Autonomic Computing and Networking pp 411-433 | Cite as
Autonomic Information Diffusion in Intermittently Connected Networks
Abstract
In this work, we introduce a framework for designing autonomic information diffusion mechanisms in intermittently connected wireless networks. Our approach is based on the use of techniques and tools drawn from evolutionary computing research, which enable to embed evolutionary features in epidemic-style forwarding mechanisms. In this way, it is possible to build a system in which information dissemination strategies change at runtime to adapt to the current network conditions in a distributed autonomic fashion. A case study is then introduced, for which design and implementation choices are presented and discussed. Simulation results are reported to validate the ability of the proposed protocol to converge to the optimal operating point (or close to it) in unknown and changing environments.
Keywords
Mobile Node Dynamic Source Route Message Delivery Optimize Link State Route Data MessagePreview
Unable to display preview. Download preview PDF.
References
- 1.Alouf S, Carreras I, Miorandi D, Neglia G (2007) Embedding evolution in epidemic-style forwarding. In: Proc. of IEEE International Conference on Mobile Adhoc and Sensor Systems (MASS 2007), Pisa, ItalyGoogle Scholar
- 2.Babaoglu O, Canright G, Deutsch A, Caro GAD, Ducatelle F, Gambardella LM, Ganguly N, Jelasity M, Montemanni R, Montresor A, Urnes T (2006) Design patterns from biology for distributed computing. ACM Trans Auton Adapt Syst 1(1):26–66CrossRefGoogle Scholar
- 3.Fall K (2003) A delay-tolerant network architecture for challenged Internets. In: Proc. of ACM SIGCOMM 2003, ACM, New York, USA, pp 27–34Google Scholar
- 4.Groenevelt R, Nain P, Koole G (2005) The message delay in mobile ad hoc networks. Performance Evaluation 62(1–4):210–228Google Scholar
- 5.Grossglauser M, Tse D (2002) Mobility increases the capacity of ad hoc wireless networks. IEEE/ACM Trans on Networking 10(4):477–486CrossRefGoogle Scholar
- 6.Haas ZJ, Small T (2006) A new networking model for biological applications of ad hoc sensor networks. IEEE/ACM Trans on Networking 14(1):27–40CrossRefGoogle Scholar
- 7.Lindgren A, Doria A, Schelen O (2004) Probabilistic routing in intermittently connected networks. In: Proc of SAPIR Workshop 2004, LNCS, vol 3126, pp 239–254Google Scholar
- 8.Neglia G, Zhang X (2006) Optimal delay-power tradeoff in sparse delay tolerant networks: a preliminary study. In: Proc of ACM SIGCOMM CHANTS 2006, pp 237–244Google Scholar
- 9.OMNeT (2007) OMNeT++ Discrete Event Simulation System. http://www.omnetpp.org
- 10.Pelusi L, Passarella A, Conti M (2006) Opportunistic networking: data forwarding in disconnected mobile ad hoc networks. IEEE Comm Mag 44(11):134–141CrossRefGoogle Scholar
- 11.Royer EM, Melliar-Smith PM, Moser LE (2001) An analysis of the optimum node density for ad hoc mobile networks. In: Proc. of IEEE ICC 2001, vol 3, pp 857–861Google Scholar
- 12.Spyropoulos T, Psounis K, Raghavendra CS (2008) Efficient routing in intermittently connected mobile networks: The multiple-copy case. IEEE/ACM Trans on Networking 16(1): 77–90CrossRefGoogle Scholar
- 13.Vahdat A, Becker D (2000) Epidemic routing for partially connected ad hoc networks. Tech Rep CS-200006, Duke UniversityGoogle Scholar
- 14.Zhang X, Neglia G, Kurose J, Towsley D (2007) Performance modeling of epidemic routing. Computer Networks 51(10):2867–2891MATHCrossRefGoogle Scholar