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Issue No.01 - January (2012 vol.23)
pp: 2-10
Giovanni Resta , Istituto di Informatica e Telematica del CNR, Pisa
Paolo Santi , Istituto di Informatica e Telematica del CNR, Pisa
In this paper, we present a framework for analyzing routing performance in delay tolerant networks (DTNs). Differently from previous work, our framework is aimed at characterizing the exact distribution of relevant performance metrics, which is a substantial improvement over existing studies characterizing either the expected value of the metric, or an asymptotic approximation of the actual distribution. In particular, the considered performance metrics are packet delivery delay, and communication cost, expressed as number of copies of a packet circulating in the network at the time of delivery. Our proposed framework is based on a characterization of the routing process as a stochastic coloring process and can be applied to model performance of most stateless delay tolerant routing protocols, such as epidemic, two-hops, and spray and wait. After introducing the framework, we present examples of its application to derive the packet delivery delay and communication cost distribution of two such protocols, namely epidemic and two-hops routing. Characterizing packet delivery delay and communication cost distribution is important to investigate fundamental properties of delay tolerant networks. As an example, we show how packet delivery delay distribution can be used to estimate how epidemic routing performance changes in presence of different degrees of node cooperation within the network. More specifically, we consider fully cooperative, noncooperative, and probabilistic cooperative scenarios, and derive nearly exact expressions of the packet delivery rate (PDR) under these scenarios based on our proposed framework. The comparison of the obtained packet delivery rate estimation in the various cooperation scenarios suggests that even a modest level of node cooperation (probabilistic cooperation with a low probability of cooperation) is sufficient to achieve 2-fold performance improvement with respect to the most pessimistic scenario in which all potential forwarders drop packets.
Delay-tolerant networks, noncooperative networks, delay-tolerant routing, packet delivery delay distribution, communication cost distribution.
Giovanni Resta, Paolo Santi, "A Framework for Routing Performance Analysis in Delay Tolerant Networks with Application to Noncooperative Networks", IEEE Transactions on Parallel & Distributed Systems, vol.23, no. 1, pp. 2-10, January 2012, doi:10.1109/TPDS.2011.99
[1] D. Aldous and J. Fill, “Reversible Markov Chains and Random Walks on Graphs,” Monograph in Preparation, http://www.stat. /, 2010.
[2] A. Al Hanbali, A.A. Kherani, and P. Nain, “Simple Models for the Performance Evaluation of a Class of Two-Hop Relay Protocols,” Proc. Sixth Int'l IFIP-TC6 Conf. Ad Hoc and Sensor Networks, Wireless Networks (IFIP '07), pp. 191-202, 2007.
[3] A. Al Hanbali, P. Nain, and E. Altman, “Performance of Ad Hoc Networks with Two-Hop Relay Routing and Limited Packet Lifetime,” Performance Evaluations, vol. 65, nos. 1-2, pp. 463-483, 2008.
[4] M. Balazs, “Sum of Independent Exponential Random Variables with Different Parameters,” , 2010.
[5] C. Bettstetter, G. Resta, and P. Santi, “The Node Distribution of the Random Waypoint Mobility Model for Wireless Ad Hoc Networks,” IEEE Trans. Mobile Computing, vol. 2, no. 3, pp. 257-269, July-Sept. 2003.
[6] L. Buttyan, L. Dora, M. Felegyhazi, and I. Vajda, “Barter-Based Cooperation in Delay-Tolerant Personal Wireless Networks,” Proc. IEEE Int'l Symp. World of Wireless Mobile and Multimedia Networks (WoWMoM '07), pp. 1-6, 2007.
[7] H.A. David, H.N. Nagaraja, Order Statistics. John Wiley and Sons, 2003.
[8] M. Grossglauser and D.N.C. Tse, “Mobility Increases the Capacity of Ad-Hoc Wireless Networks,” Proc. IEEE INFOCOM, pp. 1360-1369, 2001.
[9] R. Groenevelt, P. Nain, and G. Koole, “The Message Delay in Mobile Ad Hoc Networks,” Performance Evaluation, vol. 62, nos. 1-4, pp. 210-228, 2005.
[10] S. Jain, K. Fall, and R. Patra, “Routing in Delay Tolerant Networking,” Proc. ACM SIGCOMM, pp. 145-158, 2004.
[11] D.B. Johnson and D.A. Maltz, “Dynamic Source Routing in Ad Hoc Wireless Networks,” Mobile Computing. Kluwer Academic Publishers, pp. 153-181, 1996.
[12] T. Karagiannis, J.-Y. Le Boudec, and M. Vojnovic, “Power Law and Exponential Decay of Inter Contact Times Between Mobile Devices,” Proc. ACM MOBICOM, pp. 183-194, 2007.
[13] A. Panagakis, A. Vaios, and I. Stavrakakis, “On the Effects of Cooperation in DTNs,” Proc. IEEE Second Int'l Conf. Comm. Systems Software and Middleware (COMSWARE '07), pp. 1-6, 2007.
[14] G. Resta and P. Santi, “The Effects of Node Cooperation Level on Routing Performance in Delay Tolerant Networks,” Proc. Sixth Ann. IEEE Comm. Soc. Sensor, Mesh and Ad Hoc Comm. and Networks (Secon '09), pp. 413-421, 2009.
[15] T. Spyropoulos, K. Psounis, and C.S. Raghavendra, “Efficient Routing in Intermittently Connected Mobile Networks: The Multi-Copy Case,” IEEE Trans. Networking, vol. 16, no. 1, pp. 77-90, Feb. 2008.
[16] T. Spyropoulos, K. Psounis, and C.S. Raghavendra, “Efficient Routing in Intermittently Connected Mobile Networks: The Single-Copy Case,” IEEE Trans. Networking, vol. 16, no. 1, pp. 63-76, Feb. 2008.
[17] T. Spyropoulos, K. Psounis, and C.S. Raghavendra, “Performance Analysis of Mobility-Assisted Routing,” Proc. ACM MobiHoc, pp. 49-60, 2006.
[18] A. Vahdat and D. Becker, “Epidemic Routing for Partially Connected Ad Hoc Networks,” Technical Report CS-200006, Duke Univ., Apr. 2000.
[19] X. Zhang, G. Neglia, J. Kurose, and D. Towsley, “Performance Modeling of Epidemic Routing,” Computer Networks, vol. 51, pp. 2867-2891, 2007.
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