Scholar
- Cisco. 2017. Smart+Connected Communities. Retrieved September 19, 2017, from http://www.cisco.com/c/en/us/solutions/industries/smart-connected-communities.html.Google Scholar
- Nathan Eagle and Alex (Sandy) Pentland. 2005. The MIT/Reality Dataset (v. 2005-07-01). Retrieved September 19, 2017, from http://crawdad.org/mit/reality/.Google Scholar
- W. Gao, G. Cao, T. La Porta, and J. Han. 2013. On exploiting transient social contact patterns for data forwarding in delay-tolerant networks. IEEE Transactions on Mobile Computing 12, 1, 151--165. Google Scholar
Digital Library
- Marta C. Gonzalez, Cesar A. Hidalgo, and Albert-Laszlo Barabasi. 2008. Understanding individual human mobility patterns. Nature 453, 779--782. Google Scholar
Cross Ref
- B. Han, P. Hui, V. S. A. Kumar, M. V. Marathe, J. Shao, and A. Srinivasan. 2012. Mobile data offloading through opportunistic communications and social participation. IEEE Transactions on Mobile Computing 11, 5, 821--834. Google Scholar
Digital Library
- ITU. 2016. Measuring the Information Society Report. Retrieved September 19, 2017, from http://www.itu.int/en/ITU-D/Statistics/Documents/publications/misr2016/MISR2016-w4.pdf.Google Scholar
- Sushant Jain, Kevin Fall, and Rabin Patra. 2004. Routing in a delay tolerant network. In Proceedings of the 2004 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications (SIGCOMM’04). 145--158. Google Scholar
Digital Library
- Philo Juang, Hidekazu Oki, Yong Wang, Margaret Martonosi, Li Shiuan Peh, and Daniel Rubenstein. 2002. Energy-efficient computing for wildlife tracking: Design tradeoffs and early experiences with ZebraNet. ACM SIGARCH Computer Architecture News 30, 5, 96--107. Google Scholar
Digital Library
- Amir Krifa, Chadi Barakat, and Thrasyvoulos Spyropoulos. 2008. An optimal joint scheduling and drop policy for delay tolerant networks. In Proceedings of the IEEE International Symposium on a World of Wireless, Mobile, and Multimedia Networks (WoWMoM’08). 1--6. Google Scholar
Digital Library
- Silvia Krug, Peggy Begerow, Atheer Al Rubaye, Sebastian Schellenberg, and Jochen Seitz. 2014. A realistic underlay concept for delay tolerant networks in disaster scenarios. In Proceedings of the International Conference on Mobile Ad-Hoc and Sensor Networks (MSN’14). 163--170. Google Scholar
Digital Library
- Kyunghan Lee, Joohyun Lee, Yung Yi, Injong Rhee, and Song Chong. 2010. Mobile data offloading: How much can WiFi deliver? In Proceedings of the 6th International Conference on Emerging Networking Experiments and Technologies (CoNEXT’10). 26:1--26:12.Google Scholar
- Zhong Li, Cheng Wang, Siqian Yang, Changjun Jiang, and Ivan Stojmenovic. 2014. Improving data forwarding in mobile social networks with infrastructure support: A space-crossing community approach. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM’14). 1941--1949. Google Scholar
Cross Ref
- A. Lindgren, A. Doria, E. Davies, and S. Grasic. 2012. Probabilistic Routing Protocol for Intermittently Connected Networks. Retrieved September 19, 2017, from https://tools.ietf.org/html/draft-irtf-dtnrg-prophet-10.Google Scholar
- Anders Lindgren, Avri Doria, and Olov Schelén. 2003. Probabilistic routing in intermittently connected networks. ACM SIGMOBILE Mobile Computing and Communications Review 7, 3, 19--20.Google Scholar
Digital Library
- Cong Liu and Jie Wu. 2012. On multicopy opportunistic forwarding protocols in nondeterministic delay tolerant networks. IEEE Transactions on Parallel and Distributed Systems 23, 6, 1121--1128. Google Scholar
Digital Library
- S. Moon and A. Helmy. 2010. Understanding periodicity and regularity of nodal encounters in mobile networks: A spectral analysis. In Proceedings of the IEEE Global Communications Conference (GLOBECOM’10). 1--5. Google Scholar
Cross Ref
- Mirco Musolesi and Cecilia Mascolo. 2009. CAR: Context-aware adaptive routing for delay-tolerant mobile networks. IEEE Transactions on Mobile Computing 8, 2, 246--260. Google Scholar
Digital Library
- Hoang Anh Nguyen and Silvia Giordano. 2012. Context information prediction for social-based routing in opportunistic networks. Ad Hoc Networks 10, 8, 1557--1569. Google Scholar
Digital Library
- Alex (Sandy) Pentland, Richard Fletcher, and Amir Hasson. 2004. DakNet: Rethinking connectivity in developing nations. Computer 37, 1, 78--83.Google Scholar
Digital Library
- James Scott, Richard Gass, Jon Crowcroft, Pan Hui, Christophe Diot, and Augustin Chaintreau. 2006. The Cambridge/Haggle Dataset (v. 2006-01-31). Retrieved September 19, 2017, from http://crawdad.org/cambridge/haggle/20060131/.Google Scholar
- Thrasyvoulos Spyropoulos, Konstantinos Psounis, and Cauligi S. Raghavendra. 2005. Spray and Wait: An efficient routing scheme for intermittently connected mobile networks. In Proceedings of the ACM SIGCOMM Workshop on Delay Tolerant Networks (WDTN’05). 252--259.Google Scholar
- Amin Vahdat and David Becker. 2000. Epidemic Routing for Partially-Connected Ad Hoc Networks. Technical Report. Department of Computer Science, Duke University.Google Scholar
- L. Vu, Q. Do, and K. Nahrstedt. 2011. 3R: Fine grained encounter-based routing in delay tolerant networks. In Proceedings of the IEEE International Symposium on a World of Wireless, Mobile, and Multimedia Networks (WoWMoM’11). 1--6.Google Scholar
- Eiko Yoneki, Pan Hui, and Jon Crowcroft. 2007. Visualizing community detection in opportunistic networks. In Proceedings of the 2nd ACM Workshop on Challenged Networks (CHANTS’07). 93--96. Google Scholar
Digital Library
- Q. Yuan, I. Cardei, and J. Wu. 2012. An efficient prediction-based routing in disruption-tolerant networks. IEEE Transactions on Parallel and Distributed Systems 23, 1, 19--31. Google Scholar
Digital Library
- Xiaolan Zhang, Jim Kurose, Brian Neil Levine, Don Towsley, and Honggang Zhang. 2007. Study of a bus-based disruption tolerant network: Mobility modeling and impact on routing. In Proceedings of the Annual International Conference on Mobile Computing and Networking (MobiCom’07). 195--206.Google Scholar
Digital Library
- Yang Zhang and Jing Zhao. 2009. Social network analysis on data diffusion in delay tolerant networks. In Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc’09). 345--346. Google Scholar
Digital Library
- Huan Zhou, Huanyang Zheng, Jie Wu, and Jiming Chen. 2013. Energy-efficient contact probing in opportunistic mobile networks. In Proceedings of the IEEE International Conference on Computer Communications and Networks (ICCCN’13). 4629--4642.Google Scholar
Index Terms
Adaptive Message Routing and Replication in Mobile Opportunistic Networks for Connected Communities
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