A Lattice-Theoretic Approach to Runtime Property Detection for Pervasive Context

  • Tingting Hua
  • Yu Huang
  • Jiannong Cao
  • Xianping Tao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6406)

Abstract

Runtime detection of contextual properties is one of the primary approaches to enabling context-awareness. Existing property detection schemes implicitly assume that contexts under detection belong to the same snapshot of time. However, this assumption does not necessarily hold in the asynchronous pervasive computing environments. To cope with the asynchrony, we first model environment behavior based on logical time. One key notion of our model is that all meaningful observations of the environment have the lattice structure. Then we propose the LAT algorithm, which maintains the lattice of meaningful observations at runtime. We also propose the LATPD algorithm, which achieves detection of contextual properties at runtime. We implement algorithms over the open-source context-aware middleware MIPA, and simulations are conducted. The evaluation results show that LAT and LATPD support effective detection of contextual properties in asynchronous environments.

Keywords

Lattice Property detection Asynchronous environment  Context-awareness 

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References

  1. 1.
    MIPA - Middleware Infrastructure for Predicate detection in Asynchronous environments, http://mipa.googlecode.com/
  2. 2.
    Babaoğlu, O., Fromentin, E., Raynal, M.: A unified framework for the specification and run-time detection of dynamic properties in distributed computations. J. Syst. Softw. 33(3), 287–298 (1996)CrossRefGoogle Scholar
  3. 3.
    Babaoğlu, O., Raynal, M.: Specification and verification of dynamic properties in distributed computations. J. Parallel Distrib. Comput. 28(2), 173–185 (1995)CrossRefMATHGoogle Scholar
  4. 4.
    Bu, Y., Gu, T., Tao, X., Li, J., Chen, S., Lu, J.: Managing quality of context in pervasive computing. In: Proc. International Conference on Quality Software (QSIC 2006), Beijing, China, pp. 193–200 (2006)Google Scholar
  5. 5.
    Charron-Bost, B., Delporte-Gallet, C., Fauconnier, H.: Local and temporal predicates in distributed systems. ACM Trans. Program. Lang. Syst. 17(1), 157–179 (1995)CrossRefGoogle Scholar
  6. 6.
    Cooper, R., Marzullo, K.: Consistent detection of global predicates. In: Proc. ACM/ONR Workshop on Parallel and Distributed Debugging, New York, NY, USA, pp. 167–174 (1991)Google Scholar
  7. 7.
    Dey, A.: Providing architectural support for building context-aware applications. Ph.D. Thesis, Georgia Institute of Technology (2000)Google Scholar
  8. 8.
    Fidge, C.J.: Partial orders for parallel debugging. In: Proc. ACM SIGPLAN and SIGOPS Workshop on Parallel and Distributed Debugging, Madison, Wisconsin, US, pp. 183–194 (1988)Google Scholar
  9. 9.
    Garg, V.K., Waldecker, B.: Detection of strong unstable predicates in distributed programs. IEEE Transactions on Parallel and Distributed Systems 7, 1323–1333 (1996)CrossRefGoogle Scholar
  10. 10.
    Garg, V., Waldecker, B.: Detection of weak unstable predicates in distributed programs. IEEE Transactions on Parallel and Distributed Systems 5, 299–307 (1994)CrossRefGoogle Scholar
  11. 11.
    Garlan, D., Siewiorek, D., Smailagic, A., Steenkiste, P.: Project aura: Toward distraction-free pervasive computing. IEEE Pervasive Computing 1(2), 22–31 (2002)CrossRefGoogle Scholar
  12. 12.
    Henricksen, K., Indulska, J.: A software engineering framework for context-aware pervasive computing. In: Proc. IEEE International Conference on Pervasive Computing and Communications (PERCOM 2004), Orlando, Florida, USA, pp. 77–86 (2004)Google Scholar
  13. 13.
    Huang, Y., Ma, X., Tao, X., Cao, J., Lu, J.: A probabilistic approach to consistency checking for pervasive context. In: Proc. IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (EUC 2008), Shanghai, China, pp. 387–393 (2008)Google Scholar
  14. 14.
    Huang, Y., Cao, J., Jin, B., Tao, X., Lu, J., Feng, Y.: Flexible cache consistency maintenance over wireless ad hoc networks. IEEE Trans. Parallel Distrib. Syst. 21(8), 1150–1161 (2010)CrossRefGoogle Scholar
  15. 15.
    Huang, Y., Ma, X., Cao, J., Tao, X., Lu, J.: Concurrent event detection for asynchronous consistency checking of pervasive context. In: Proc. IEEE International Conference on Pervasive Computing and Communications (PERCOM 2009), Galveston, Texas, USA (2009)Google Scholar
  16. 16.
    Huang, Y., Yu, J., Cao, J., Ma, X., Tao, X., Lu, J.: Checking behavioral consistency constraints for pervasive context in asynchronous environments. In: Technical Report, Institute of Computer Software. Nanjing University (2009), http://arxiv.org/abs/0911.0136
  17. 17.
    Jegou, R., Medina, R., et al.: Linear space algorithm for on-line detection of global predicates. In: Proc. International Workshop on Structures in Concurrency Theory (STRICT 1995), pp. 175–189. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  18. 18.
    Kaveti, L., Pulluri, S., Singh, G.: Event ordering in pervasive sensor networks. In: Proc. IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOMW 2009), Galveston, US, pp. 604–609 (2009)Google Scholar
  19. 19.
    Lamport, L.: Time, clocks, and the ordering of events in a distributed system. ACM Commun. 21(7), 558–565 (1978)CrossRefMATHGoogle Scholar
  20. 20.
    Lee, E.A.: Cyber-physical systems - are computing foundations adequate? In: NSF Workshop on Cyber-physical Systems: Research motivation, Techniques and Roadmap, Position Paper, Austin, Texas, USA (2006)Google Scholar
  21. 21.
    Mattern, F.: Virtual time and global states of distributed systems. In: Proc. International Workshop on Parallel and Distributed Algorithms, Holland, pp. 215–226 (1989)Google Scholar
  22. 22.
    Roman, M., Hess, C., Cerqueira, R., Ranganathan, A., Campbell, R.H., Nahrstedt, K.: A middleware infrastructure for active spaces. IEEE Pervasive Computing 1(4), 74–83 (2002)CrossRefGoogle Scholar
  23. 23.
    Sama, M., Rosenblum, D.S., Wang, Z., Elbaum, S.: Model-based fault detection in context-aware adaptive applications. In: Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of Software Engineering (SIGSOFT 2008/FSE-16), pp. 261–271. ACM, New York (2008)CrossRefGoogle Scholar
  24. 24.
    Xu, C., Cheung, S.C.: Inconsistency detection and resolution for context-aware middleware support. In: Proc. ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE 2005), Lisbon, Portugal, pp. 336–345 (2005)Google Scholar
  25. 25.
    Xu, C., Cheung, S.C., Chan, W.K., Ye, C.: Partial constraint checking for context consistency in pervasive computing. ACM Trans. Softw. Eng. Methodol. 19(3), 1–61 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tingting Hua
    • 1
    • 2
  • Yu Huang
    • 1
    • 2
  • Jiannong Cao
    • 3
  • Xianping Tao
    • 1
    • 2
  1. 1.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina
  2. 2.Department of Computer Science and TechnologyNanjing UniversityNanjingChina
  3. 3.Internet and Mobile Computing Lab, Department of ComputingHong Kong Polytechnic UniversityHong KongChina

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