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Improving performance in delay/disruption tolerant networks through passive relay points

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Abstract

In this paper, we study the case of a limited number of mobile nodes trying to communicate in a large geographic area, forming a delay/disruption tolerant network (DTN). In such networks the mobile nodes are disconnected for significantly long periods of time. Traditional routing protocols proposed for mobile ad hoc networks or mesh networks, which assume at least one path between each source and destination, are ineffective in DTNs. One approach to improve communication is through gossip based protocols because these protocols do not rely on a fixed path. Another approach is to control the movement of the mobile nodes and/or use special mobile nodes called ferry nodes. Others try to employ a fixed infrastructure including stationary relay points. One scheme in stationary relay point approach is to use base stations as relay points which need their own power supply. In this paper, we study a passive approach where mobile nodes deposit/retrieve messages to/ from known stationary locations in the geographic region. Messages are delivered from a source by being deposited at one or more locations that are later visited by the destination. A proposed implementation of our approach using read/writable passive Radio Frequency Identification (RFID) tags, one per point location, is considered in this work. Passive RFID technology is desirable because it operates wirelessly and without the need for attached power. Our simulation results indicate that our approach can achieve competitive message delay and delivery rates. We also demonstrate several techniques for optimizing the stationary relay node placement, namely relay pruning, probability based relay distribution and a genetic algorithm; the genetic algorithm is shown to provide the best solutions to this problem.

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Notes

  1. Disruption Tolerant Networking. [Online]. Available: http://www.darpa.mil/ato/solicit/DTN/.

  2. http://www.mojix.com/products.

  3. http://www.fujitsu.com/global/news/pr/archives/month/2008/20080109-01.html.

  4. http://www.rfid.net/product-listing/reviews/176-csl-cs101-handheld-reader.

References

  1. Akyildiz, I. F., Pompili, D., & Melodia, T. (2005). Underwater acoustic sensor networks: Research challenges. Ad Hoc Networks 3(3), 257–279.

    Article  Google Scholar 

  2. Al Hanbali, A., Ibrahim, M., Simon, V., Varga, E., & Carreras, I. (2008). A survey of message diffusion protocols in mobile ad hoc networks. In Proceedings of the 3rd international conference on performance evaluation methodologies and tools (ICST, Brussels, Belgium, Belgium), ValueTools ’08 (pp. 82:1–82:16). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering).

  3. Baghaei-Nejad, M., Mendoza, D. S., Zou, Z., Radiom, S., Gielen, G., Zheng, L. -R., & Tenhunen, H. (2009). A remote-powered rfid tag with 10mb/s uwb uplink and −18.5 dbm sensitivity uhf downlink in 0.18 μm cmos. In Solid–state circuits conference— digest of technical papers, 2009. ISSCC 2009. IEEE International (pp. 198–199,199a).

  4. Balasubramanian, A., Levine, B., & Venkataramani, A. (2007). Dtn routing as a resource allocation problem. SIGCOMM Computer Communication Review, 37(4), 373–384.

    Article  Google Scholar 

  5. Banerjee, N., Corner, M. D., & Levine, B. N. (2007). An energy-efficient architecture for dtn throwboxes. INFOCOM 2007. 26th IEEE international conference on computer communications. IEEE (pp. 776–784).

  6. Banerjee, N., Corner, M. D., Towsley, D., & Levine, B. N. (2008). Relays, base stations, and meshes: Enhancing mobile networks with infrastructure. In MobiCom ’08: Proceedings of the 14th ACM international conference on mobile computing and networking (pp. 81–91). New York, NY, USA: ACM.

  7. Bettstetter, C. (2001). Smooth is better than sharp: A random mobility model for simulation of wireless networks. In MSWIM ’01: Proceedings of the 4th ACM international workshop on modeling, analysis and simulation of wireless and mobile systems (pp. 19–27). NY, USA: ACM.

  8. Bettstetter, C., Hartenstein, H., & Perez-Costa, X. (2002). Stochastic properties of the random waypoint mobility model: epoch length, direction distribution, and cell change rate. In MSWiM ’02: Proceedings of the 5th ACM international workshop on modeling analysis and simulation of wireless and mobile systems (pp. 7–14). NY, USA: ACM.

  9. Tariq, M. M. B., Ammar, M., & Zegura, E. (2006). Message ferry route design for sparse ad hoc networks with mobile nodes. In MobiHoc ’06: Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing (pp. 37–48). New York, NY, USA: ACM.

  10. Burgess, J., Gallagher, B., Jensen, D., & Levine, B. N. (2006). Maxprop: Routing for vehicle-based disruption-tolerant networks. INFOCOM 2006. 25th IEEE international conference on computer communications. Proceedings (pp. 1–11).

  11. Camp J. B., & Davies, V. (2002). A survey of mobility models for ad hoc network research. Wireless Communication and Mobile Computing (WCMC), 2(5), 483–502.

    Article  Google Scholar 

  12. Chen, W., Guha, R. K., Kwon, T. J., Lee, J., & Yuan-Ying, H. (2009). A survey and challenges in routing and data dissemination in vehicular ad hoc networks. Wireless Communications and Mobile Computing.

  13. Daly, E. M., & Haahr, M. (2007). Social network analysis for routing in disconnected delay-tolerant manets. In Proceedings of the 8th ACM international symposium on mobile ad hoc networking and computing, MobiHoc ’07 (pp. 32–40). New York, NY, USA: ACM.

  14. de Oliveira, E. C. R., & de Albuquerque, C. V. N. (2009). Nectar: A dtn routing protocol based on neighborhood contact history. Proceedings of the 2009 ACM symposium on applied computing, SAC ’09 (pp. 40–46). New York, NY, USA: ACM.

  15. Fall, K. (2003). A delay-tolerant network architecture for challenged internets. In SIGCOMM ’03: Proceedings of the 2003 conference on applications, technologies, architectures, and protocols for computer communications (pp. 27–34). New York, NY, USA: ACM.

  16. Farahmand, F., Cerutti, R. I., Patel, A. N., Jue, J. P., & Rodrigues, J. J. P. C. (2009). Performance of vehicular delay-tolerant networks with relay nodes. Wireless Communications and Mobile Computing.

  17. Gao, W., Li, Q., Zhao, B., & Cao, G. (2009). Multicasting in delay tolerant networks: A social network perspective. In Proceedings of the tenth ACM international symposium on Mobile ad hoc networking and computing, MobiHoc ’09 (pp. 299–308). New York, NY, USA: ACM.

  18. Goldenberg, D. K., Lin, J., Morse, A. S., Rosen, B. E., & Richard Y. Y. (2004). Towards mobility as a network control primitive. In MobiHoc ’04: Proceedings of the 5th ACM international symposium on Mobile ad hoc networking and computing (pp. 163–174). NY, USA: ACM.

  19. Han, J. K., Park, B. S., Choi, Y. S., & Park, H. K. (2001). Genetic approach with a new representation for base station placement in mobile communications. In Vehicular technology conference, 2001. VTC 2001 Fall. IEEE VTS 54th (Vol. 4, pp. 2703 –2707).

  20. Hong, X., Gerla, M., Pei, G., & Chiang, C.-C. (1999). A group mobility model for ad hoc wireless networks. In MSWiM ’99: Proceedings of the 2nd ACM international workshop on modeling, analysis and simulation of wireless and mobile systems (pp. 53–60). NY, USA: ACM.

  21. Hui, P., Crowcroft, J., & Yoneki, E. (2008). Bubble rap: Social-based forwarding in delay tolerant networks. In Proceedings of the 9th ACM international symposium on mobile ad hoc networking and computing, MobiHoc ’08 (pp. 241–250). New York, NY, USA: ACM.

  22. Ibrahim, M., Al Hanbali, A., & Nain, P. (2007). Delay and resource analysis in manets in presence of throwboxes. Performance Evalution, 64(9–12), 933–947.

    Article  Google Scholar 

  23. Jain, S., Fall, K., & Patra, R. (2004). Routing in a delay tolerant network. In SIGCOMM ’04: Proceedings of the 2004 conference on applications, technologies, architectures, and protocols for computer communications (pp. 145–158). New York, NY, USA: ACM.

  24. Johnson, D. B., & Maltz, D. A. (1996). Dynamic source routing in ad hoc wireless networks. In: T. Imielinski & Korth, H. (Eds.), Mobile computing (pp. 153–181). Dordrecht: Kluwer.

  25. Juang, P., Oki, H., Wang, Y., Martonosi, M., Peh, L. S., & Rubenstein, D. (2002). Energy-efficient computing for wildlife tracking: Design tradeoffs and early experiences with zebranet. SIGPLAN, 37, 96–107.

    Article  Google Scholar 

  26. Dheeraj, K., Kwan-Wu, C., & Raad, R. (2009). On the energy consumption of pure and slotted aloha based rfid anti-collision protocols. Computer Communications, 32, 961–973.

    Article  Google Scholar 

  27. Lahde, S., Doering, M., Pttner, W.-B., Lammert, G., & Wolf, L. (2007). A practical analysis of communication characteristics for mobile and distributed pollution measurements on the road. Wireless Communications and Mobile Computing, 7(10), 1209–1218.

    Article  Google Scholar 

  28. LeBrun, J., Chuah, C.-N., Ghosal, D., & Zhang, M. (2005). Knowledge-based opportunistic forwarding in vehicular wireless ad hoc networks. In Vehicular technology conference, 2005. VTC 2005-Spring. 2005 IEEE 61st (Vol. 4, pp. 2289–2293).

  29. Leguay, J., Friedman, T., & Conan, V. (2006). Evaluating mobility pattern space routing for dtns, INFOCOM 2006. In 25th IEEE international conference on computer communications. Proceedings (pp. 1 –10).

  30. Li, Q., & Rus, D. (2000). Sending messages to mobile users in disconnected ad-hoc wireless networks. In Proceedings of the 6th annual international conference on mobile computing and networking (pp. 44–55). New York, NY, USA: MobiCom ’00, ACM.

  31. Lin, X., & Chen, H.-H. (2010). A secure and efficient rsu-aided bundle forwarding protocol for vehicular delay tolerant networks. In Wireless communications and mobile computing.

  32. Lindgren, A., Doria, A., & Schelén, O. (2004). Probabilistic routing in intermittently connected networks. Lecture Notes in Computer Science 3126, 239–254.

    Article  Google Scholar 

  33. Ma, Y., & Jamalipour, A. (2009). Optimized message delivery framework using fuzzy logic for intermittently connected mobile ad hoc networks. Wireless Communications and Mobile Computing, 9(4), 501–512.

    Article  Google Scholar 

  34. Maral, G., Bousquet, M., & Sun, Z. (2009). Satellite communications systems: Systems, techniques and technology, 5th edn., communication and distributed systems. New Jersey: Wiley.

    Google Scholar 

  35. Meunier, H., Talbi, E.-G. & Reininger P. (2000). A multiobjective genetic algorithm for radio network optimization, evolutionary computation, 2000. In Proceedings of the 2000 congress on (Vol. 1, pp. 317 –324).

  36. Melanie, M. (1998). An introduction to genetic algorithms. Cambridge, MA, USA: MIT Press.

    MATH  Google Scholar 

  37. Musolesi, M., Hailes, S., & Mascolo, C. (2002). Adaptive routing for intermittently connected mobile ad hoc networks. In World of wireless mobile and multimedia networks, 2005. WoWMoM 2005. Sixth IEEE International Symposium on a, June 2005 (pp. 183 – 189).

  38. Ott J., & Kutscher D.(2005). A disconnection-tolerant transport for drive-thru internet environments, INFOCOM 2005. 24th annual joint conference of the ieee computer and communications societies. Proceedings IEEE (Vol. 3, pp. 1849–1862).

    Google Scholar 

  39. Pabst, R., Walke, B. H., Schultz, D. C., Herhold, P., Yanikomeroglu, H., Mukherjee, S., Viswanathan, H., Lott, M., Zirwas, W.,Dohler, M., Aghvami, H., Falconer, D. D. & Fettweis, G. P. (2004). Relay-based deployment concepts for wireless and mobile broadband radio. Communications magazine, IEEE (Vol. 42, pp. 80–89.

  40. Partan, J., Kurose, J., & Levine B. N. (2007). A survey of practical issues in underwater networks. SIGMOBILE Mobile Computer Communication Review, 11, 23–33.

    Article  Google Scholar 

  41. Pei, G., Gerla, M., & Hong, X. (2000). Lanmar: Landmark routing for large scale wireless ad hoc networks with group mobility. In Proceedings of the 1st ACM international symposium on mobile ad hoc networking & computing (pp. 11–18), Piscataway, NJ, USA: MobiHoc ’00, IEEE Press.

  42. Perkins, C. E., & Royer, E. M. (1999). Ad-hoc on-demand distance vector routing. In Mobile computing systems and applications 1999. Proceedings. WMCSA ’99. Second IEEE Workshop on (pp. 90 –100).

  43. Perkins, C. E., & Bhagwat, P. (1994). Highly dynamic destination-sequenced distance-vector routing (dsdv) for mobile computers. SIGCOMM Computer Communication Review, 24(4), 234–244.

    Article  Google Scholar 

  44. Perur, S., & Iyer, S. (2006). Characterization of a connectivity measure for sparse wireless multi-hop networks. Distributed computing systems workshops. International conference on (p. 80).

  45. Ristic, B., Arulampalam, S., & Gordon, N. (2004). Beyond the Kalman filter: Particle filters for tracking applications. Boston: Artech House.

    MATH  Google Scholar 

  46. Santi, P., & Blough, D. M. (2003). The critical transmitting range for connectivity in sparse wireless ad hoc networks. Mobile Computing, IEEE Transactions on, 2(1), 25–39.

    Article  Google Scholar 

  47. Seth, A., Kroeker, D., Zaharia, M., Guo, S., & Keshav, S. (2006). Low-cost communication for rural internet kiosks using mechanical backhaul. In MobiCom ’06: Proceedings of the 12th annual international conference on Mobile computing and networking (pp. 334–345). NY, USA: ACM.

  48. Shahbazi, S., Ghassem-Sani, G., Rabiee, H., Ghanbari, M., & Dehghan, M. (2006). Adian: A distributed intelligent ad-hoc network. In Distributed computing and networking, lecture notes in computer science (Vol. 4308, pp. 27–39). Berlin, Heidelberg: Springer.

  49. Shahbazi, S., Harwood, A., & Karunasekera, S. (2008). Achieving ubiquitous network connectivity using an rfid tag-based routing protocol. In ICPADS ’08: Proceedings of the 2008 14th IEEE international conference on parallel and distributed systems (pp. 391–398).

  50. Shahbazi, S., Harwood, A., & Karunasekera, S. (2009). An analytical model for performance evaluation in sparse mobile ad hoc networks. In WD’09: Proceedings of the 2nd IFIP conference on wireless days (pp. 236–241). Piscataway, NJ, USA: WD’09, IEEE Press.

  51. Shahbazi, S., Harwood, A., & Karunasekera, S. (2011). On placement of passive stationary relay points in delay tolerant networking. In AINA 2011: Advanced information networking and applications, international conference on (pp. 764–771). Los Alamitos, CA, USA: IEEE Computer Society.

  52. Spyropoulos, T., Psounis, K., & Raghavendra, C. S. (2005). Spray and wait: an efficient routing scheme for intermittently connected mobile networks. In WDTN ’05: Proceedings of the 2005 ACM SIGCOMM workshop on delay-tolerant networking (pp. 252–259). NY, USA: ACM.

  53. Spyropoulos, T., Psounis, K., & Raghavendra, C. S. (2007). Spray and focus: Efficient mobility-assisted routing for heterogeneous and correlated mobility. In Pervasive computing and communications workshops, 2007. PerCom workshops ’07. Fifth annual IEEE international conference on (pp. 79–85).

  54. Ting, C.-K., Lee, C.-N., Chang, H.-C., & Wu, J.-S. (2009). Wireless heterogeneous transmitter placement using multiobjective variable-length genetic algorithm. Systems, man, and cybernetics, part B: Cybernetics. IEEE Transactions on, 39, 945 –958.

    Google Scholar 

  55. Tournoux, P.-U., Leguay, J., Benbadis, F., Conan, V., de Amorim, M.D., & Whitbeck J. (2009). The accordion phenomenon: Analysis, characterization, and impact on dtn routing. INFOCOM, IEEE (pp. 1116 –1124).

  56. Vahdat, A., & Becker, D. (2000). Epidemic routing for partially-connected ad hoc networks. Tech. report, Duke University CS-2000-06.

  57. Wang Y., Dang H., & Hongyi, W. (2007). A survey on analytic studies of delay-tolerant mobile sensor networks. Wireless Communications and Mobile Computing, 7(10), 1197–1208.

    Article  Google Scholar 

  58. Whitehouse, K., Woo, A., Jiang, F., Polastre, J., & Culler, D. (2005). Exploiting the capture effect for collision detection and recovery, Embedded Networked Sensors, 2005. In EmNetS-II. The second IEEE workshop on (pp. 45–52).

  59. Wu, J., Yang, S., & Dai, F. (2007). Logarithmic store-carry-forward routing in mobile ad hoc networks. Parallel and Distributed Systems, IEEE Transactions on, 18(6), 735–748.

    Article  Google Scholar 

  60. Yang, J., Chen, Y., Ammar, M., & Lee, C. (2005). Ferry replacement protocols in sparse manet message ferrying systems. In Wireless communications and networking conference, 2005 IEEE (Vol. 4, pp. 2038–2044).

  61. Yuan, Q., Cardei, I., & Wu, J. (2009). Predict and relay: an efficient routing in disruption-tolerant networks.In E. W. Knightly, C.-F. Chiasserini, & X. Lin, (Eds.), MobiHoc (pp. 95–104). ACM.

  62. Zhang, Y. P., & Hwang, Y. (1998). Characterization of uhf radio propagation channels in tunnel environments for microcellular and personal communications. Vehicular Technology, IEEE Transactions on, 47, 283–296.

    Article  Google Scholar 

  63. Zhang Z., & Qian Z. (2007). Delay/disruption tolerant mobile ad hoc networks: Latest developments. Wireless Communications and Mobile Computing, 7(10), 1219–1232.

    Article  Google Scholar 

  64. Zhang, Z., Lu, Z., Pang, Z., Yan, X., Chen, Q., & Zheng, L.-R. (2010). A low delay multiple reader passive rfid system using orthogonal th-ppm ir-uwb. In Computer communications and networks (ICCCN), 2010 proceedings of 19th international conference on (pp. 1–6).

  65. Zhao, W., Ammar, M., & Zegura, E. (2004). A message ferrying approach for data delivery in sparse mobile ad hoc networks. In Proceedings of the 5th ACM international symposium on Mobile ad hoc networking and computing (pp. 187–198). New York, NY, USA: MobiHoc ’04, ACM.

  66. Zhao, W., Chen, Y., Ammar, M., Corner, M., Levine, B., & Zegura, E. (2006). Capacity enhancement using throwboxes in dtns. In: Mobile Adhoc and Sensor Systems (MASS), 2006 IEEE International Conference on (pp. 31–40).

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  1. The University of Melbourne/NICTA, 3.08, 111 Barry St., ICT Building, Carlton, VIC, 3053, Australia

    Saeed Shahbazi, Shanika Karunasekera & Aaron Harwood

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  1. Saeed Shahbazi
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Correspondence to Saeed Shahbazi.

Appendices

DTN definition

The performance of the traditional routing protocols for mobile ad hoc networks (MANETs) or mesh networks [24, 42, 43, 48] in terms of packet delivery rate is significantly decreased at a point when the network is getting sparse. To find that point, we ran an experiment shown in the Fig. 14. In Fig. 14 we show a number of well known MANET routing protocols (e.g. AODV [42], DSDV [43], DSR [24], and ADIAN [48]) compared to random gossiping in terms of the fraction of delivered packets versus average percentage of available contacts. To do this, we used NS2. We set a fixed number of mobile nodes, i.e., 20, while increasing the area within which they could move, thus increasing sparseness. The data traffic used in the simulation is CBR with a rate of 8 kbps. Mobile nodes move according to the RWP mobility model with a pause time of 0 s and maximum allowed speed of 3 m/s. The simulation time is 1,000 s and radio range of the nodes is 250 m. In the Random Gossip protocol, each node picks a connected node at random and forwards the packet. The maximum number of possible contacts between each pair of nodes can be calculated as follows:

$$ Max\,Contact\,No. = N_{node}\left(N_{node}-1\right)/2. $$

According to Fig. 14, the other protocols converge to the performance of the Random Gossip protocol when the total contacts are about 5% of all 190 possible contacts between each pair of nodes.

Fig. 14
figure 14

Packet delivery rate versus fraction of available contacts to all potential contacts between each pair of nodes

Full size image

GA placement details

In our GA placement approach, the genome is represented as a sequence of relay coordinates:

$$ \left[x_{1}, y_{1}, x_{2}, y_{2},\ldots, x_{n}, y_{n}\right] $$

where \(x_{i}, y_{i}\in\left[0 .. 1\right]\) (\(0\ldots 1\) is a normalized coordinate). A single population was used of 120 genomes, with each genome initialized by placing relays uniformly at random in the region. The number of elite genomes was set to 10 and the search was run for 100 iterations. We use Genetic Algorithm Solver Toolbox provided by Matlab for our simulation. Through some preliminary experiments we determined some GA parameters values that improved GA performance which is presented in Table 6. Other parameters are set as the default value in the latter toolbox. We did not do an exhaustive search over the entire parameter space. We leave this to future work. The mutation and crossover functions were customized for our problem. We hypothesized that the structure/topology of the geographic relationships between relays is important in terms of performance. In order to allow the GA to learn about spatial characteristics of relay placement, we identified regions of relays using a breadth first tree construction based on the Delaunay graph defined by the genome. Initially, we simply chose relays at random, rather than using a breath first tree approach and the resulting GA performance was comparably poor.

Table 6 GA parameter settings
Full size table

We use the Delaunay triangulation operator in Matlab to generate edges between spatially close nodes, creating a mesh. The choice of the Delaunay triangulation is arbitrary and unimportant; there are many different ways to generate these edges. We then select a node at random in the mesh and construct a tree using a breadth search search from that node, with a target number of nodes to be included in the tree.

Figure 15(a, b), show two genomes, each consisting of 50 relays. The Delaunay graph of the relays is shown using light lines. A random breadth first tree is constructed by choosing a relay at random and forming a breadth first tree that consists of the required number of relays. An example set of relays in such a tree is shown using solid dots for each genome.

Fig. 15
figure 15

Example genetic algorithm crossover and mutation. a Genome 1, b Genome 2, c Crossover of genome 1 and 2, d Mutation of genome 2

Full size image

The child shown in Fig. 15(c) is constructed from the selected trees in genome 1 and genome 2. In practice, for our crossover function, the number of trees and the number of relays in each tree selected from genome 1 is randomized, i.e., we choose a small number of random trees from genome 1. The resulting number of relays (and number of trees) selected from genome 2 is constrained so that the total number of relays in the child equals N relay . If genome 1 and 2 have identical selected relays (as in the case when they have common ancestors) then one of the identical relays is replaced with a point selected at random in the region.

As an example representing crossover operator, assume the following two genomes:

$$ \begin{aligned} g_{1}&=\left[x_{1,1}, y_{1,1}, x_{1,2}, y_{1,2}, ..., x_{1,n}, y_{1,n}\right],\\ g_{2}&=\left[x_{2,1}, y_{2,1}, x_{2,2}, y_{2,2}, ..., x_{2,n}, y_{2,n}\right]. \end{aligned} $$

The crossover function then takes a subset of points from g 1, and the remaining points from g 2. Therefore, the result of crossover operator would be a new genome as follows:

$$ g_{3}=g_{1}\times g_{2}=\left[x_{3,1}, y_{3,1}, x_{3,2}, y_{3,2}, \ldots, x_{3,n}, y_{3,n}\right] $$

Our mutation operation similarly selects a random breadth first tree (in practice consisting of up to 5% of the relays). For mutation, the selected relays are replaced with relays chosen at random in the region. Figure 15(d) is a mutation of genome 2. Note that a “hole" appears where the selected relays were, since the new relays are less likely to appear in the selected region (for a small number of selected relays and hence a small selected region).

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Shahbazi, S., Karunasekera, S. & Harwood, A. Improving performance in delay/disruption tolerant networks through passive relay points. Wireless Netw 18, 9–31 (2012). https://doi.org/10.1007/s11276-011-0384-1

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