Partially Ordered Knowledge Sharing and Fractionated Systems in the Context of other Models for Distributed Computing

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8373)


The latest sensor, actuator, and wireless communication technologies make it feasible to build systems that can operate in challenging environments, but we argue in this paper that the foundations needed to support the design of such systems are not well developed. Traditional models based on strong computing primitives, such as atomic transactions, should be replaced by weaker models such as the partially ordered knowledge sharing model, which we motivate in this paper and put into context of existing research. We also introduce a general probabilistic semantics for our model and the flavor of its specialization to characterize fractionated systems, an interesting class of systems with a potentially large number of redundantly operating components that can be programmed independently of the actual number that is deployed or operational at runtime.


Shared Memory Fractionate System Distribute Hash Table Tuple Space Distribute Shared Memory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Acosta-Elías, J., Luna-Rivera, J.M., Recio-Lara, M., Gutiérrez-Navarro, O., Pineda-Reyes, B.: Topology-sensitive epidemic algorithm for information spreading in large-scale systems. In: Guo, M., Yang, L.T., Di Martino, B., Zima, H.P., Dongarra, J., Tang, F. (eds.) ISPA 2006. LNCS, vol. 4330, pp. 439–450. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Agha, G.: Actors: a model of concurrent computation in distributed systems. MIT Press, Cambridge (1986)Google Scholar
  3. 3.
    Amir, Y.: Overtaken by events. In: Personnal Communication (April 2010)Google Scholar
  4. 4.
    Amir, Y., Danilov, C., Goose, S., Hedqvist, D., Terzis, A.: 1-800-overlays: using overlay networks to improve VoIP quality. In: NOSSDAV 2005: Proceedings of the International Workshop on Network and Operating Systems Support for Digital Audio and Video, pp. 51–56. ACM, New York (2005)Google Scholar
  5. 5.
    Androutsellis-Theotokis, S., Spinellis, D.: A survey of peer-to-peer content distribution technologies. ACM Comput. Surv. 36(4), 335–371 (2004)CrossRefGoogle Scholar
  6. 6.
    Angluin, D.: Local and global properties in networks of processors (extended abstract). In: STOC 1980: Proceedings of the Twelfth Annual ACM Symposium on Theory of Computing, pp. 82–93. ACM, New York (1980)CrossRefGoogle Scholar
  7. 7.
    Angluin, D., Aspnes, J., Fischer, M.J., Jiang, H.: Self-stabilizing population protocols. ACM Trans. Auton. Adapt. Syst. 3(4), 1–28 (2008)CrossRefGoogle Scholar
  8. 8.
    Arora, A., Gouda, M.: Closure and convergence: A foundation of fault-tolerant computing. IEEE Transactions on Software Engineering 19, 1015–1027 (1993)CrossRefGoogle Scholar
  9. 9.
    Arora, A., Gouda, M., Varghese, G.: Constraint satisfaction as a basis for designing nonmasking fault-tolerance. J. High Speed Netw. 5(3), 293–306 (1996)Google Scholar
  10. 10.
    Attiya, H., Welch, J.: Distributed Computing: Fundamentals, Simulations, and Advanced Topics. Wiley-Interscience (2004)Google Scholar
  11. 11.
    Aysal, T.C., Yildiz, M.E., Sarwate, A.D., Scaglione, A.: Broadcast gossip algorithms for consensus. Trans. Sig. Proc. 57(7), 2748–2761 (2009)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Babaoglu, O., Canright, G., Deutsch, A., Caro, G.D., Ducatelle, F., Gambardella, L., Ganguly, N., Jelasity, M., Montemanni, R.: Design patterns from biology for distributed computing. ACM Transactions on Autonomous and Adaptive Systems 1, 26–66 (2006)CrossRefGoogle Scholar
  13. 13.
    Bal, H.E., Tanenbaum, A.S.: Distributed programming with shared data. Comput. Lang. 16(2), 129–146 (1991)CrossRefGoogle Scholar
  14. 14.
    Balakrishnan, H., Kaashoek, M.F., Karger, D., Morris, R., Stoica, I.: Looking up data in p2p systems. Commun. ACM 46(2), 43–48 (2003)CrossRefGoogle Scholar
  15. 15.
    Bessler, S., Fischer, A., Kḧn, E., Mordinyi, R., Tomic, S.: Using tuple-spaces to manage the storage and dissemination of spatial-temporal content. Journal of Computer and System Sciences (2009)Google Scholar
  16. 16.
    Birman, K.: The promise, and limitations, of gossip protocols. SIGOPS Oper. Syst. Rev. 41(5), 8–13 (2007)CrossRefGoogle Scholar
  17. 17.
    Birman, K.P., Hayden, M., Ozkasap, O., Xiao, Z., Budiu, M., Minsky, Y.: Bimodal multicast. ACM Trans. Comput. Syst. 17(2), 41–88 (1999)CrossRefGoogle Scholar
  18. 18.
    Bisiani, R., Forin, A.: Multilanguage parallel programming of heterogeneous machines. IEEE Trans. Comput. 37(8), 930–945 (1988)CrossRefGoogle Scholar
  19. 19.
    Boldi, P., Vigna, S.: Self-stabilizing universal algorithms. In: Self–Stabilizing Systems (Proc. of the 3rd Workshop on Self–Stabilizing Systems), pp. 141–156. Carleton University Press (1997)Google Scholar
  20. 20.
    Bruni, R., Montanari, U.: Concurrent models for linda with transactions. Mathematical. Structures in Comp. Sci. 14(3), 421–468 (2004)MathSciNetzbMATHCrossRefGoogle Scholar
  21. 21.
    Busnel, Y., Bertier, M., Kermarrec, A.-M.: Bridging the Gap between Population and Gossip-based Protocols. Research Report RR-6720, INRIA (2008)Google Scholar
  22. 22.
    Cerf, V., Burleigh, S., Hooke, A., Torgerson, L., Durst, R., Scott, K., Travis, E., Weiss, H.: Status of this memo interplanetary internet (ipn): Architectural definition, Internet Draft (May 2001)Google Scholar
  23. 23.
    Chandy, K.M.: Parallel program design: a foundation. Addison-Wesley Longman Publishing Co. Inc. (1988)Google Scholar
  24. 24.
    Christensen, S., Hansen, N.D.: Coloured petri nets extended with place capacities, test arcs and inhibitor arcs. In: Ajmone Marsan, M. (ed.) ICATPN 1993. LNCS, vol. 691, pp. 186–205. Springer, Heidelberg (1993)CrossRefGoogle Scholar
  25. 25.
    Cole, R., Zajicek, O.: The APRAM: incorporating asynchrony into the PRAM model. In: SPAA 1989: Proceedings of the First Annual ACM Symposium on Parallel Algorithms and Architectures, pp. 169–178. ACM (1989)Google Scholar
  26. 26.
    Cole, R., Zajicek, O.: The expected advantage of asynchrony. J. Comput. Syst. Sci. 51(2), 286–300 (1995)MathSciNetzbMATHCrossRefGoogle Scholar
  27. 27.
    Corkill, D.D.: Design alternatives for parallel and distributed blackboard systems. In: Jagannathan, V., Dodhiawala, R., Baum, L.S. (eds.) Blackboard Architectures and Applications, pp. 99–136. Academic Press (1989)Google Scholar
  28. 28.
    Demers, A., Greene, D., Hauser, C., Irish, W., Larson, J., Shenker, S., Sturgis, H., Swinehart, D., Terry, D.: Epidemic algorithms for replicated database maintenance. In: PODC 1987: Proceedings of the Sixth Annual ACM Symposium on Principles of Distributed Computing, pp. 1–12. ACM, New York (1987)CrossRefGoogle Scholar
  29. 29.
    Devismes, S., Tixeuil, S., Yamashita, M.: Weak vs. self vs. probabilistic stabilization. In: ICDCS 2008: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems, pp. 681–688. IEEE Computer Society, Washington, DC (2008)Google Scholar
  30. 30.
    Dijkstra, E.W.: Self-stabilizing systems in spite of distributed control. Commun. ACM 17(11), 643–644 (1974)zbMATHCrossRefGoogle Scholar
  31. 31.
    Dolev, S.: Self-stabilization. MIT Press, Cambridge (2000)zbMATHGoogle Scholar
  32. 32.
    Dolev, S., Tzachar, N.: Empire of colonies: Self-stabilizing and self-organizing distributed algorithm. Theor. Comput. Sci. 410(6-7), 514–532 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  33. 33.
    Engelmore, R.S., Morgan, A. (eds.): Blackboard Systems. Addison-Wesley (1988)Google Scholar
  34. 34.
    Ensor, J.R., Gabbe, J.D.: Transactional blackboards. In: IJCAI 1985: Proceedings of the 9th International Joint Conference on Artificial Intelligence, pp. 340–344. Morgan Kaufmann Publishers Inc. (1985)Google Scholar
  35. 35.
    Eskicioglu, M.R.: A comprehensive bibliography of distributed shared memory. SIGOPS Oper. Syst. Rev. 30(1), 71–96 (1996)CrossRefGoogle Scholar
  36. 36.
    R. K et al.: The spindle disruption tolerant networking system. In: Proceedings of IEEE Military Communications Conference (2007)Google Scholar
  37. 37.
    Eugster, P., Felber, P., Le Fessant, F.: The “art” of programming gossip-based systems. SIGOPS Oper. Syst. Rev. 41(5), 37–42 (2007)CrossRefGoogle Scholar
  38. 38.
    Eugster, P.T., Guerraoui, R., Kermarrec, A.M., Massouli, L.: From epidemics to distributed computing. IEEE Computer 37, 60–67 (2004)CrossRefGoogle Scholar
  39. 39.
    Fall, K.: A delay-tolerant network architecture for challenged internets. In: SIGCOMM 2003: Proceedings of the 2003 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, pp. 27–34. ACM (2003)Google Scholar
  40. 40.
    Farrell, S., Cahill, V.: Delay- and Disruption-Tolerant Networking. Artech House, Inc., Norwood (2006)Google Scholar
  41. 41.
    Fernandess, Y., Fernández, A., Monod, M.: A generic theoretical framework for modeling gossip-based algorithms. SIGOPS Oper. Syst. Rev. 41(5), 19–27 (2007)CrossRefGoogle Scholar
  42. 42.
    Fich, F., Ruppert, E.: Hundreds of impossibility results for distributed computing. Distrib. Comput. 16(2-3), 121–163 (2003)CrossRefGoogle Scholar
  43. 43.
    Fischer, M.J., Lynch, N.A., Paterson, M.S.: Impossibility of distributed consensus with one faulty process. J. ACM 32(2), 374–382 (1985)MathSciNetzbMATHCrossRefGoogle Scholar
  44. 44.
    Frommer, A., Szyld, D.B.: On asynchronous iterations. J. Comput. Appl. Math. 123(1-2), 201–216 (2000)MathSciNetzbMATHCrossRefGoogle Scholar
  45. 45.
    Gibbons, P.B.: A more practical PRAM model. In: SPAA 1989: Proceedings of the First Annual ACM Symposium on Parallel Algorithms and Architectures, pp. 158–168. ACM (1989)Google Scholar
  46. 46.
    Girard, J.-Y.: Linear logic. Theor. Comput. Sci. 50, 1–102 (1987)MathSciNetzbMATHCrossRefGoogle Scholar
  47. 47.
    Golding, R.A., Long, D.D.E.: The performance of weak-consistency replication protocols. Technical Report UCSC-CRL-92-30, Santa Cruz, CA, USA (1992)Google Scholar
  48. 48.
    Gouda, M.G., Multari, N.J.: Stabilizing communication protocols. IEEE Trans. Comput. 40(4), 448–458 (1991)CrossRefGoogle Scholar
  49. 49.
    Guerraoui, R., Olivera, R., Schiper, A.: Stubborn communication channels. Technical report, LSE, D’epartement d’Informatique, Ecole Polytechnique F’ed’erale de (1996)Google Scholar
  50. 50.
    Gupta, I., Renesse, R.V., Birman, K.P.: A probabilistically correct leader election protocol for large groups. In: Herlihy, M.P. (ed.) DISC 2000. LNCS, vol. 1914, pp. 89–103. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  51. 51.
    Halpern, J.Y., Moses, Y.: Knowledge and common knowledge in a distributed environment. Journal of the ACM 37, 549–587 (1984)MathSciNetzbMATHCrossRefGoogle Scholar
  52. 52.
    Heer, T., Gotz, S., Rieche, S., Wehrle, K.: Adapting distributed hash tables for mobile ad hoc networks. In: PERCOMW 2006: Proceedings of the 4th Annual IEEE International Conference on Pervasive Computing and Communications Workshops, p. 173. IEEE Computer Society (2006)Google Scholar
  53. 53.
    Herlihy, M.: Impossibility results for asynchronous PRAM (extended abstract). In: SPAA 1991: Proceedings of the Third Annual ACM Symposium on Parallel Algorithms and Architectures, pp. 327–336. ACM, New York (1991)CrossRefGoogle Scholar
  54. 54.
    Herman, T.: Models of self-stabilization and sensor networks. In: Das, S.R., Das, S.K. (eds.) IWDC 2003. LNCS, vol. 2918, pp. 205–214. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  55. 55.
    Howell, R.R., Nesterenko, M., Mizuno, M., Mizuno, M.: Finite-state self-stabilizing protocols in message-passing systems. In: Proceedings of the Fourth Workshop on Self-Stabilizing Systems, pp. 62–69 (1999)Google Scholar
  56. 56.
    Itkis, G., Levin, L.: Fast and lean self-stabilizing asynchronous protocols. In: SFCS 1994: Proceedings of the 35th Annual Symposium on Foundations of Computer Science, pp. 226–239. IEEE Computer Society, Washington, DC (1994)Google Scholar
  57. 57.
    Jacobson, V., Smetters, D.K., Thornton, J.D., Plass, M.F., Briggs, N.H., Braynard, R.L.: Networking named content. In: CoNEXT 2009: Proceedings of the 5th International Conference on Emerging Networking Experiments and Technologies, pp. 1–12. ACM (2009)Google Scholar
  58. 58.
    Jelasity, M., Voulgaris, S., Guerraoui, R., Kermarrec, A.-M., van Steen, M.: Gossip-based peer sampling. ACM Trans. Comput. Syst. 25(3), 8 (2007)CrossRefGoogle Scholar
  59. 59.
    Kang, W., Kapitanova, K., Son, S.H.: RDDS: A real-time data distribution service for cyber-physical systems. IEEE Trans. Industrial Informatics 8(2), 393–405 (2012)CrossRefGoogle Scholar
  60. 60.
    Karp, R., Schindelhauer, C., Shenker, S., Vocking, B.: Randomized rumor spreading. In: FOCS 2000: Proceedings of the 41st Annual Symposium on Foundations of Computer Science, p. 565. IEEE Computer Society (2000)Google Scholar
  61. 61.
    Katz, S., Perry, K.: Self-stabilizing extensions for message-passing systems. In: PODC 1990: Proceedings of the Ninth Annual ACM Symposium on Principles of Distributed Computing, pp. 91–101. ACM (1990)Google Scholar
  62. 62.
    Kempe, D., Dobra, A., Gehrke, J.: Gossip-based computation of aggregate information. In: FOCS 2003: Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science, p. 482. IEEE Computer Society, Washington, DC (2003)Google Scholar
  63. 63.
    Kempe, D., Kleinberg, J., Demers, A.: Spatial gossip and resource location protocols. In: STOC 2001: Proceedings of the Thirty-Third Annual ACM Symposium on Theory of Computing, pp. 163–172. ACM (2001)Google Scholar
  64. 64.
    Kermarrec, A.-M., van Steen, M.: Gossiping in distributed systems. SIGOPS Oper. Syst. Rev. 41(5), 2–7 (2007)CrossRefGoogle Scholar
  65. 65.
    Kim, J., Kim, M., Stehr, M.-O., Oh, H., Ha, S.: A parallel and distributed meta-heuristic framework based on partially ordered knowledge sharing. ELSEVIER Journal of Parallel and Distributed Computing (JPDC) 72(4), 564–578 (2012)CrossRefGoogle Scholar
  66. 66.
    Kim, M., Stehr, M.-O., Talcott, C.: A distributed logic for networked cyber-physical systems. ELSEVIER Journal of Science of Computer Programming (2013),
  67. 67.
    Klenke, A.: Probability Theory: A Comprehensive Course. Springer, London (2008)zbMATHCrossRefGoogle Scholar
  68. 68.
    Kulkarni, S.S., Arumugam, M.: Transformations for write-all-with-collision model. Comput. Commun. 29(2), 183–199 (2006)zbMATHCrossRefGoogle Scholar
  69. 69.
    Lahiri, B., Tirthapura, S.: Computing frequent elements using gossip. In: Shvartsman, A.A., Felber, P. (eds.) SIROCCO 2008. LNCS, vol. 5058, pp. 119–130. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  70. 70.
    Lesser, V.R., Corkill, D.D.: Functionally accurate, cooperative distributed systems. In: Distributed Artificial Intelligence, pp. 295–310. Morgan Kaufmann Publishers Inc. (1988)Google Scholar
  71. 71.
    Lynch, N.A., Malkhi, D., Ratajczak, D.: Atomic data access in distributed hash tables. In: Druschel, P., Kaashoek, M.F., Rowstron, A. (eds.) IPTPS 2002. LNCS, vol. 2429, pp. 295–305. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  72. 72.
    Meseguer, J.: Conditioned rewriting logic as a united model of concurrency. Theor. Comput. Sci. 96(1), 73–155 (1992)zbMATHCrossRefGoogle Scholar
  73. 73.
    auf der Heide, F.M., Scheideler, C., Stemann, V.: Exploiting storage redundancy to speed up randomized shared memory simulations. Theor. Comput. Sci. 162(2), 245–281 (1996)MathSciNetzbMATHCrossRefGoogle Scholar
  74. 74.
    Misra, J.: A discipline of multiprogramming: programming theory for distributed applications. Springer-Verlag New York, Inc., Secaucus (2001)zbMATHCrossRefGoogle Scholar
  75. 75.
    Moser, L.E., Amir, Y., Melliar-Smith, P.M., Agarwal, D.A.: Extended virtual synchrony. In: Proceedings of the IEEE 14th International Conference on Distributed Computing Systems, pp. 56–65. IEEE Computer Society Press (1994)Google Scholar
  76. 76.
    Mosk-Aoyama, D., Shah, D.: Computing separable functions via gossip. In: PODC 2006: Proceedings of the Twenty-Fifth Annual ACM Symposium on Principles of Distributed Computing, pp. 113–122. ACM, New York (2006)CrossRefGoogle Scholar
  77. 77.
    Murphy, A.L., Picco, G.P., Roman, G.-C.: Lime: A coordination model and middleware supporting mobility of hosts and agents. ACM Trans. Softw. Eng. Methodol. 15(3), 279–328 (2006)CrossRefGoogle Scholar
  78. 78.
    De Nicola, R., Ferrari, G., Loreti, M., Pugliese, R.: A language-based approach to autonomic computing. In: Beckert, B., Damiani, F., de Boer, F.S., Bonsangue, M.M. (eds.) FMCO 2011. LNCS, vol. 7542, pp. 25–48. Springer, Heidelberg (2012)Google Scholar
  79. 79.
    Panangaden, P.: Knowledge and information in probabilistic systems. In: van Breugel, F., Chechik, M. (eds.) CONCUR 2008. LNCS, vol. 5201, p. 4. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  80. 80.
    Pereira, J., Oliveira, R., Rodrigues, L., Kermarrec, A.-M.: Probabilistic semantically reliable multicast. In: NCA 2001: Proceedings of the IEEE International Symposium on Network Computing and Applications, p. 100. IEEE Computer Society, Washington, DC (2001)CrossRefGoogle Scholar
  81. 81.
    Pereira, J., Rodrigues, L., Oliveira, R.: Semantically reliable multicast: Definition, implementation, and performance evaluation. IEEE Trans. Comput. 52(2), 150–165 (2003)CrossRefGoogle Scholar
  82. 82.
    Petersen, K., Spreitzer, M.J., Terry, D.B., Theimer, M.M., Demers, A.J.: Flexible update propagation for weakly consistent replication. In: SOSP 1997: Proceedings of the Sixteenth ACM Symposium on Operating Systems Principles, pp. 288–301. ACM (1997)Google Scholar
  83. 83.
    Rao, J.R.: Extensions of the Unity Methodology: Compositionality, Fairness and Probability in Parallelism. Springer-Verlag New York, Inc., Secaucus (1995)zbMATHCrossRefGoogle Scholar
  84. 84.
    Ratnasamy, S., Stoica, I., Shenker, S.: Routing algorithms for dhts: Some open questions. In: Druschel, P., Kaashoek, M.F., Rowstron, A. (eds.) IPTPS 2002. LNCS, vol. 2429, pp. 45–52. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  85. 85.
    Reisig, W.: Elements of distributed algorithms: modeling and analysis with Petri nets. Springer-Verlag New York, Inc. (1998)Google Scholar
  86. 86.
    Schneider, M.: Self-stabilization. ACM Comput. Surv. 25(1), 45–67 (1993)CrossRefGoogle Scholar
  87. 87.
    Stehr, M.-O., Kim, M., Talcott, C.: Toward distributed declarative control of networked cyber-physical systems. In: Proc. of the 7th Intl. Conf. on Ubiquitous Intelligence and Computing (to appear). Full version available
  88. 88.
    Stehr, M.-O., Talcott, C.: Planning and learning algorithms for routing in disruption-tolerant networks. In: Proceedings of IEEE Military Communications Conference (2008)Google Scholar
  89. 89.
    Temkow, B., Bosneag, A.-M., Li, X., Brockmeyer, M.: PaxonDHT: Achieving consensus in distributed hash tables. In: SAINT 2006: Proceedings of the International Symposium on Applications on Internet, pp. 236–244. IEEE Computer Society, Washington, DC (2006)Google Scholar
  90. 90.
    Turau, V., Weyer, C.: Fault tolerance in wireless sensor networks through self-stabilisation. Int. J. Commun. Netw. Distrib. Syst. 2(1), 78–98 (2009)CrossRefGoogle Scholar
  91. 91.
    Van Renesse, R., Birman, K.P., Vogels, W.: Astrolabe: A robust and scalable technology for distributed system monitoring, management, and data mining. ACM Trans. Comput. Syst. 21(2), 164–206 (2003)CrossRefGoogle Scholar
  92. 92.
    van Renesse, R., Dumitriu, D., Gough, V., Thomas, C.: Efficient reconciliation and flow control for anti-entropy protocols. In: LADIS 2008: Proceedings of the 2nd Workshop on Large-Scale Distributed Systems and Middleware, pp. 1–7. ACM (2008)Google Scholar
  93. 93.
    Varghese, G.: Self-stabilization by counter flushing. In: PODC 1994: Proceedings of the Thirteenth Annual ACM Symposium on Principles of Distributed Computing, pp. 244–253. ACM (1994)Google Scholar
  94. 94.
    Vogler, W.: Partial order semantics and read arcs. Theor. Comput. Sci. 286(1), 33–63 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  95. 95.
    Völzer, H.: Fairneß, Randomisierung und Konspiration in verteilten Algorithmen. PhD thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II (2000)Google Scholar
  96. 96.
    Wood, S., Mathewson, J., Joy, J., Stehr, M.-O., Kim, M., Gehani, A., Gerla, M., Sadjadpour, H., Garcia-Luna-Aceves, J.: ICEMAN: A system for efficient, robust and secure situational awareness at the network edge. In: Proceedings of IEEE Military Communications Conference (2013)Google Scholar
  97. 97.
    Zhang, Z., Zhang, Q.: Delay-/disruption tolerant mobile ad hoc networks: latest developments: Research articles. Wirel. Commun. Mob. Comput. 7(10), 1219–1232 (2007)CrossRefGoogle Scholar
  98. 98.
    Zhao, W., Ammar, M.H.: Message ferrying: Proactive routing in highly-partitioned wireless ad hoc networks. In: FTDCS 2003: Proceedings of the The Ninth IEEE Workshop on Future Trends of Distributed Computing Systems, p. 308. IEEE Computer Society, Washington, DC (2003)CrossRefGoogle Scholar
  99. 99.
    Zhao, W., Chen, Y., Ammar, M., Corner, M., Levine, B., Zegura, E.: Capacity enhancement using throwboxes in dtns. In: Proc. IEEE Intl Conf on Mobile Ad hoc and Sensor Systems (MASS), pp. 31–40 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.SRI InternationalUSA

Personalised recommendations